fThCSSKJrJrJr SSKJrJrJrJr SSK J r J r SSK J r SSKJrJr "SS\S S 9r"S S \S S 9rS r"SS\5rS/rg))ListOptionalUnion) ImagesKwargsProcessingKwargsProcessorMixinUnpack)PreTokenizedInput TextInput) BatchFeature) ImageInputmake_flat_list_of_imagesc6\rSrSr%\\\S'\\\S'Srg)Llama4ImagesKwargs" max_patchesresize_to_max_canvasN) __name__ __module__ __qualname____firstlineno__rint__annotations__bool__static_attributes__rd/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/llama4/processing_llama4.pyrr"s#"4.(rrF)totalc,\rSrSr%\\S'SSS00rSrg)Llama4ProcessorKwargs' images_kwargs text_kwargs padding_sideleftrN)rrrrrr _defaultsrrrrr"r"'s%% F Irr"a>{{- bos_token }} {%- if custom_tools is defined %} {%- set tools = custom_tools %} {%- endif %} {%- if not tools_in_user_message is defined %} {%- set tools_in_user_message = true %} {%- endif %} {%- if not date_string is defined %} {%- if strftime_now is defined %} {%- set date_string = strftime_now("%d %b %Y") %} {%- else %} {%- set date_string = "26 Jul 2024" %} {%- endif %} {%- endif %} {%- if not tools is defined %} {%- set tools = none %} {%- endif %} {#- This block extracts the system message, so we can slot it into the right place. #} {%- if messages[0]['role'] == 'system' %} {%- if messages[0]['content'] is string %} {%- set system_message = messages[0]['content']|trim %} {%- else %} {#- FIXME: The processor requires an array, always. #} {%- set system_message = messages[0]['content'][0]['text']|trim %} {%- endif %} {%- set messages = messages[1:] %} {%- set user_supplied_system_message = true %} {%- else %} {%- set system_message = "" %} {%- set user_supplied_system_message = false %} {%- endif %} {#- System message if the user supplied one #} {%- if user_supplied_system_message %} {{- "<|header_start|>system<|header_end|> " }} {%- if tools is not none %} {{- "Environment: ipython " }} {%- endif %} {%- if tools is not none and not tools_in_user_message %} {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} {{- "Do not use variables. " }} {%- for t in tools %} {{- t | tojson(indent=4) }} {{- " " }} {%- endfor %} {%- endif %} {{- system_message }} {{- "<|eot|>" }} {%- endif %} {#- Custom tools are passed in a user message with some extra guidance #} {%- if tools_in_user_message and not tools is none %} {#- Extract the first user message so we can plug it in here #} {%- if messages | length != 0 %} {%- set first_user_message = messages[0]['content']|trim %} {%- set messages = messages[1:] %} {%- else %} {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} {%- endif %} {{- '<|header_start|>user<|header_end|> ' -}} {{- "Given the following functions, please respond with a JSON for a function call " }} {{- "with its proper arguments that best answers the given prompt. " }} {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} {{- "Do not use variables. " }} {%- for t in tools %} {{- t | tojson(indent=4) }} {{- " " }} {%- endfor %} {{- first_user_message + "<|eot|>"}} {%- endif %} {%- for message in messages %} {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} {{- '<|header_start|>' + message['role'] + '<|header_end|> ' }} {%- if message['content'] is string %} {{- message['content'] }} {%- else %} {%- for content in message['content'] %} {%- if content['type'] == 'image' %} {{- '<|image|>' }} {%- elif content['type'] == 'text' %} {{- content['text'] }} {%- endif %} {%- endfor %} {%- endif %} {{- "<|eot|>" }} {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %} {{- '<|header_start|>assistant<|header_end|> ' -}} {{- '<|python_start|>' }} {%- if message['content'] is string %} {{- message['content'] }} {%- else %} {%- for content in message['content'] %} {%- if content['type'] == 'image' %} {{- '<|image|>' }} {%- elif content['type'] == 'text' %} {{- content['text'] }} {%- endif %} {%- endfor %} {%- endif %} {{- '<|python_end|>' }} {%- for tool_call in message.tool_calls %} {{- '{"name": "' + tool_call.function.name + '", ' }} {{- '"parameters": ' }} {{- tool_call.function.arguments | tojson }} {{- "}" }} {%- endfor %} {{- "<|eot|>" }} {%- elif message.role == "tool" or message.role == "ipython" %} {{- "<|header_start|>ipython<|header_end|> " }} {%- if message.content is mapping or message.content is iterable %} {{- message.content | tojson }} {%- else %} {{- message.content }} {%- endif %} {{- "<|eot|>" }} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<|header_start|>assistant<|header_end|> ' }} {%- endif %} c ^\rSrSrSrSS/r/SQrSrSrSSS S S S S S SSS\ 4 S\ S\ 4U4Sjjjr Sr SS\\S\\\\\\\\4S\\S\4SjjrSrSr\S5rSrU=r$)Llama4Processor3a Constructs a Llama4 processor which wraps a [`AutoImageProcessor`] and [`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and tokenizer functionalities. See the [`~Llama4Processor.__call__`] and [`~Llama4Processor.decode`] for more information. Args: image_processor ([`AutoImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. patch_size (`int`, *optional*, defaults to 28): The size of image patches for tokenization. img_size (`int`, *optional*, defaults to 364): The size of the image to be tokenized. This should correspond to the size given to the image processor. image_token (`str`, *optional*, defaults to `"<|image|>"`): The token to be used to represent an image in the text. downsample_factor (`int`, *optional*, defaults to 1): The factor by which to scale the patch size. start_of_img_token (`str`, *optional*, defaults to `"<|START_OF_IMG|>"`): The token to be used to represent the start of an image in the text. end_of_img_token (`str`, *optional*, defaults to `"<|END_OF_IMG|>"`): The token to be used to represent the end of an image in the text. img_patch_token (`str`, *optional*, defaults to `"<|IMG_PATCH|>"`): The token to be used to represent an image patch in the text. img_line_break_token (`str`, *optional*, defaults to `"<|IMG_LINE_BREAK|>"`): The token to be used to represent a line break in the text. tile_token (`str`, *optional*, defaults to `"TILE"`): The token to be used to represent an image patch in the text. tile_global_token (`str`, *optional*, defaults to `"TILE_GLOBAL"`): The token to be used to represent the cover image in the text. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_processor tokenizer) chat_template image_token patch_sizeimg_sizedownsample_factorstart_of_img_tokenend_of_img_tokenimg_patch_tokenimg_line_break_token tile_tokentile_global_tokenAutoImageProcessor AutoTokenizerNg? <|image|><|image_start|> <|image_end|> <|patch|><|tile_x_separator|><|tile_y_separator|>r0pixel_shuffle_ratioc  >[TU]XU S9 [[SUS-- 55UlX0lXPlX`lURUR5Ul Xpl Xl Xl Xl Xlg)N)r.g?)super__init__rrounddownsample_ratior0fake_image_tokenr/convert_tokens_to_idsimage_token_idr3r4r5r7r8)selfr,r-r0rBrIr/start_of_image_tokenend_of_image_token patch_tokentile_x_separator_tokentile_y_separator_tokenr.kwargs __class__s rrFLlama4Processor.__init__fs{ =Q #E#1Da1G*H$I J$ 0&'==d>N>NO"6 2*0!7rcSnUupEXE-S:a@[U5H1n[U5HnUSU-- nXuS- :dMUS- nM US- nM3 US- nUSU-- nUS- nU$)z Create a structured string representation of image tokens Args: num_patches: Number of patches in the image Returns: String with appropriate image tokens r=r?r@rAr<r>)range)rL aspect_rationum_patches_per_chunk img_stringratio_hratio_wyyxxs r_prompt_split_image#Llama4Processor._prompt_split_images' '  q Gn.B+0E"EEJaK'"&<< ) 44 % k! k$999 o% rimagestextrRreturnc ^Uc [S5eTR"[4STRR0UD6n[ U[ [45(dU/n0nUGb[U5nTR"SSU0USD6nUSSRSSup[UTR-U TR--TR-5n URS 5n [U4S jU55n U [!U5:wa[S U S [!U5S 35eSn /nUHnUR#TR$5nUS:XaUR'U5 M7UR)TR$5n/n[+U5HIunnUR'U5 UU:dMTR-XU 5nU S- n UR'U5 MK UR'SR/U55 M U [!U5:wa [S5eUnUSRSS5nTR"U40USD6nTR1UUS/S9 [30UEUEUS9$)a Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text. To prepare the vision inputs, this method forwards the `images` and `kwargs` arguments to Llama4ImageProcessor's [`~Llama4ImageProcessor.__call__`] if `images` is not `None`. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. NzYou have to specify text.tokenizer_init_kwargsrar$ pixel_valuesr aspect_ratiosc3X># UHoRTR5v M! g7fN)countrI).0promptrLs r +Llama4Processor.__call__..s#$\W[V\\$2G2G%H%HW[s'*zFound z) placeholders across the batch, but have z flattened images.rVzONumber of image placeholders in the prompt does not match the number of images.r%return_tensorsimage) modalities)data tensor_typer) ValueError _merge_kwargsr"r- init_kwargs isinstancelisttuplerr,shaperr0rHpopsumlenrkrIappendsplit enumerater_join_check_special_mm_tokensr )rLrarbaudiovideosrR output_kwargs image_inputs image_height image_widthrYrhtotal_placeholders image_indexprocessed_textrmplaceholder_count prompt_splits new_promptlocal_image_index split_parttokens_for_this_imagerq text_inputss` r__call__Llama4Processor.__call__sL <89 9** ! "&.."<"<  $u ..6D  -f5F//`v`A_`L(4^(DQ(G(M(Mbc(R %L$'0[DOO5STX\XmXmm% !),,_=M!$$\W[$\!\ !S[0 /01 #F },>@ KN$*LL1F1F$G!$)"))&1 & T-B-B C  5>}5M1%z%%j1(+<<040H0H)68M1-$q( "))*?@6N%%bggj&9:!$c&k) !rss!D&}599:JDQnnTJ]=-IJ  %%dKWI%N!@K!@rsf")( N2 )U) ,E]P aOnaOH  r