






import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 							__a							(  __A ,	__A						):





		'''simple docstring'''



		@register_to_config
		def __init__(						self ,    *,
		    UpperCamelCase__ = 4 ,    UpperCamelCase__ = 768 ,    UpperCamelCase__ ,    UpperCamelCase__ ,    ):
						super().__init__()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   nn.Parameter(torch.zeros(UpperCamelCase__       )       )

						# parameters for additional clip time embeddings
						SCREAMING_SNAKE_CASE_   :	Any													=   nn.Linear(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   nn.Linear(UpperCamelCase__ ,    UpperCamelCase__       )

						# parameters for encoder hidden states
						SCREAMING_SNAKE_CASE_   :	str													=   clip_extra_context_tokens
						SCREAMING_SNAKE_CASE_   :	str													=   nn.Linear(
						    UpperCamelCase__ ,    self.clip_extra_context_tokens * cross_attention_dim       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   nn.Linear(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   nn.LayerNorm(UpperCamelCase__       )






		def   __snake_case			(						self ,    *, UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if do_classifier_free_guidance:
										# Add the classifier free guidance embeddings to the image embeddings
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_embeddings.shape[0]
										SCREAMING_SNAKE_CASE_   :	List[str]													=   self.learned_classifier_free_guidance_embeddings.unsqueeze(0       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   classifier_free_guidance_embeddings.expand(
										    UpperCamelCase__ ,    -1       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,    dim=0       )

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.embedding_proj(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.clip_extra_context_tokens_proj(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   clip_extra_context_tokens.reshape(UpperCamelCase__ ,    -1 ,    self.clip_extra_context_tokens       )
						SCREAMING_SNAKE_CASE_   :	Any													=   clip_extra_context_tokens.permute(0 ,    2 ,    1       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.encoder_hidden_states_proj(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.text_encoder_hidden_states_norm(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,    dim=1       )

						return text_encoder_hidden_states, additive_clip_time_embeddings







import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A        =			'src/transformers'
A        =			'docs/source/en/tasks'



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				with open(lowerCAmelCase__    ,    'r'    ,    encoding='utf-8'    ,    newline='\n'      ) as f:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   f.readlines()
				# Find the start prompt.
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   0
				while not lines[start_index].startswith(lowerCAmelCase__      ):
								start_index += 1
				start_index += 1

				SCREAMING_SNAKE_CASE_   :	int													=   start_index
				while not lines[end_index].startswith(lowerCAmelCase__      ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]      ) <= 1:
								start_index += 1
				while len(lines[end_index]      ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A        =			direct_transformers_import(TRANSFORMERS_PATH)

A        =			{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A        =			{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}



def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   TASK_GUIDE_TO_MODELS[task_guide]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase__    ,    set()      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]      ) + "\n"



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )    ,    start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'    ,    end_prompt='<!--End of the generated tip-->'    ,    )

				SCREAMING_SNAKE_CASE_   :	Dict													=   get_model_list_for_task(lowerCAmelCase__      )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )    ,    'w'    ,    encoding='utf-8'    ,    newline='\n'      ) as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:]      )
								else:
												raise ValueError(
												    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
												    ' to fix this.'      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				A        =			parser.parse_args()

				for task_guide in TASK_GUIDE_TO_MODELS.keys():
								check_model_list_for_task(task_guide, args.fix_and_overwrite)