






import torch
from torch import nn

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


class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  @register_to_config
  def __init__(  self		,		*,
      _UpperCAmelCase = 4		,		_UpperCAmelCase = 768		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)      ->			Any:
     super().__init__()

     UpperCamelCase_						=  nn.Parameter(torch.zeros(_UpperCAmelCase			)			)

     # parameters for additional clip time embeddings
     UpperCamelCase_						=  nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)

     # parameters for encoder hidden states
     UpperCamelCase_						=  clip_extra_context_tokens
     UpperCamelCase_						=  nn.Linear(
         _UpperCAmelCase		,		self.clip_extra_context_tokens * cross_attention_dim			)
     UpperCamelCase_						=  nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  nn.LayerNorm(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		*, _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        UpperCamelCase_						=  image_embeddings.shape[0]
        UpperCamelCase_						=  self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
        UpperCamelCase_						=  classifier_free_guidance_embeddings.expand(
            _UpperCAmelCase		,		-1			)
        UpperCamelCase_						=  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]

     UpperCamelCase_						=  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, ...
     UpperCamelCase_						=  self.embedding_proj(_UpperCAmelCase			)
     UpperCamelCase_						=  self.clip_image_embeddings_project_to_time_embeddings(_UpperCAmelCase			)
     UpperCamelCase_						=  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"
     UpperCamelCase_						=  self.clip_extra_context_tokens_proj(_UpperCAmelCase			)
     UpperCamelCase_						=  clip_extra_context_tokens.reshape(_UpperCAmelCase		,		-1		,		self.clip_extra_context_tokens			)
     UpperCamelCase_						=  clip_extra_context_tokens.permute(0		,		2		,		1			)

     UpperCamelCase_						=  self.encoder_hidden_states_proj(_UpperCAmelCase			)
     UpperCamelCase_						=  self.text_encoder_hidden_states_norm(_UpperCAmelCase			)
     UpperCamelCase_						=  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 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


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

				lowercase_	    =   start_index
				while not lines[end_index].startswith(snake_case__   ):
								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 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    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 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


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

				lowercase_	    =   get_model_list_for_task(snake_case__   )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(snake_case__		,  snake_case__   )		,  '''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)




