





"""simple docstring"""






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

				__a :						Tuple			       =  start_index
				while not lines[end_index].startswith(a_):
								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      (			a_						:int)		->		int:
				__a :						int			       =  TASK_GUIDE_TO_MODELS[task_guide]
				__a :						Any			       =  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_   ,       set())
				__a :						Optional[Any]			       =  {
				    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      (			a_						:Any   ,       a_						:Optional[int]=False)		->		Union[str, Any]:
				__a       ,						__a       ,						__a       ,						__a :						Tuple			       =  _find_text_in_file(
				    filename=os.path.join(a_   ,       a_)   ,       start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''   ,       end_prompt='''<!--End of the generated tip-->'''   ,       )

				__a :						List[Any]			       =  get_model_list_for_task(a_)

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






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states