"""simple docstring"""






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A	     =					logging.get_logger(__name__)

A	     =					{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}




class 			__lowercase     (     __lowerCAmelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''conditional_detr'''
					__lowerCAmelCase					=					['''past_key_values''']
					__lowerCAmelCase					=					{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}







					def __init__(  self		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=3		,     _UpperCAmelCase=300		,     _UpperCAmelCase=6		,     _UpperCAmelCase=2048		,     _UpperCAmelCase=8		,     _UpperCAmelCase=6		,     _UpperCAmelCase=2048		,     _UpperCAmelCase=8		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=True		,     _UpperCAmelCase="relu"		,     _UpperCAmelCase=256		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=1.0		,     _UpperCAmelCase=False		,     _UpperCAmelCase="sine"		,     _UpperCAmelCase="resnet50"		,     _UpperCAmelCase=True		,     _UpperCAmelCase=False		,     _UpperCAmelCase=2		,     _UpperCAmelCase=5		,     _UpperCAmelCase=2		,     _UpperCAmelCase=1		,     _UpperCAmelCase=1		,     _UpperCAmelCase=2		,     _UpperCAmelCase=5		,     _UpperCAmelCase=2		,     _UpperCAmelCase=0.2_5		,     **_UpperCAmelCase		,     ):
									if backbone_config is not None and use_timm_backbone:
													raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

									if not use_timm_backbone:
													if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
																	__a	:  Optional[int]         =					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
													elif isinstance(lowerCAmelCase_		,     lowerCAmelCase_       ):
																	__a	:  int         =					backbone_config.get('''model_type'''       )
																	__a	:  int         =					CONFIG_MAPPING[backbone_model_type]
																	__a	:  Optional[int]         =					config_class.from_dict(lowerCAmelCase_       )

									__a	:  str         =					use_timm_backbone
									__a	:  Tuple         =					backbone_config
									__a	:  List[str]         =					num_channels
									__a	:  List[str]         =					num_queries
									__a	:  int         =					d_model
									__a	:  Union[str, Any]         =					encoder_ffn_dim
									__a	:  Any         =					encoder_layers
									__a	:  int         =					encoder_attention_heads
									__a	:  int         =					decoder_ffn_dim
									__a	:  Any         =					decoder_layers
									__a	:  Optional[int]         =					decoder_attention_heads
									__a	:  Optional[int]         =					dropout
									__a	:  List[str]         =					attention_dropout
									__a	:  List[Any]         =					activation_dropout
									__a	:  Tuple         =					activation_function
									__a	:  List[Any]         =					init_std
									__a	:  Optional[int]         =					init_xavier_std
									__a	:  Tuple         =					encoder_layerdrop
									__a	:  Union[str, Any]         =					decoder_layerdrop
									__a	:  str         =					encoder_layers
									__a	:  int         =					auxiliary_loss
									__a	:  str         =					position_embedding_type
									__a	:  Any         =					backbone
									__a	:  Optional[int]         =					use_pretrained_backbone
									__a	:  Optional[Any]         =					dilation
									# Hungarian matcher
									__a	:  str         =					class_cost
									__a	:  List[str]         =					bbox_cost
									__a	:  List[Any]         =					giou_cost
									# Loss coefficients
									__a	:  List[str]         =					mask_loss_coefficient
									__a	:  str         =					dice_loss_coefficient
									__a	:  str         =					cls_loss_coefficient
									__a	:  Optional[Any]         =					bbox_loss_coefficient
									__a	:  int         =					giou_loss_coefficient
									__a	:  Optional[Any]         =					focal_alpha
									super().__init__(is_encoder_decoder=lowerCAmelCase_		,     **lowerCAmelCase_       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.encoder_attention_heads







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.d_model







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					copy.deepcopy(self.__dict__       )
									if self.backbone_config is not None:
													__a	:  Tuple         =					self.backbone_config.to_dict()
									__a	:  Union[str, Any]         =					self.__class__.model_type
									return output






class 			__lowercase     (     __lowerCAmelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					version.parse('''1.11'''							)







					@property
					def 	_lowerCamelCase						(  self       ):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									        ('''pixel_mask''', {0: '''batch'''}),
									    ]       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return 1e-5







					@property
					def 	_lowerCamelCase						(  self       ):
									return 12






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

				__a	:  int         =					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_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  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_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_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	:  Optional[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)