




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
lowerCamelCase__					:  Dict      							=				'src/transformers'
lowerCamelCase__					:  List[Any]      							=				'docs/source/en/tasks'

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

      SCREAMING_SNAKE_CASE_      		=     start_index
      while not lines[end_index].startswith(__UpperCAmelCase				):
            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.
lowerCamelCase__					:  str      							=				direct_transformers_import(TRANSFORMERS_PATH)

lowerCamelCase__					:  List[Any]      							=				{
    '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`).
lowerCamelCase__					:  Any      							=				{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      		=     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCAmelCase     ,     set()				)
      SCREAMING_SNAKE_CASE_      		=     {
          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    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      List[Any]=False				)   ->					int:
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     _find_text_in_file(
          filename=os.path.join(__UpperCAmelCase     ,     __UpperCAmelCase				)     ,     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_      		=     get_model_list_for_task(__UpperCAmelCase				)

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(__UpperCAmelCase     ,     __UpperCAmelCase				)     ,     '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__":
   lowerCamelCase__					:  str      							=				argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   lowerCamelCase__					:  Union[str, Any]      							=				parser.parse_args()

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Dict      							=				{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "longformer"



 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[List[int], int] = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 1							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 30_522							,							_lowerCAmelCase    :						int = 768							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 3_072							,							_lowerCAmelCase    :						str = "gelu"							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						int = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						float = 0.02							,							_lowerCAmelCase    :						float = 1E-12							,							_lowerCAmelCase    :						bool = False							,							**_lowerCAmelCase    :						Union[str, Any]							,							):
       super().__init__(pad_token_id=_lowerCAmelCase							,							**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     attention_window
       SCREAMING_SNAKE_CASE_      		=     sep_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_hidden_layers
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     intermediate_size
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     type_vocab_size
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     onnx_export





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						"PretrainedConfig"							,							_lowerCAmelCase    :						str = "default"							,							_lowerCAmelCase    :						"List[PatchingSpec]" = None		):
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     True



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('global_attention_mask', dynamic_axis),
           ]		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     super().outputs
       if self.task == "default":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch'}
       return outputs



 @property
 def  lowerCAmelCase_    (				self    :						str		):
       return 1E-4



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset							,							14		)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						"PreTrainedTokenizerBase"							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						Optional[TensorType] = None							,							):
       SCREAMING_SNAKE_CASE_      		=     super().generate_dummy_inputs(
           preprocessor=_lowerCAmelCase							,							batch_size=_lowerCAmelCase							,							seq_length=_lowerCAmelCase							,							is_pair=_lowerCAmelCase							,							framework=_lowerCAmelCase		)
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE_      		=     torch.zeros_like(inputs['input_ids']		)
       # make every second token global
       SCREAMING_SNAKE_CASE_      		=     1

       return inputs