



"""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					:	str							=		'src/transformers'
a					:	Union[str, Any]							=		'docs/source/en/tasks'


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Tuple:





    """simple docstring"""

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

    _UpperCAmelCase          =   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					:	Dict							=		direct_transformers_import(TRANSFORMERS_PATH)

a					:	Dict							=		{
    '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					:	Optional[Any]							=		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}


def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   TASK_GUIDE_TO_MODELS[task_guide]
    _UpperCAmelCase          =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCamelCase					,      set()      )
    _UpperCAmelCase          =   {
        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	(						_A					,      _A=False      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   _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-->"""					,      )

    _UpperCAmelCase          =   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					:	Tuple							=		argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 a					:	Dict							=		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 argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)