






import collections
import os
import re
from pathlib import Path


lowerCAmelCase_	:    List[str]            =      '''src/transformers'''


# Matches is_xxx_available()
lowerCAmelCase_	:    Union[str, Any]            =      re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase_	:    str            =      re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase_	:    Dict            =      re.compile(R'''\s+\"\S*\":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowerCAmelCase_	:    Optional[int]            =      re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase_	:    Dict            =      re.compile(R'''^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase_	:    List[Any]            =      re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCAmelCase_	:    List[str]            =      re.compile(R'''^\s+\"([^\"]+)\",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCAmelCase_	:    Tuple            =      re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase_	:    int            =      re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowerCAmelCase_	:    Optional[int]            =      re.compile(R'''^\s*try:''')
# Catches a line with else:
lowerCAmelCase_	:    Union[str, Any]            =      re.compile(R'''^\s*else:''')






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       if _re_test_backend.search(snake_case_		) is None:
              return None
       UpperCAmelCase	    =			[b[0] for b in _re_backend.findall(snake_case_		)]
       backends.sort()
       return "_and_".join(snake_case_		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       with open(snake_case_	,      """r"""	,      encoding="""utf-8"""	,      newline="""\n"""		) as f:
              UpperCAmelCase	    =			f.readlines()

       UpperCAmelCase	    =			0
       while line_index < len(snake_case_		) and not lines[line_index].startswith("""_import_structure = {"""		):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(snake_case_		):
              return None

       # First grab the objects without a specific backend in _import_structure
       UpperCAmelCase	    =			[]
       while not lines[line_index].startswith("""if TYPE_CHECKING"""		) and find_backend(lines[line_index]		) is None:
              UpperCAmelCase	    =			lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(snake_case_		):
                     UpperCAmelCase	    =			_re_one_line_import_struct.search(snake_case_		).groups()[0]
                     UpperCAmelCase	    =			re.findall(r"""\[([^\]]+)\]"""	,      snake_case_		)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(""", """		)]		)
                     line_index += 1
                     continue
              UpperCAmelCase	    =			_re_import_struct_key_value.search(snake_case_		)
              if single_line_import_search is not None:
                     UpperCAmelCase	    =			[obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """		) if len(snake_case_		) > 0]
                     objects.extend(snake_case_		)
              elif line.startswith(""" """ * 8 + """\""""		):
                     objects.append(line[9:-3]		)
              line_index += 1

       UpperCAmelCase	    =			{"""none""": objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith("""if TYPE_CHECKING"""		):
              # If the line is an if not is_backend_available, we grab all objects associated.
              UpperCAmelCase	    =			find_backend(lines[line_index]		)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]		) is None:
                     UpperCAmelCase	    =			None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]		) is None:
                            line_index += 1

                     line_index += 1

                     UpperCAmelCase	    =			[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]		) <= 1 or lines[line_index].startswith(""" """ * 4		):
                            UpperCAmelCase	    =			lines[line_index]
                            if _re_import_struct_add_one.search(snake_case_		) is not None:
                                   objects.append(_re_import_struct_add_one.search(snake_case_		).groups()[0]		)
                            elif _re_import_struct_add_many.search(snake_case_		) is not None:
                                   UpperCAmelCase	    =			_re_import_struct_add_many.search(snake_case_		).groups()[0].split(""", """		)
                                   UpperCAmelCase	    =			[obj[1:-1] for obj in imports if len(snake_case_		) > 0]
                                   objects.extend(snake_case_		)
                            elif _re_between_brackets.search(snake_case_		) is not None:
                                   UpperCAmelCase	    =			_re_between_brackets.search(snake_case_		).groups()[0].split(""", """		)
                                   UpperCAmelCase	    =			[obj[1:-1] for obj in imports if len(snake_case_		) > 0]
                                   objects.extend(snake_case_		)
                            elif _re_quote_object.search(snake_case_		) is not None:
                                   objects.append(_re_quote_object.search(snake_case_		).groups()[0]		)
                            elif line.startswith(""" """ * 8 + """\""""		):
                                   objects.append(line[9:-3]		)
                            elif line.startswith(""" """ * 12 + """\""""		):
                                   objects.append(line[13:-3]		)
                            line_index += 1

                     UpperCAmelCase	    =			objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       UpperCAmelCase	    =			[]
       while (
           line_index < len(snake_case_		)
           and find_backend(lines[line_index]		) is None
           and not lines[line_index].startswith("""else"""		)
       ):
              UpperCAmelCase	    =			lines[line_index]
              UpperCAmelCase	    =			_re_import.search(snake_case_		)
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(""", """		)		)
              elif line.startswith(""" """ * 8		):
                     objects.append(line[8:-2]		)
              line_index += 1

       UpperCAmelCase	    =			{"""none""": objects}
       # Let's continue with backend-specific objects
       while line_index < len(snake_case_		):
              # If the line is an if is_backend_available, we grab all objects associated.
              UpperCAmelCase	    =			find_backend(lines[line_index]		)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]		) is None:
                     UpperCAmelCase	    =			None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]		) is None:
                            line_index += 1

                     line_index += 1

                     UpperCAmelCase	    =			[]
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]		) <= 1 or lines[line_index].startswith(""" """ * 8		):
                            UpperCAmelCase	    =			lines[line_index]
                            UpperCAmelCase	    =			_re_import.search(snake_case_		)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(""", """		)		)
                            elif line.startswith(""" """ * 12		):
                                   objects.append(line[12:-2]		)
                            line_index += 1

                     UpperCAmelCase	    =			objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''

       def find_duplicates(lowerCAmelCase		):
              return [k for k, v in collections.Counter(snake_case_		).items() if v > 1]

       if list(import_dict_objects.keys()		) != list(type_hint_objects.keys()		):
              return ["Both sides of the init do not have the same backends!"]

       UpperCAmelCase	    =			[]
       for key in import_dict_objects.keys():
              UpperCAmelCase	    =			find_duplicates(import_dict_objects[key]		)
              if duplicate_imports:
                     errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''		)
              UpperCAmelCase	    =			find_duplicates(type_hint_objects[key]		)
              if duplicate_type_hints:
                     errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''		)

              if sorted(set(import_dict_objects[key]		)		) != sorted(set(type_hint_objects[key]		)		):
                     UpperCAmelCase	    =			"""base imports""" if key == """none""" else F'''{key} backend'''
                     errors.append(F'''Differences for {name}:'''		)
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''		)
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''		)
       return errors






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       for root, _, files in os.walk(snake_case_		):
              if "__init__.py" in files:
                     UpperCAmelCase	    =			os.path.join(snake_case_	,      """__init__.py"""		)
                     UpperCAmelCase	    =			parse_init(snake_case_		)
                     if objects is not None:
                            UpperCAmelCase	    =			analyze_results(*snake_case_		)
                            if len(snake_case_		) > 0:
                                   UpperCAmelCase	    =			F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                                   failures.append("""\n""".join(snake_case_		)		)
       if len(snake_case_		) > 0:
              raise ValueError("""\n\n""".join(snake_case_		)		)






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       for path, directories, files in os.walk(snake_case_		):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith("""_"""		):
                            directories.remove(snake_case_		)
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(snake_case_		) / folder).glob("""*.py"""		)		)		) == 0:
                            continue
                     UpperCAmelCase	    =			str((Path(snake_case_		) / folder).relative_to(snake_case_		)		)
                     UpperCAmelCase	    =			short_path.replace(os.path.sep	,      """."""		)
                     submodules.append(snake_case_		)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     UpperCAmelCase	    =			str((Path(snake_case_		) / fname).relative_to(snake_case_		)		)
                     UpperCAmelCase	    =			short_path.replace(""".py"""	,      """"""		).replace(os.path.sep	,      """."""		)
                     if len(submodule.split("""."""		)		) == 1:
                            submodules.append(snake_case_		)
       return submodules


lowerCAmelCase_	:    List[str]            =      [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       # This is to make sure the transformers module imported is the one in the repo.
       from transformers.utils import direct_transformers_import

       UpperCAmelCase	    =			direct_transformers_import(snake_case_		)

       UpperCAmelCase	    =			set(transformers._import_structure.keys()		)
       # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
       # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
       # (potentiall re-) add them.
       with open(os.path.join(snake_case_	,      """__init__.py"""		)	,      """r"""		) as f:
              UpperCAmelCase	    =			f.read()
       import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""	,      snake_case_		)		)		)

       UpperCAmelCase	    =			[
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in import_structure_keys
       ]

       if len(snake_case_		) > 0:
              UpperCAmelCase	    =			"""\n""".join(F'''- {module}''' for module in module_not_registered		)
              raise ValueError(
                  """The following submodules are not properly registed in the main init of Transformers:\n"""
                  F'''{list_of_modules}\n'''
                  """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""		)


if __name__ == "__main__":
 check_all_inits()
 check_submodules()








"""simple docstring"""







import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowerCAmelCase_	:    List[str]            =      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              UpperCAmelCase	    =			cached_file(
                  lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       # Load PyTorch checkpoint in tf2 model:
       UpperCAmelCase	    =			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase	,      lowerCAmelCase		)

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

              with torch.no_grad():
                     UpperCAmelCase	    =			pt_model(**pt_model.dummy_inputs		)

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			np.amax(np.abs(np_pt - np_tf		)		)
              print(F'''Max absolute difference between models outputs {diff}'''		)
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''		)
       tf_model.save_weights(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {model_type}'''		)
              print("""=""" * 100		)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			list(aws_model_maps.keys()		)
              if config_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      start=1		):
                     print("""-""" * 100		)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
                                   continue
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase	,      pytorch_checkpoint_path=lowerCAmelCase	,      config_file=lowerCAmelCase	,      tf_dump_path=os.path.join(lowerCAmelCase	,      model_shortcut_name + """-tf_model.h5"""		)	,      compare_with_pt_model=lowerCAmelCase	,      )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase		)
                            os.remove(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )
