
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()

UpperCAmelCase__		:     Tuple     			=      {
    '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__							(					a		,		a		,		a		,		a		,		a=False		,		a=True      )      ->   Optional[Any]:



   if model_type not in MODEL_CLASSES:
      raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()      )}."""      )

   _A			,  _A			,  _A			,  _A:  Union[str, Any]					=       MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      _A:  Union[str, Any]					=       cached_file(__lowerCAmelCase		,		__lowerCAmelCase		,		force_download=not use_cached_models      )
   _A:  Optional[int]					=       config_class.from_json_file(__lowerCAmelCase      )
   _A:  int					=       True
   _A:  Optional[int]					=       True
   print(f"""Building TensorFlow model from configuration: {config}"""      )
   _A:  Dict					=       model_class(__lowerCAmelCase      )

   # Load weights from tf checkpoint
   if pytorch_checkpoint_path in aws_config_map.keys():
      _A:  List[Any]					=       cached_file(
          __lowerCAmelCase		,		__lowerCAmelCase		,		force_download=not use_cached_models      )
   # Load PyTorch checkpoint in tf2 model:
   _A:  List[str]					=       load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase		,		__lowerCAmelCase      )

   if compare_with_pt_model:
      _A:  Tuple					=       tf_model(tf_model.dummy_inputs		,		training=__lowerCAmelCase      )  # build the network

      _A:  int					=       torch.load(__lowerCAmelCase		,		map_location='''cpu'''      )
      _A:  Optional[Any]					=       pt_model_class.from_pretrained(
          pretrained_model_name_or_path=__lowerCAmelCase		,		config=__lowerCAmelCase		,		state_dict=__lowerCAmelCase      )

      with torch.no_grad():
         _A:  Any					=       pt_model(**pt_model.dummy_inputs      )

      _A:  int					=       pto[0].numpy()
      _A:  int					=       tfo[0].numpy()
      _A:  Dict					=       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__							(					a		,		a		,		a=None		,		a=None		,		a=False		,		a=False		,		a=False		,		a=False		,		)      ->   Tuple:



   if args_model_type is None:
      _A:  List[Any]					=       list(MODEL_CLASSES.keys()      )
   else:
      _A:  Optional[int]					=       [args_model_type]

   for j, model_type in enumerate(__lowerCAmelCase		,		start=1      ):
      print('''=''' * 1_00      )
      print(f""" Converting model type {j}/{len(__lowerCAmelCase      )}: {model_type}"""      )
      print('''=''' * 1_00      )
      if model_type not in MODEL_CLASSES:
         raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()      )}."""      )

      _A			,  _A			,  _A			,  _A			,  _A:  Union[str, Any]					=       MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         _A:  Optional[Any]					=       list(aws_model_maps.keys()      )
      if config_shortcut_names_or_path is None:
         _A:  List[str]					=       model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(__lowerCAmelCase		,		__lowerCAmelCase      )		,		start=1      ):
         print('''-''' * 1_00      )
         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
            _A:  Tuple					=       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('''-''' * 1_00      )

         if config_shortcut_name in aws_config_map:
            _A:  Any					=       cached_file(__lowerCAmelCase		,		__lowerCAmelCase		,		force_download=not use_cached_models      )
         else:
            _A:  List[str]					=       config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            _A:  Tuple					=       cached_file(__lowerCAmelCase		,		__lowerCAmelCase		,		force_download=not use_cached_models      )
         else:
            _A:  Any					=       model_shortcut_name

         if os.path.isfile(__lowerCAmelCase      ):
            _A:  Optional[Any]					=       '''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__":
   UpperCAmelCase__		:     int     			=      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.')
   UpperCAmelCase__		:     Dict     			=      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,
   )











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
