





import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase :			str     		=    get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
@require_tokenizers
class 					A__       (				a_					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							Optional[int]			    =    ReformerTokenizer
   __A  :							Union[str, Any]			    =    ReformerTokenizerFast
   __A  :							Tuple			    =    True
   __A  :							Any			    =    False
   __A  :							Optional[Any]			    =    True

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          super().setUp()

          a__		:      Optional[int]					=							ReformerTokenizer(_a   ,    keep_accents=_a)
          tokenizer.save_pretrained(self.tmpdirname)

   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      List[Any]					=							"<s>"
          a__		:      Dict					=							1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a)   ,    _a)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a)   ,    _a)

   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      int					=							list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]   ,    '<unk>')
          self.assertEqual(vocab_keys[1]   ,    '<s>')
          self.assertEqual(vocab_keys[-1]   ,    'j')
          self.assertEqual(len(_a)   ,    1000)

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          self.assertEqual(self.get_tokenizer().vocab_size   ,    1000)

   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          if not self.test_rust_tokenizer:
                 return

          a__		:      int					=							self.get_tokenizer()
          a__		:      List[str]					=							self.get_rust_tokenizer()

          a__		:      List[str]					=							"I was born in 92000, and this is falsé."

          a__		:      Optional[int]					=							tokenizer.tokenize(_a)
          a__		:      Union[str, Any]					=							rust_tokenizer.tokenize(_a)
          self.assertListEqual(_a   ,    _a)

          a__		:      Optional[Any]					=							tokenizer.encode(_a   ,    add_special_tokens=_a)
          a__		:      Union[str, Any]					=							rust_tokenizer.encode(_a   ,    add_special_tokens=_a)
          self.assertListEqual(_a   ,    _a)

          a__		:      Optional[Any]					=							self.get_rust_tokenizer()
          a__		:      List[str]					=							tokenizer.encode(_a)
          a__		:      Union[str, Any]					=							rust_tokenizer.encode(_a)
          self.assertListEqual(_a   ,    _a)

   def 				__lowercase						(   self   ,    lowercase=15)     ->		Any:







          '''simple docstring'''

          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                        a__		:      Any					=							self.rust_tokenizer_class.from_pretrained(_a   ,    **_a)

                        # Simple input
                        a__		:      Optional[int]					=							"This is a simple input"
                        a__		:      Optional[int]					=							["This is a simple input 1", "This is a simple input 2"]
                        a__		:      Optional[Any]					=							("This is a simple input", "This is a pair")
                        a__		:      Optional[Any]					=							[
                            ("This is a simple input 1", "This is a simple input 2"),
                            ("This is a simple pair 1", "This is a simple pair 2"),
                        ]

                        # Simple input tests
                        self.assertRaises(_a   ,    tokenizer_r.encode   ,    _a   ,    max_length=_a   ,    padding='max_length')

                        # Simple input
                        self.assertRaises(_a   ,    tokenizer_r.encode_plus   ,    _a   ,    max_length=_a   ,    padding='max_length')

                        # Simple input
                        self.assertRaises(
                            _a   ,    tokenizer_r.batch_encode_plus   ,    _a   ,    max_length=_a   ,    padding='max_length'   ,    )

                        # Pair input
                        self.assertRaises(_a   ,    tokenizer_r.encode   ,    _a   ,    max_length=_a   ,    padding='max_length')

                        # Pair input
                        self.assertRaises(_a   ,    tokenizer_r.encode_plus   ,    _a   ,    max_length=_a   ,    padding='max_length')

                        # Pair input
                        self.assertRaises(
                            _a   ,    tokenizer_r.batch_encode_plus   ,    _a   ,    max_length=_a   ,    padding='max_length'   ,    )

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      List[Any]					=							ReformerTokenizer(_a   ,    keep_accents=_a)

          a__		:      List[str]					=							tokenizer.tokenize('This is a test')
          self.assertListEqual(_a   ,    ['▁This', '▁is', '▁a', '▁t', 'est'])

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_a)   ,    [285, 46, 10, 170, 382]   ,    )

          a__		:      Union[str, Any]					=							tokenizer.tokenize('I was born in 92000, and this is falsé.')
          self.assertListEqual(
              _a   ,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]   ,    )
          a__		:      Dict					=							tokenizer.convert_tokens_to_ids(_a)
          self.assertListEqual(
              _a   ,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]   ,    )

          a__		:      List[str]					=							tokenizer.convert_ids_to_tokens(_a)
          self.assertListEqual(
              _a   ,    [
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]   ,    )

   @cached_property
   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')

   @slow
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      Dict					=							"Hello World!"
          a__		:      List[Any]					=							[126, 32, 262, 152, 38, 72, 287]

          self.assertListEqual(_a   ,    self.big_tokenizer.encode(_a))

   @slow
   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							(
              "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
              " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
          )
          a__		:      Union[str, Any]					=							[
              108,
              265,
              24,
              111,
              4,
              258,
              156,
              35,
              28,
              275,
              3,
              259,
              297,
              260,
              84,
              4,
              35,
              110,
              44,
              8,
              259,
              91,
              268,
              21,
              11,
              209,
              274,
              109,
              266,
              277,
              117,
              86,
              93,
              315,
              258,
              278,
              258,
              277,
              258,
              0,
              258,
              288,
              258,
              319,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              287,
              258,
              315,
              258,
              289,
              258,
              278,
              99,
              269,
              266,
              262,
              8,
              259,
              241,
              4,
              217,
              230,
              268,
              266,
              55,
              168,
              106,
              75,
              193,
              266,
              223,
              27,
              49,
              26,
              282,
              25,
              264,
              299,
              19,
              26,
              0,
              258,
              277,
              117,
              86,
              93,
              176,
              183,
              270,
              11,
              262,
              42,
              61,
              265,
          ]

          self.assertListEqual(_a   ,    self.big_tokenizer.encode(_a))

   @require_torch
   @slow
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          a__		:      Union[str, Any]					=							list(self.big_tokenizer.get_vocab().keys())[:10]
          a__		:      List[Any]					=							" ".join(_a)
          a__		:      Tuple					=							self.big_tokenizer.encode_plus(_a   ,    return_tensors='pt')
          a__		:      Optional[Any]					=							self.big_tokenizer.batch_encode_plus([sequence, sequence]   ,    return_tensors='pt')

          a__		:      Optional[Any]					=							ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          a__		:      Tuple					=							encoded_sequence["input_ids"].shape
          a__		:      List[str]					=							ReformerModel(_a)

          # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
                 model(**_a)
                 model(**_a)

   @slow
   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      List[str]					=							{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # This tokenizer does not know some characters like ")".
          # That is the reason why we use very simple texts here.
          # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
          a__		:      Tuple					=							[
              "This is a very simple sentence.",
              "The quick brown fox jumps over the lazy dog.",
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=_a   ,    model_name='google/reformer-crime-and-punishment'   ,    revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'   ,    padding=_a   ,    sequences=_a   ,    )




"""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()

_A     :			Optional[int]  						=			{
    """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     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Dict	,					__snake_case							:			str	,					__snake_case							:			Dict=False	,					__snake_case							:			Optional[Any]=True    )					->							Dict:
       if model_type not in MODEL_CLASSES:
              raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}."""    )

       lowercase						,       lowercase						,       lowercase						,       lowercase       :							Tuple									=				MODEL_CLASSES[model_type]

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

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

       if compare_with_pt_model:
              lowercase       :							Optional[int]									=				tf_model(tf_model.dummy_inputs	,					training=__snake_case    )  # build the network

              lowercase       :							Optional[Any]									=				torch.load(__snake_case	,					map_location="cpu"    )
              lowercase       :							Tuple									=				pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=__snake_case	,					config=__snake_case	,					state_dict=__snake_case    )

              with torch.no_grad():
                     lowercase       :							Dict									=				pt_model(**pt_model.dummy_inputs    )

              lowercase       :							List[str]									=				pto[0].numpy()
              lowercase       :							List[Any]									=				tfo[0].numpy()
              lowercase       :							List[str]									=				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(__snake_case	,					save_format="h5"    )


def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Optional[int]	,					__snake_case							:			Dict=None	,					__snake_case							:			List[str]=None	,					__snake_case							:			Optional[int]=False	,					__snake_case							:			Optional[int]=False	,					__snake_case							:			List[Any]=False	,					__snake_case							:			Tuple=False	,					)					->							int:
       if args_model_type is None:
              lowercase       :							Any									=				list(MODEL_CLASSES.keys()    )
       else:
              lowercase       :							int									=				[args_model_type]

       for j, model_type in enumerate(__snake_case	,					start=1    ):
              print("=" * 100    )
              print(f""" Converting model type {j}/{len(__snake_case    )}: {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()    )}."""    )

              lowercase						,       lowercase						,       lowercase						,       lowercase						,       lowercase       :							int									=				MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowercase       :							Optional[int]									=				list(aws_model_maps.keys()    )
              if config_shortcut_names_or_path is None:
                     lowercase       :							Union[str, Any]									=				model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(__snake_case	,					__snake_case    )	,					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
                            lowercase       :							Optional[Any]									=				model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""    )
                            continue
                     print(
                         f"""    Converting checkpoint {i}/{len(__snake_case    )}: {model_shortcut_name} - model_type {model_type}"""    )
                     print("-" * 100    )

                     if config_shortcut_name in aws_config_map:
                            lowercase       :							List[Any]									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Optional[int]									=				config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowercase       :							int									=				cached_file(__snake_case	,					__snake_case	,					force_download=not use_cached_models    )
                     else:
                            lowercase       :							Union[str, Any]									=				model_shortcut_name

                     if os.path.isfile(__snake_case    ):
                            lowercase       :							Union[str, Any]									=				"converted_model"

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


if __name__ == "__main__":
   _A     :			Tuple  						=			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.""")
   _A     :			str  						=			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,
   )


