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()

SCREAMING_SNAKE_CASE__		:					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     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			List[str]=False     ,  lowercase__  :			List[Any]=True ):







					'''simple docstring'''


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

					SCREAMING_SNAKE_CASE__				:				List[str]			=		MODEL_CLASSES[model_type]

					# Initialise TF model
					if config_file in aws_config_map:
										SCREAMING_SNAKE_CASE__				:				List[str]			=		cached_file(lowercase__     ,  lowercase__     ,  force_download=not use_cached_models )
					SCREAMING_SNAKE_CASE__				:				Tuple			=		config_class.from_json_file(lowercase__ )
					SCREAMING_SNAKE_CASE__				:				Dict			=		True
					SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		True
					print(f'''Building TensorFlow model from configuration: {config}''' )
					SCREAMING_SNAKE_CASE__				:				str			=		model_class(lowercase__ )

					# Load weights from tf checkpoint
					if pytorch_checkpoint_path in aws_config_map.keys():
										SCREAMING_SNAKE_CASE__				:				Dict			=		cached_file(
										    lowercase__     ,  lowercase__     ,  force_download=not use_cached_models )
					# Load PyTorch checkpoint in tf2 model:
					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		load_pytorch_checkpoint_in_tfa_model(lowercase__     ,  lowercase__ )

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

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

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

										SCREAMING_SNAKE_CASE__				:				int			=		pto[0].numpy()
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		tfo[0].numpy()
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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(lowercase__     ,  save_format='h5' )







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Dict     ,  lowercase__  :			Union[str, Any]=None     ,  lowercase__  :			List[str]=None     ,  lowercase__  :			Any=False     ,  lowercase__  :			Union[str, Any]=False     ,  lowercase__  :			Union[str, Any]=False     ,  lowercase__  :			int=False     ,  ):







					'''simple docstring'''


					if args_model_type is None:
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		list(MODEL_CLASSES.keys() )
					else:
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		[args_model_type]

					for j, model_type in enumerate(lowercase__     ,  start=1 ):
										print('=' * 1_00 )
										print(f''' Converting model type {j}/{len(lowercase__ )}: {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() )}.''' )

										SCREAMING_SNAKE_CASE__				:				Tuple			=		MODEL_CLASSES[model_type]

										if model_shortcut_names_or_path is None:
															SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		list(aws_model_maps.keys() )
										if config_shortcut_names_or_path is None:
															SCREAMING_SNAKE_CASE__				:				List[str]			=		model_shortcut_names_or_path

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(lowercase__     ,  lowercase__ )     ,  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
																				SCREAMING_SNAKE_CASE__				:				int			=		model_shortcut_name
															elif only_convert_finetuned_models:
																				print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}''' )
																				continue
															print(
															    f'''    Converting checkpoint {i}/{len(lowercase__ )}: {model_shortcut_name} - model_type {model_type}''' )
															print('-' * 1_00 )

															if config_shortcut_name in aws_config_map:
																				SCREAMING_SNAKE_CASE__				:				List[Any]			=		cached_file(lowercase__     ,  lowercase__     ,  force_download=not use_cached_models )
															else:
																				SCREAMING_SNAKE_CASE__				:				Any			=		config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				SCREAMING_SNAKE_CASE__				:				Tuple			=		cached_file(lowercase__     ,  lowercase__     ,  force_download=not use_cached_models )
															else:
																				SCREAMING_SNAKE_CASE__				:				List[str]			=		model_shortcut_name

															if os.path.isfile(lowercase__ ):
																				SCREAMING_SNAKE_CASE__				:				int			=		'converted_model'

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE__		:					List[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.")
						SCREAMING_SNAKE_CASE__		:					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 unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


SCREAMING_SNAKE_CASE__		:					List[Any]						=					get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




  def 	__lowercase(      self     :	int				)->       List[Any]:




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		PegasusTokenizer(a_				)
       tokenizer.save_pretrained(self.tmpdirname				)




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




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/pegasus-large'				)




  def 	__lowercase(      self     :	Any     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[Any]				)->       Optional[int]:




       """simple docstring"""
       return ("This is a test", "This is a test")




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		'</s>'
       SCREAMING_SNAKE_CASE__				:				Any			=		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     :	Dict				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<pad>'				)
       self.assertEqual(vocab_keys[1]     , '</s>'				)
       self.assertEqual(vocab_keys[-1]     , 'v'				)
       self.assertEqual(len(a_				)     , 1103				)




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




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 1103				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				List[str]			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       SCREAMING_SNAKE_CASE__				:				Any			=		'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       SCREAMING_SNAKE_CASE__				:				int			=		'To ensure a smooth flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		['This is going to be way too long.' * 150, 'short example']
       SCREAMING_SNAKE_CASE__				:				int			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  @slow
  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a_     , model_name='google/bigbird-pegasus-large-arxiv'     , revision='ba85d0851d708441f91440d509690f1ab6353415'     , )




@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




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




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		PegasusTokenizer(a_     , offset=0     , mask_token_sent=a_     , mask_token='[MASK]'				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'				)




  def 	__lowercase(      self     :	List[str]     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Tuple				)->       str:




       """simple docstring"""
       return ("This is a test", "This is a test")




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				str			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				str			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		['This is going to be way too long.' * 1000, 'short example']
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				str			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		(
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self._large_tokenizer(a_				).input_ids

       self.assertListEqual(
           a_     , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]     , )



