"""simple docstring"""







import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a					:					Union[str, Any]=			logging.get_logger(__name__)


_a					:					List[str]=			{
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

_a					:					List[str]=			{
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

_a					:					List[str]=			"</w>"
_a					:					Dict=			"@@ "
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			set()
	__snake_case      :							Optional[Any]						=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		__snake_case      :							List[str]						=			char
	return pairs


# Speech2Text2 has no max input length
_a					:					Union[str, Any]=			{"facebook/s2t-wav2vec2-large-en-de": 1_024}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				List[str]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				List[str]   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[str]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		["""input_ids""", """attention_mask"""]



				def __init__(self    :				str     ,  _A    :				Any     ,  _A    :				Union[str, Any]="<s>"     ,  _A    :				Optional[int]="<pad>"     ,  _A    :				Tuple="</s>"     ,  _A    :				List[Any]="<unk>"     ,  _A    :				List[str]=False     ,  _A    :				List[Any]=None     ,  **_A    :				int     ,  )   ->     List[str]:
					super().__init__(
					    unk_token=_A     ,  bos_token=_A     ,  eos_token=_A     ,  pad_token=_A     ,  do_lower_case=_A     ,  **_A     ,  )

					__snake_case      :							List[str]						=			do_lower_case

					with open(_A     ,  encoding='utf-8') as vocab_handle:
						__snake_case      :							Optional[Any]						=			json.load(_A)
					__snake_case      :							Dict						=			{v: k for k, v in self.encoder.items()}

					if merges_file is None:
						logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding.")

						__snake_case      :							List[Any]						=			None
						__snake_case      :							List[str]						=			None
					else:
						with open(_A     ,  encoding='utf-8') as merges_handle:
							__snake_case      :							Tuple						=			merges_handle.read().split('\n')[:-1]

						__snake_case      :							List[Any]						=			[tuple(merge.split()[:2]) for merge in merges]
						__snake_case      :							List[str]						=			dict(zip(_A     ,  range(len(_A))))
						__snake_case      :							Tuple						=			{}



				@property
				def  _lowercase					(self    :				Dict)   ->     int:
					return len(self.decoder)



				def  _lowercase					(self    :				Tuple)   ->     Dict:
					return dict(self.encoder     ,  **self.added_tokens_encoder)



				def  _lowercase					(self    :				Optional[int]     ,  _A    :				Dict)   ->     Any:
					__snake_case      :							Tuple						=			tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
						return self.cache[token]
					__snake_case      :							List[Any]						=			get_pairs(_A)

					if not pairs:
						return token

					while True:
						__snake_case      :							Dict						=			min(_A     ,  key=lambda _A: self.bpe_ranks.get(_A     ,  float('inf')))
						if bigram not in self.bpe_ranks:
							break
						__snake_case		,    __snake_case      :							Dict						=			bigram
						__snake_case      :							Union[str, Any]						=			[]
						__snake_case      :							List[Any]						=			0
						while i < len(_A):
							try:
								__snake_case      :							Any						=			word.index(_A     ,  _A)
							except ValueError:
								new_word.extend(word[i:])
								break
							else:
								new_word.extend(word[i:j])
								__snake_case      :							List[Any]						=			j

							if word[i] == first and i < len(_A) - 1 and word[i + 1] == second:
								new_word.append(first + second)
								i += 2
							else:
								new_word.append(word[i])
								i += 1
						__snake_case      :							Tuple						=			tuple(_A)
						__snake_case      :							List[str]						=			new_word
						if len(_A) == 1:
							break
						else:
							__snake_case      :							int						=			get_pairs(_A)
					__snake_case      :							Tuple						=			' '.join(_A)
					if word == "\n  " + BPE_TOKEN_MERGES:
						__snake_case      :							Tuple						=			'\n' + BPE_TOKEN_MERGES

					if word.endswith(_A):
						__snake_case      :							List[str]						=			word.replace(_A     ,  '')

					__snake_case      :							Tuple						=			word.replace(' '     ,  _A)
					__snake_case      :							Optional[int]						=			word
					return word



				def  _lowercase					(self    :				str     ,  _A    :				List[str])   ->     str:

					if self.bpe_ranks is None:
						raise ValueError(
						    'This tokenizer was instantiated without a `merges.txt` file, so'
						    ' that it can only be used for decoding, not for encoding.'
						    'Make sure to provide `merges.txt` file at instantiation to enable '
						    'encoding.')

					if self.do_lower_case:
						__snake_case      :							Dict						=			text.lower()

					__snake_case      :							str						=			text.split()

					__snake_case      :							Optional[int]						=			[]
					for token in text:
						if token:
							split_tokens.extend(list(self.bpe(_A).split(' ')))

					return split_tokens



				def  _lowercase					(self    :				str     ,  _A    :				str)   ->     int:
					return self.encoder.get(_A     ,  self.encoder.get(self.unk_token))



				def  _lowercase					(self    :				int     ,  _A    :				int)   ->     str:
					__snake_case      :							str						=			self.decoder.get(_A     ,  self.unk_token)
					return result



				def  _lowercase					(self    :				Optional[int]     ,  _A    :				List[str])   ->     str:
					__snake_case      :							Union[str, Any]						=			' '.join(_A)

					# make sure @@ tokens are concatenated
					__snake_case      :							Tuple						=			''.join(string.split(_A))

					return string






				def  _lowercase					(self    :				str     ,  _A    :				str     ,  _A    :				Optional[str] = None)   ->     Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case      :							Any						=			os.path.join(
					    _A     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
					__snake_case      :							List[Any]						=			os.path.join(
					    _A     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

					with open(_A     ,  'w'     ,  encoding='utf-8') as f:
						f.write(json.dumps(self.encoder     ,  indent=2     ,  sort_keys=_A     ,  ensure_ascii=_A) + '\n')

					__snake_case      :							int						=			0
					if self.bpe_ranks is None:
						return (vocab_file,)

					with open(_A     ,  'w'     ,  encoding='utf-8') as writer:
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,  key=lambda _A: kv[1]):
							if index != token_index:
								logger.warning(
								    f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
								    ' Please check that the tokenizer is not corrupted!')
								__snake_case      :							str						=			token_index
							writer.write(' '.join(_A) + '\n')
							index += 1

					return (vocab_file, merges_file)






"""simple docstring"""







from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Optional[int]   =		"""new-model"""


if is_tf_available():

	class 		UpperCamelCase      (   lowercase						):
					UpperCAmelCase    :				List[str]   =		NewModelConfig

@require_tf
class 		UpperCamelCase      (   unittest.TestCase						):



				@slow
				def  _lowercase					(self    :				List[str])   ->     Dict:
					__snake_case      :							Any						=			'bert-base-cased'
					__snake_case      :							Optional[Any]						=			AutoConfig.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							Union[str, Any]						=			TFAutoModel.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				List[Any])   ->     str:
					__snake_case      :							Optional[int]						=			'bert-base-cased'
					__snake_case      :							List[Any]						=			AutoConfig.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							Dict						=			TFAutoModelForPreTraining.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Any)   ->     List[str]:
					for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							str						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							List[str]						=			TFAutoModelForCausalLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							List[str]						=			TFAutoModelForCausalLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Tuple)   ->     Dict:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Union[str, Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Union[str, Any]						=			TFAutoModelWithLMHead.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Union[str, Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Optional[Any]						=			TFAutoModelForMaskedLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Optional[int]						=			TFAutoModelForMaskedLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Dict						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							str						=			TFAutoModelForSeqaSeqLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Union[str, Any]						=			TFAutoModelForSeqaSeqLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				str)   ->     str:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
						__snake_case      :							Tuple						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Tuple						=			TFAutoModelForSequenceClassification.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
						__snake_case      :							List[str]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Any						=			TFAutoModelForQuestionAnswering.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				@require_tensorflow_probability
				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
						__snake_case      :							Optional[Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							int						=			TFAutoModelForTableQuestionAnswering.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Dict						=			TFAutoModelForTableQuestionAnswering.from_pretrained(
						    _A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[Any])   ->     Optional[Any]:
					__snake_case      :							Optional[int]						=			TFAutoModelWithLMHead.from_pretrained(_A)
					self.assertIsInstance(_A     ,  _A)
					self.assertEqual(model.num_parameters()     ,  1_44_10)
					self.assertEqual(model.num_parameters(only_trainable=_A)     ,  1_44_10)



				def  _lowercase					(self    :				Any)   ->     List[str]:
					__snake_case      :							List[Any]						=			TFAutoModelWithLMHead.from_pretrained(_A)
					self.assertIsInstance(_A     ,  _A)
					self.assertEqual(model.num_parameters()     ,  1_44_10)
					self.assertEqual(model.num_parameters(only_trainable=_A)     ,  1_44_10)



				def  _lowercase					(self    :				Optional[Any])   ->     str:
					# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
					__snake_case      :							Optional[Any]						=			TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							int						=			copy.deepcopy(model.config)
					__snake_case      :							int						=			['FunnelBaseModel']
					__snake_case      :							int						=			TFAutoModel.from_config(_A)
					self.assertIsInstance(_A     ,  _A)

					with tempfile.TemporaryDirectory() as tmp_dir:
						model.save_pretrained(_A)
						__snake_case      :							List[Any]						=			TFAutoModel.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     int:
					try:
						AutoConfig.register('new-model'     ,  _A)

						__snake_case      :							int						=			[
						    TFAutoModel,
						    TFAutoModelForCausalLM,
						    TFAutoModelForMaskedLM,
						    TFAutoModelForPreTraining,
						    TFAutoModelForQuestionAnswering,
						    TFAutoModelForSequenceClassification,
						    TFAutoModelForTokenClassification,
						]

						for auto_class in auto_classes:
							with self.subTest(auto_class.__name__):
								# Wrong config class will raise an error
								with self.assertRaises(_A):
									auto_class.register(_A     ,  _A)
								auto_class.register(_A     ,  _A)
								# Trying to register something existing in the Transformers library will raise an error
								with self.assertRaises(_A):
									auto_class.register(_A     ,  _A)

								# Now that the config is registered, it can be used as any other config with the auto-API
								__snake_case      :							Union[str, Any]						=			BertModelTester(self).get_config()
								__snake_case      :							Optional[int]						=			NewModelConfig(**tiny_config.to_dict())
								__snake_case      :							List[str]						=			auto_class.from_config(_A)
								self.assertIsInstance(_A     ,  _A)

								with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(_A)
									__snake_case      :							Tuple						=			auto_class.from_pretrained(_A)
									self.assertIsInstance(_A     ,  _A)

					finally:
						if "new-model" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["new-model"]
						for mapping in (
						    TF_MODEL_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						):
							if NewModelConfig in mapping._extra_content:
								del mapping._extra_content[NewModelConfig]



				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					with self.assertRaisesRegex(
					    _A     ,  'bert-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Any						=			TFAutoModel.from_pretrained('bert-base')



				def  _lowercase					(self    :				str)   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							Optional[Any]						=			TFAutoModel.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				int)   ->     Any:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'     ,  ):
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[Any])   ->     Any:
					with self.assertRaisesRegex(_A     ,  'Use `from_pt=True` to load this model'):
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')






				def  _lowercase					(self    :				str)   ->     Any:
					# Make sure we have cached the model.
					__snake_case      :							str						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
					with RequestCounter() as counter:
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
						self.assertEqual(counter.get_request_count     ,  0)
						self.assertEqual(counter.head_request_count     ,  1)
						self.assertEqual(counter.other_request_count     ,  0)

					# With a sharded checkpoint
					__snake_case      :							Optional[int]						=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
					with RequestCounter() as counter:
						__snake_case      :							Any						=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
						self.assertEqual(counter.get_request_count     ,  0)
						self.assertEqual(counter.head_request_count     ,  1)
						self.assertEqual(counter.other_request_count     ,  0)





