


'''simple docstring'''





import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				str					      =			['''input_ids''', '''attention_mask''']
			def __init__(  self							:			List[Any]			,    lowerCAmelCase_							:			Union[str, Any]="</s>"			,    lowerCAmelCase_							:			Union[str, Any]="<unk>"			,    lowerCAmelCase_							:			Any="<pad>"			,    lowerCAmelCase_							:			Any=125			,    lowerCAmelCase_							:			Tuple=None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		None:
						# Add extra_ids to the special token list
						if extra_ids > 0 and additional_special_tokens is None:
									__A=	[F"""<extra_id_{i}>""" for i in range(lowerCAmelCase_	)]
						elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra_id special tokens
									__A=	len(set(filter(lambda lowerCAmelCase_	: bool('extra_id' in str(lowerCAmelCase_	)	)			,    lowerCAmelCase_	)	)	)
									if extra_tokens != extra_ids:
												raise ValueError(
												    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
												    ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
												    ' extra_ids tokens'	)

						__A=	AddedToken(lowerCAmelCase_			,    lstrip=lowerCAmelCase_			,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else pad_token
						__A=	AddedToken(lowerCAmelCase_			,    lstrip=lowerCAmelCase_			,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else eos_token
						__A=	AddedToken(lowerCAmelCase_			,    lstrip=lowerCAmelCase_			,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else unk_token

						super().__init__(
						    eos_token=lowerCAmelCase_			,    unk_token=lowerCAmelCase_			,    pad_token=lowerCAmelCase_			,    extra_ids=lowerCAmelCase_			,    additional_special_tokens=lowerCAmelCase_			,    **lowerCAmelCase_			,    )

						__A=	extra_ids

						__A=	2**8  # utf is 8 bits

						# define special tokens dict
						__A=	{
						    self.pad_token: 0,
						    self.eos_token: 1,
						    self.unk_token: 2,
						}
						__A=	len(self.special_tokens_encoder	)
						__A=	len(lowerCAmelCase_	)
						for i, token in enumerate(lowerCAmelCase_	):
									__A=	self.vocab_size + i - n
						__A=	{v: k for k, v in self.special_tokens_encoder.items()}
			@property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Union[str, Any]:
						return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			Optional[List[int]] = None			,    lowerCAmelCase_							:			bool = False	)      ->		List[int]:
						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase_			,    token_ids_a=lowerCAmelCase_			,    already_has_special_tokens=lowerCAmelCase_	)

						# normal case: some special tokens
						if token_ids_a is None:
									return ([0] * len(lowerCAmelCase_	)) + [1]
						return ([0] * len(lowerCAmelCase_	)) + [1] + ([0] * len(lowerCAmelCase_	)) + [1]
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[int]	)      ->		List[int]:
						if len(lowerCAmelCase_	) > 0 and token_ids[-1] == self.eos_token_id:
									warnings.warn(
									    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
									    ' eos tokens being added.'	)
									return token_ids
						else:
									return token_ids + [self.eos_token_id]
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			Optional[List[int]] = None	)      ->		List[int]:
						__A=	[self.eos_token_id]

						if token_ids_a is None:
									return len(token_ids_a + eos	) * [0]
						return len(token_ids_a + eos + token_ids_a + eos	) * [0]
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			Optional[List[int]] = None	)      ->		List[int]:
						__A=	self._add_eos_if_not_present(lowerCAmelCase_	)
						if token_ids_a is None:
									return token_ids_a
						else:
									__A=	self._add_eos_if_not_present(lowerCAmelCase_	)
									return token_ids_a + token_ids_a
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			str	)      ->		List[str]:
						__A=	[chr(lowerCAmelCase_	) for i in text.encode('utf-8'	)]
						return tokens
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Optional[int]	)      ->		Optional[int]:
						if token in self.special_tokens_encoder:
									__A=	self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
									__A=	self.added_tokens_encoder[token]
						elif len(lowerCAmelCase_	) != 1:
									__A=	self.unk_token_id
						else:
									__A=	ord(lowerCAmelCase_	) + self._num_special_tokens
						return token_id
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Union[str, Any]:
						if index in self.special_tokens_decoder:
									__A=	self.special_tokens_decoder[index]
						else:
									__A=	chr(index - self._num_special_tokens	)
						return token
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[int]:
						__A=	b''
						for token in tokens:
									if token in self.special_tokens_decoder:
												__A=	self.special_tokens_decoder[token].encode('utf-8'	)
									elif token in self.added_tokens_decoder:
												__A=	self.special_tokens_decoder[token].encode('utf-8'	)
									elif token in self.special_tokens_encoder:
												__A=	token.encode('utf-8'	)
									elif token in self.added_tokens_encoder:
												__A=	token.encode('utf-8'	)
									else:
												__A=	bytes([ord(lowerCAmelCase_	)]	)
									bstring += tok_string
						__A=	bstring.decode('utf-8'			,    errors='ignore'	)
						return string

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[str] = None	)      ->		Tuple[str]:
						return ()







'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					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

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)
@is_pt_tf_cross_test
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModel.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModel.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)



