import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					__UpperCamelCase	: Tuple    		=  'platform'

					import jax
					import jax.numpy as jnp

					from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
					    FlaxBlenderbotSmallForConditionalGeneration,
					    FlaxBlenderbotSmallModel,
					    shift_tokens_right,
					)
def    A			(					_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						):

						if attention_mask is None:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.where(input_ids != config.pad_token_id	,						1	,						0		)
						if decoder_attention_mask is None:
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.where(decoder_input_ids != config.pad_token_id	,						1	,						0		)
						if head_mask is None:
												SCREAMING_SNAKE_CASE							:					Any           =							np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}





class       lowercase__		:


						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[str]=13					,	UpperCamelCase__			:       Optional[int]=7					,	UpperCamelCase__			:       List[str]=True					,	UpperCamelCase__			:       int=False					,	UpperCamelCase__			:       List[str]=99					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=2					,	UpperCamelCase__			:       Union[str, Any]=4					,	UpperCamelCase__			:       List[Any]=4					,	UpperCamelCase__			:       Any="gelu"					,	UpperCamelCase__			:       str=0.1					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=32					,	UpperCamelCase__			:       Optional[int]=2					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       str=0					,	UpperCamelCase__			:       Optional[int]=0.02					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							parent
												SCREAMING_SNAKE_CASE							:					List[str]           =							batch_size
												SCREAMING_SNAKE_CASE							:					Any           =							seq_length
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							is_training
												SCREAMING_SNAKE_CASE							:					Any           =							use_labels
												SCREAMING_SNAKE_CASE							:					str           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					str           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Tuple           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[str]           =							eos_token_id
												SCREAMING_SNAKE_CASE							:					List[str]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							bos_token_id
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,	self.vocab_size		)					,	3					,	self.vocab_size		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,	dtype=np.intaa		))					,	-1		)

												SCREAMING_SNAKE_CASE							:					Any           =							shift_tokens_right(lowercase_					,	1					,	2		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							BlenderbotSmallConfig(
												    vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	encoder_layers=self.num_hidden_layers					,	decoder_layers=self.num_hidden_layers					,	encoder_attention_heads=self.num_attention_heads					,	decoder_attention_heads=self.num_attention_heads					,	encoder_ffn_dim=self.intermediate_size					,	decoder_ffn_dim=self.intermediate_size					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	max_position_embeddings=self.max_position_embeddings					,	eos_token_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	initializer_range=self.initializer_range					,	use_cache=lowercase_					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							prepare_blenderbot_inputs_dict(lowercase_					,	lowercase_					,	lowercase_		)
												return config, inputs_dict


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							self.prepare_config_and_inputs()
												return config, inputs_dict


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							20
												SCREAMING_SNAKE_CASE							:					Any           =							model_class_name(lowercase_		)

												SCREAMING_SNAKE_CASE							:					int           =							model.encode(inputs_dict['''input_ids''']		)

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												SCREAMING_SNAKE_CASE							:					str           =							model.init_cache(decoder_input_ids.shape[0]					,	lowercase_					,	lowercase_		)
												SCREAMING_SNAKE_CASE							:					int           =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,	dtype='''i4'''		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							model.decode(
												    decoder_input_ids[:, :-1]					,	lowercase_					,	decoder_attention_mask=lowercase_					,	past_key_values=lowercase_					,	decoder_position_ids=lowercase_					,	)

												SCREAMING_SNAKE_CASE							:					List[str]           =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype='''i4'''		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model.decode(
												    decoder_input_ids[:, -1:]					,	lowercase_					,	decoder_attention_mask=lowercase_					,	past_key_values=outputs_cache.past_key_values					,	decoder_position_ids=lowercase_					,	)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							model.decode(lowercase_					,	lowercase_		)

												SCREAMING_SNAKE_CASE							:					str           =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
												self.parent.assertTrue(diff < 1E-3					,	msg=f"""Max diff is {diff}"""		)



						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							20
												SCREAMING_SNAKE_CASE							:					Any           =							model_class_name(lowercase_		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model.encode(inputs_dict['''input_ids''']		)

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Any           =							(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												SCREAMING_SNAKE_CASE							:					Dict           =							jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
												    ]					,	axis=-1					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model.init_cache(decoder_input_ids.shape[0]					,	lowercase_					,	lowercase_		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)

												SCREAMING_SNAKE_CASE							:					str           =							model.decode(
												    decoder_input_ids[:, :-1]					,	lowercase_					,	decoder_attention_mask=lowercase_					,	past_key_values=lowercase_					,	decoder_position_ids=lowercase_					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype='''i4'''		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model.decode(
												    decoder_input_ids[:, -1:]					,	lowercase_					,	past_key_values=outputs_cache.past_key_values					,	decoder_attention_mask=lowercase_					,	decoder_position_ids=lowercase_					,	)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model.decode(lowercase_					,	lowercase_					,	decoder_attention_mask=lowercase_		)

												SCREAMING_SNAKE_CASE							:					str           =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
												self.parent.assertTrue(diff < 1E-3					,	msg=f"""Max diff is {diff}"""		)






@require_flax
class       lowercase__		(     unittest.TestCase):
						UpperCamelCase_  =	99


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array(
												    [
												        [71, 82, 18, 33, 46, 91, 2],
												        [68, 34, 26, 58, 30, 82, 2],
												        [5, 97, 17, 39, 94, 40, 2],
												        [76, 83, 94, 25, 70, 78, 2],
												        [87, 59, 41, 35, 48, 66, 2],
												        [55, 13, 16, 58, 5, 2, 1],  # note padding
												        [64, 27, 31, 51, 12, 75, 2],
												        [52, 64, 86, 17, 83, 39, 2],
												        [48, 61, 9, 24, 71, 82, 2],
												        [26, 1, 60, 48, 22, 13, 2],
												        [21, 5, 62, 28, 14, 76, 2],
												        [45, 98, 37, 86, 59, 48, 2],
												        [70, 70, 50, 9, 28, 0, 2],
												    ]					,	dtype=np.intaa					,	)

												SCREAMING_SNAKE_CASE							:					List[str]           =							input_ids.shape[0]
												SCREAMING_SNAKE_CASE							:					str           =							BlenderbotSmallConfig(
												    vocab_size=self.vocab_size					,	d_model=24					,	encoder_layers=2					,	decoder_layers=2					,	encoder_attention_heads=2					,	decoder_attention_heads=2					,	encoder_ffn_dim=32					,	decoder_ffn_dim=32					,	max_position_embeddings=48					,	eos_token_id=2					,	pad_token_id=1					,	bos_token_id=0					,	)
												return config, input_ids, batch_size


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							self._get_config_and_data()
												SCREAMING_SNAKE_CASE							:					Any           =							FlaxBlenderbotSmallForConditionalGeneration(lowercase_		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							lm_model(input_ids=lowercase_		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							(batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape					,	lowercase_		)


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							BlenderbotSmallConfig(
												    vocab_size=self.vocab_size					,	d_model=14					,	encoder_layers=2					,	decoder_layers=2					,	encoder_attention_heads=2					,	decoder_attention_heads=2					,	encoder_ffn_dim=8					,	decoder_ffn_dim=8					,	max_position_embeddings=48					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							FlaxBlenderbotSmallForConditionalGeneration(lowercase_		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,	dtype=np.intaa		)
												SCREAMING_SNAKE_CASE							:					str           =							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,	dtype=np.intaa		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							lm_model(input_ids=lowercase_					,	decoder_input_ids=lowercase_		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							(*summary.shape, config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape					,	lowercase_		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,	dtype=np.intaa		)
												SCREAMING_SNAKE_CASE							:					Dict           =							shift_tokens_right(lowercase_					,	1					,	2		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.equal(lowercase_					,	1		).astype(np.floataa		).sum()
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.equal(lowercase_					,	1		).astype(np.floataa		).sum()
												self.assertEqual(shifted.shape					,	input_ids.shape		)
												self.assertEqual(lowercase_					,	n_pad_before - 1		)
												self.assertTrue(np.equal(shifted[:, 0]					,	2		).all()		)






@require_flax
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase			,							UpperCamelCase_):
						UpperCamelCase_  =	True
						UpperCamelCase_  =	(
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase_  =	(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							FlaxBlenderbotSmallModelTester(self		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(lowercase_					,	lowercase_					,	lowercase_		)


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_					,	lowercase_					,	lowercase_		)


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					int           =							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__		):
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._prepare_for_class(lowercase_					,	lowercase_		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							model_class(lowercase_		)

																								@jax.jit
																								def encode_jitted(UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple=None					,	**UpperCamelCase__			:       Any		):
																														return model.encode(input_ids=lowercase_					,	attention_mask=lowercase_		)

																								with self.subTest('''JIT Enabled'''		):
																														SCREAMING_SNAKE_CASE							:					str           =							encode_jitted(**lowercase_		).to_tuple()

																								with self.subTest('''JIT Disabled'''		):
																														with jax.disable_jit():
																																				SCREAMING_SNAKE_CASE							:					Dict           =							encode_jitted(**lowercase_		).to_tuple()

																								self.assertEqual(len(lowercase_		)					,	len(lowercase_		)		)
																								for jitted_output, output in zip(lowercase_					,	lowercase_		):
																														self.assertEqual(jitted_output.shape					,	output.shape		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__		):
																								SCREAMING_SNAKE_CASE							:					Dict           =							model_class(lowercase_		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							model.encode(inputs_dict['''input_ids''']					,	inputs_dict['''attention_mask''']		)

																								SCREAMING_SNAKE_CASE							:					Dict           =							{
																								    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																								    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																								    '''encoder_outputs''': encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(UpperCamelCase__			:       Any					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[str]		):
																														return model.decode(
																														    decoder_input_ids=lowercase_					,	decoder_attention_mask=lowercase_					,	encoder_outputs=lowercase_					,	)

																								with self.subTest('''JIT Enabled'''		):
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							decode_jitted(**lowercase_		).to_tuple()

																								with self.subTest('''JIT Disabled'''		):
																														with jax.disable_jit():
																																				SCREAMING_SNAKE_CASE							:					Optional[Any]           =							decode_jitted(**lowercase_		).to_tuple()

																								self.assertEqual(len(lowercase_		)					,	len(lowercase_		)		)
																								for jitted_output, output in zip(lowercase_					,	lowercase_		):
																														self.assertEqual(jitted_output.shape					,	output.shape		)



						@slow
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''		)
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		SCREAMING_SNAKE_CASE							:					str           =							np.ones((1, 1)		) * model.config.eos_token_id
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(lowercase_		)
																		self.assertIsNotNone(lowercase_		)






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_ta import TaTokenizer
else:
					__UpperCamelCase	: Dict    		=  None


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[int]    		=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase	: Optional[int]    		=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCamelCase	: Union[str, Any]    		=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]
						UpperCamelCase_  =	TaTokenizer

						UpperCamelCase_  =	[]


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Dict=None					,	UpperCamelCase__			:       str="</s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       Optional[int]="<pad>"					,	UpperCamelCase__			:       Optional[Any]=100					,	UpperCamelCase__			:       List[Any]=None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												if extra_ids > 0 and additional_special_tokens is None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[f"""<extra_id_{i}>""" for i in range(UpperCamelCase__		)]
												elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		SCREAMING_SNAKE_CASE							:					int           =							len(set(filter(lambda UpperCamelCase__		: bool('''extra_id_''' in str(UpperCamelCase__		)		)					,	UpperCamelCase__		)		)		)
																		if extra_tokens != extra_ids:
																								raise ValueError(
																								    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																								    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																								    ''' tokens'''		)

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

												SCREAMING_SNAKE_CASE							:					str           =							vocab_file
												SCREAMING_SNAKE_CASE							:					int           =							False if not self.vocab_file else True
												SCREAMING_SNAKE_CASE							:					str           =							extra_ids


						@staticmethod
						def  __A ( UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																								return init_max_model_length
																		elif init_max_model_length is None:
																								warnings.warn(
																								    '''This tokenizer was incorrectly instantiated with a model max length of'''
																								    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																								    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																								    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																								    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																								    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																								    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																								    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																								    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''					,	UpperCamelCase__					,	)

												return max_model_length


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





												if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''		)

												if not os.path.isdir(UpperCamelCase__		):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																		return
												SCREAMING_SNAKE_CASE							:					Any           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
																		logger.info(f"""Copy vocab file to {out_vocab_file}"""		)

												return (out_vocab_file,)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
												else:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[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  __A ( self			:       Dict		):





												'''simple docstring'''





												return list(
												    set(filter(lambda UpperCamelCase__		: bool(re.search(r'''<extra_id_\d+>'''					,	UpperCamelCase__		)		) is not None					,	self.additional_special_tokens		)		)		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return [self.convert_tokens_to_ids(UpperCamelCase__		) for token in self.get_sentinel_tokens()]





