


"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.gptj.modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    shape_list,
						)
class 				lowerCamelCase							:


							"""simple docstring"""






							def __init__(    self			:  int		,			lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     parent
													UpperCAmelCase__												=     13
													UpperCAmelCase__												=     7
													UpperCAmelCase__												=     True
													UpperCAmelCase__												=     True
													UpperCAmelCase__												=     True
													UpperCAmelCase__												=     True
													UpperCAmelCase__												=     True
													UpperCAmelCase__												=     99
													UpperCAmelCase__												=     32
													UpperCAmelCase__												=     4
													UpperCAmelCase__												=     2
													UpperCAmelCase__												=     4
													UpperCAmelCase__												=     37
													UpperCAmelCase__												=     '''gelu'''
													UpperCAmelCase__												=     0.1
													UpperCAmelCase__												=     0.1
													UpperCAmelCase__												=     512
													UpperCAmelCase__												=     16
													UpperCAmelCase__												=     2
													UpperCAmelCase__												=     0.02
													UpperCAmelCase__												=     3
													UpperCAmelCase__												=     4
													UpperCAmelCase__												=     None
													UpperCAmelCase__												=     self.vocab_size - 1
													UpperCAmelCase__												=     self.vocab_size - 1
													UpperCAmelCase__												=     self.vocab_size - 1






							def        lowerCAmelCase  (    self			:  List[str]					):
													UpperCAmelCase__												=     ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size					)

													UpperCAmelCase__												=     None
													if self.use_input_mask:
																			UpperCAmelCase__												=     random_attention_mask([self.batch_size, self.seq_length]					)

													UpperCAmelCase__												=     None
													if self.use_token_type_ids:
																			UpperCAmelCase__												=     ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size					)

													UpperCAmelCase__												=     None
													if self.use_mc_token_ids:
																			UpperCAmelCase__												=     ids_tensor([self.batch_size, self.num_choices]		,			self.seq_length					)

													UpperCAmelCase__												=     None
													UpperCAmelCase__												=     None
													UpperCAmelCase__												=     None
													if self.use_labels:
																			UpperCAmelCase__												=     ids_tensor([self.batch_size]		,			self.type_sequence_label_size					)
																			UpperCAmelCase__												=     ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels					)
																			UpperCAmelCase__												=     ids_tensor([self.batch_size]		,			self.num_choices					)

													UpperCAmelCase__												=     GPTJConfig(
													    vocab_size=self.vocab_size		,			n_embd=self.hidden_size		,			n_layer=self.num_hidden_layers		,			n_head=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			n_positions=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			initializer_range=self.initializer_range		,			bos_token_id=self.bos_token_id		,			eos_token_id=self.eos_token_id		,			pad_token_id=self.pad_token_id		,			rotary_dim=self.rotary_dim		,			return_dict=lowerCamelCase_		,			)

													UpperCAmelCase__												=     ids_tensor([self.num_hidden_layers, self.num_attention_heads]		,			2					)

													return (
													    config,
													    input_ids,
													    input_mask,
													    head_mask,
													    token_type_ids,
													    mc_token_ids,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Tuple		,			*lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     TFGPTJModel(config=lowerCamelCase_					)
													UpperCAmelCase__												=     {
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}
													UpperCAmelCase__												=     model(lowerCamelCase_					)

													UpperCAmelCase__												=     [input_ids, None, input_mask]  # None is the input for 'past'
													UpperCAmelCase__												=     model(lowerCamelCase_					)

													UpperCAmelCase__												=     model(lowerCamelCase_					)

													self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)					)






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  List[Any]		,			lowerCamelCase_			:  List[Any]		,			lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  str		,			*lowerCamelCase_			:  Optional[int]					):
													UpperCAmelCase__												=     TFGPTJModel(config=lowerCamelCase_					)

													# first forward pass
													UpperCAmelCase__												=     model(lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			use_cache=lowerCamelCase_					)
													UpperCAmelCase__												=     model(lowerCamelCase_		,			token_type_ids=lowerCamelCase_					)
													UpperCAmelCase__												=     model(lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			use_cache=lowerCamelCase_					)

													self.parent.assertTrue(len(lowerCamelCase_					) == len(lowerCamelCase_					)					)
													self.parent.assertTrue(len(lowerCamelCase_					) == len(lowerCamelCase_					) + 1					)

													UpperCAmelCase__						,					UpperCAmelCase__												=     outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													UpperCAmelCase__												=     ids_tensor((self.batch_size, 1)		,			config.vocab_size					)
													UpperCAmelCase__												=     ids_tensor([self.batch_size, 1]		,			self.type_vocab_size					)

													# append to next input_ids and token_type_ids
													UpperCAmelCase__												=     tf.concat([input_ids, next_tokens]		,			axis=-1					)
													UpperCAmelCase__												=     tf.concat([token_type_ids, next_token_types]		,			axis=-1					)

													UpperCAmelCase__												=     model(lowerCamelCase_		,			token_type_ids=lowerCamelCase_					)['''last_hidden_state''']
													UpperCAmelCase__												=     model(lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			past_key_values=lowerCamelCase_					)[
													    '''last_hidden_state'''
													]

													# select random slice
													UpperCAmelCase__												=     int(ids_tensor((1,)		,			shape_list(lowerCamelCase_					)[-1]					)					)
													UpperCAmelCase__												=     output_from_no_past[:, -1, random_slice_idx]
													UpperCAmelCase__												=     output_from_past[:, 0, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(lowerCamelCase_		,			lowerCamelCase_		,			rtol=1E-6					)






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  Optional[Any]		,			lowerCamelCase_			:  List[Any]		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  int		,			*lowerCamelCase_			:  List[Any]					):
													UpperCAmelCase__												=     TFGPTJModel(config=lowerCamelCase_					)

													# create attention mask
													UpperCAmelCase__												=     self.seq_length // 2
													UpperCAmelCase__												=     tf.ones((self.batch_size, half_seq_length)		,			dtype=tf.intaa					)
													UpperCAmelCase__												=     tf.zeros((self.batch_size, self.seq_length - half_seq_length)		,			dtype=tf.intaa					)
													UpperCAmelCase__												=     tf.concat([attn_mask_begin, attn_mask_end]		,			axis=1					)

													# first forward pass
													UpperCAmelCase__						,					UpperCAmelCase__												=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_					).to_tuple()

													# create hypothetical next token and extent to next_input_ids
													UpperCAmelCase__												=     ids_tensor((self.batch_size, 1)		,			config.vocab_size					)

													# change a random masked slice from input_ids
													UpperCAmelCase__												=     ids_tensor((1,)		,			lowerCamelCase_					).numpy() + 1
													UpperCAmelCase__												=     ids_tensor((self.batch_size, self.seq_length)		,			config.vocab_size					)
													UpperCAmelCase__												=     tf.range(self.seq_length					) == (self.seq_length - random_seq_idx_to_change)
													UpperCAmelCase__												=     tf.transpose(
													    tf.broadcast_to(tf.expand_dims(lowerCamelCase_		,			-1					)		,			(self.seq_length, self.batch_size)					)					)
													UpperCAmelCase__												=     tf.where(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													# append to next input_ids and attn_mask
													UpperCAmelCase__												=     tf.concat([input_ids, next_tokens]		,			axis=-1					)
													UpperCAmelCase__												=     tf.concat([attn_mask, tf.ones((shape_list(lowerCamelCase_					)[0], 1)		,			dtype=tf.intaa					)]		,			axis=1					)

													# get two different outputs
													UpperCAmelCase__												=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_					)['''last_hidden_state''']
													UpperCAmelCase__												=     model(lowerCamelCase_		,			past_key_values=lowerCamelCase_		,			attention_mask=lowerCamelCase_					)[
													    '''last_hidden_state'''
													]

													# select random slice
													UpperCAmelCase__												=     int(ids_tensor((1,)		,			shape_list(lowerCamelCase_					)[-1]					)					)
													UpperCAmelCase__												=     output_from_no_past[:, -1, random_slice_idx]
													UpperCAmelCase__												=     output_from_past[:, 0, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(lowerCamelCase_		,			lowerCamelCase_		,			rtol=1E-12					)






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Optional[Any]		,			lowerCamelCase_			:  List[Any]		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]		,			*lowerCamelCase_			:  int					):
													UpperCAmelCase__												=     TFGPTJModel(config=lowerCamelCase_					)

													UpperCAmelCase__												=     input_ids[:1, :]
													UpperCAmelCase__												=     input_mask[:1, :]
													UpperCAmelCase__												=     token_type_ids[:1, :]
													UpperCAmelCase__												=     1

													# first forward pass
													UpperCAmelCase__												=     model(lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			use_cache=lowerCamelCase_					)

													UpperCAmelCase__						,					UpperCAmelCase__												=     outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													UpperCAmelCase__												=     ids_tensor((self.batch_size, 3)		,			config.vocab_size					)
													UpperCAmelCase__												=     ids_tensor((self.batch_size, 3)		,			2					)
													UpperCAmelCase__												=     ids_tensor((self.batch_size, 3)		,			self.type_vocab_size					)

													# append to next input_ids and token_type_ids
													UpperCAmelCase__												=     tf.concat([input_ids, next_tokens]		,			axis=-1					)
													UpperCAmelCase__												=     tf.concat([input_mask, next_attn_mask]		,			axis=-1					)
													UpperCAmelCase__												=     tf.concat([token_type_ids, next_token_types]		,			axis=-1					)

													UpperCAmelCase__												=     model(
													    lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			attention_mask=lowerCamelCase_					)['''last_hidden_state''']
													UpperCAmelCase__												=     model(
													    lowerCamelCase_		,			token_type_ids=lowerCamelCase_		,			attention_mask=lowerCamelCase_		,			past_key_values=lowerCamelCase_		,			)['''last_hidden_state''']
													self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]					)

													# select random slice
													UpperCAmelCase__												=     int(ids_tensor((1,)		,			shape_list(lowerCamelCase_					)[-1]					)					)
													UpperCAmelCase__												=     output_from_no_past[:, -3:, random_slice_idx]
													UpperCAmelCase__												=     output_from_past[:, :, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(lowerCamelCase_		,			lowerCamelCase_		,			rtol=1E-3					)






							def        lowerCAmelCase  (    self			:  str		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[int]		,			*lowerCamelCase_			:  Optional[int]					):
													UpperCAmelCase__												=     TFGPTJForCausalLM(config=lowerCamelCase_					)
													UpperCAmelCase__												=     {
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}
													UpperCAmelCase__												=     model(lowerCamelCase_					)
													self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)					)






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__												=     self.prepare_config_and_inputs()

													(
													    (
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					(
													    UpperCAmelCase__
													)						,					
													)												=     config_and_inputs

													UpperCAmelCase__												=     {
													    '''input_ids''': input_ids,
													    '''token_type_ids''': token_type_ids,
													    '''attention_mask''': input_mask,
													}
													return config, inputs_dict




@require_tf
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   (
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							SCREAMING_SNAKE_CASE              =   (TFGPTJForCausalLM,) if is_tf_available() else ()
							SCREAMING_SNAKE_CASE              =   (
							    {
							        """feature-extraction""": TFGPTJModel,
							        """question-answering""": TFGPTJForQuestionAnswering,
							        """text-classification""": TFGPTJForSequenceClassification,
							        """text-generation""": TFGPTJForCausalLM,
							        """zero-shot""": TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							SCREAMING_SNAKE_CASE              =   False
							SCREAMING_SNAKE_CASE              =   False
							SCREAMING_SNAKE_CASE              =   False
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  Tuple					):
													if (
													    pipeline_test_casse_name == "QAPipelineTests"
													    and tokenizer_name is not None
													    and not tokenizer_name.endswith('''Fast'''					)
													):
																			# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																			# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																			# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																			return True

													return False






							def        lowerCAmelCase  (    self			:  Tuple					):
													UpperCAmelCase__												=     TFGPTJModelTester(self					)
													UpperCAmelCase__												=     ConfigTester(self		,			config_class=lowerCamelCase_		,			n_embd=37					)






							def        lowerCAmelCase  (    self			:  List[str]					):
													self.config_tester.run_common_tests()






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_gptj_model(*lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_gptj_model_past(*lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_gptj_lm_head_model(*lowerCamelCase_					)






							@slow
							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''					)					) > 0		,			'''skip testing on GPU for now to avoid GPU OOM.'''		,			)
							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B'''		,			from_pt=lowerCamelCase_					)
													self.assertIsNotNone(lowerCamelCase_					)






							@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''					)
							def        lowerCAmelCase  (    self			:  List[str]					):
													super().test_resize_token_embeddings()




@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 				lowerCamelCase							(      unittest.TestCase			):


							"""simple docstring"""






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													UpperCAmelCase__												=     TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''		,			from_pt=lowerCamelCase_					)
													UpperCAmelCase__												=     tf.convert_to_tensor([[464, 3290]]		,			dtype=tf.intaa					)  # The dog
													# fmt: off
													# The dog is a man's best friend. It is a loyal companion, and it is a friend
													UpperCAmelCase__												=     [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
													# fmt: on
													UpperCAmelCase__												=     model.generate(lowerCamelCase_		,			do_sample=lowerCamelCase_					)
													self.assertListEqual(output_ids[0].numpy().tolist()		,			lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[int]					):
													UpperCAmelCase__												=     AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''		,			revision='''float16'''					)
													UpperCAmelCase__												=     TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''		,			revision='''float16'''		,			from_pt=lowerCamelCase_					)

													UpperCAmelCase__												=     tokenizer('''Today is a nice day and'''		,			return_tensors='''tf'''					)
													# forces the generation to happen on CPU, to avoid GPU-related quirks
													with tf.device(''':/CPU:0'''					):
																			UpperCAmelCase__												=     model.generate(**lowerCamelCase_		,			do_sample=lowerCamelCase_		,			seed=[42, 0]					)
													UpperCAmelCase__												=     tokenizer.decode(output_ids[0]		,			skip_special_tokens=lowerCamelCase_					)

													UpperCAmelCase__												=     '''Today is a nice day and I’m going to go for a walk. I’'''
													self.assertEqual(lowerCamelCase_		,			lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''		,			revision='''float16'''		,			from_pt=lowerCamelCase_					)
													UpperCAmelCase__												=     AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''		,			revision='''float16'''					)

													UpperCAmelCase__												=     '''left'''

													# Define PAD Token = EOS Token = 50256
													UpperCAmelCase__												=     tokenizer.eos_token
													UpperCAmelCase__												=     model.config.eos_token_id

													# use different length sentences to test batching
													UpperCAmelCase__												=     [
													    '''Hello, my dog is a little''',
													    '''Today, I''',
													]
													UpperCAmelCase__												=     [
													    '''Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia''',
													    '''Today, I’m going to be talking about a topic that’''',
													]
													return model, tokenizer, sentences, expected_output_sentences






							def        lowerCAmelCase  (    self			:  Tuple					):
													# Confirms that we get the expected results with left-padded beam search
													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     self._get_beam_search_test_objects()

													UpperCAmelCase__												=     tokenizer(lowerCamelCase_		,			return_tensors='''tf'''		,			padding=lowerCamelCase_					)
													UpperCAmelCase__												=     model.generate(**lowerCamelCase_		,			do_sample=lowerCamelCase_		,			num_beams=2					)
													UpperCAmelCase__												=     tokenizer.batch_decode(lowerCamelCase_		,			skip_special_tokens=lowerCamelCase_					)
													self.assertListEqual(lowerCamelCase_		,			lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  List[Any]					):
													# Confirms that left-padding is working properly
													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     self._get_beam_search_test_objects()

													UpperCAmelCase__												=     tokenizer(lowerCamelCase_		,			return_tensors='''tf'''		,			padding=lowerCamelCase_					)
													UpperCAmelCase__												=     tokenizer(sentences[0]		,			return_tensors='''tf'''					)
													UpperCAmelCase__												=     model.generate(**lowerCamelCase_		,			do_sample=lowerCamelCase_		,			num_beams=2					)
													UpperCAmelCase__												=     (
													    shape_list(inputs_non_padded['''input_ids''']					)[-1]
													    - tf.reduce_sum(tf.cast(inputs['''attention_mask'''][-1]		,			tf.intaa					)					).numpy()
													)
													UpperCAmelCase__												=     tokenizer(sentences[1]		,			return_tensors='''tf'''					)
													UpperCAmelCase__												=     model.generate(
													    **lowerCamelCase_		,			do_sample=lowerCamelCase_		,			num_beams=2		,			max_length=model.config.max_length - num_paddings					)
													UpperCAmelCase__												=     tokenizer.decode(output_non_padded[0]		,			skip_special_tokens=lowerCamelCase_					)
													UpperCAmelCase__												=     tokenizer.decode(output_padded[0]		,			skip_special_tokens=lowerCamelCase_					)
													self.assertListEqual(lowerCamelCase_		,			[non_padded_sentence, padded_sentence]					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													# Confirms that XLA is working properly
													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     self._get_beam_search_test_objects()

													UpperCAmelCase__												=     tokenizer(lowerCamelCase_		,			return_tensors='''tf'''		,			padding=lowerCamelCase_					)
													UpperCAmelCase__												=     tf.function(model.generate		,			jit_compile=lowerCamelCase_					)
													UpperCAmelCase__												=     xla_generate(**lowerCamelCase_		,			do_sample=lowerCamelCase_		,			num_beams=2					)
													UpperCAmelCase__												=     tokenizer.batch_decode(lowerCamelCase_		,			skip_special_tokens=lowerCamelCase_					)
													self.assertListEqual(lowerCamelCase_		,			lowerCamelCase_					)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez import BarthezTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)