


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class      __lowercase  (__SCREAMING_SNAKE_CASE     ,    unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		PreTrainedTokenizerFast
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		True
							_UpperCAmelCase     =		"""tokenizer_file"""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= False  # because we don't have pretrained_vocab_files_map
										super().setUp()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= True

										SCREAMING_SNAKE_CASE_						:			List[str]       						= ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 'SaulLu/dummy-tokenizer-bytelevel-bpe'

										# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

										SCREAMING_SNAKE_CASE_						:			Dict       						= PreTrainedTokenizerFast.from_pretrained(model_paths[0]    )
										tokenizer.save_pretrained(self.tmpdirname    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																try:
																			SCREAMING_SNAKE_CASE_						:			List[str]       						= tempfile.mkdtemp()
																			SCREAMING_SNAKE_CASE_						:			str       						= self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__       ,		**lowerCAmelCase__    )

																			tokenizer.save_pretrained(self.tmpdirname    )
																			super().test_training_new_tokenizer()
																finally:
																			# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																			# is restored
																			shutil.rmtree(self.tmpdirname    )
																			SCREAMING_SNAKE_CASE_						:			Dict       						= tmpdirname_orig






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.tmpdirname
										# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																try:
																			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= tempfile.mkdtemp()
																			SCREAMING_SNAKE_CASE_						:			Dict       						= self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__       ,		**lowerCAmelCase__    )

																			tokenizer.save_pretrained(self.tmpdirname    )
																			super().test_training_new_tokenizer_with_special_tokens_change()
																finally:
																			# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																			# is restored
																			shutil.rmtree(self.tmpdirname    )
																			SCREAMING_SNAKE_CASE_						:			Any       						= tmpdirname_orig






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= ('a' for _ in range(1_0_0_0    ))
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase__       ,		length=1_0_0_0       ,		vocab_size=5_0    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= new_tokenizer.encode('a🤗'    )
										self.assertEqual(lowerCAmelCase__       ,		[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]    )







@require_tokenizers
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= AutoTokenizer.from_pretrained('bert-base-cased'    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= json.loads(tokenizer._tokenizer.to_str()    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= len(lowerCAmelCase__    )

										with tempfile.TemporaryDirectory() as tmp_dir:
													# Hack to save this in the tokenizer_config.json
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= ['tokenizer.4.0.0.json']
													tokenizer.save_pretrained(lowerCAmelCase__    )
													json.dump(lowerCAmelCase__       ,		open(os.path.join(lowerCAmelCase__       ,		'tokenizer.4.0.0.json'    )       ,		'w'    )    )

													# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
													SCREAMING_SNAKE_CASE_						:			str       						= AutoTokenizer.from_pretrained(lowerCAmelCase__    )
													self.assertEqual(len(lowerCAmelCase__    )       ,		len(lowerCAmelCase__    ) + 1    )
													SCREAMING_SNAKE_CASE_						:			str       						= json.loads(new_tokenizer._tokenizer.to_str()    )
													self.assertIn('huggingface'       ,		json_tokenizer['model']['vocab']    )

													# Will need to be adjusted if we reach v42 and this test is still here.
													# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
													shutil.move(os.path.join(lowerCAmelCase__       ,		'tokenizer.4.0.0.json'    )       ,		os.path.join(lowerCAmelCase__       ,		'tokenizer.42.0.0.json'    )    )
													SCREAMING_SNAKE_CASE_						:			int       						= ['tokenizer.42.0.0.json']
													tokenizer.save_pretrained(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			List[str]       						= AutoTokenizer.from_pretrained(lowerCAmelCase__    )
													self.assertEqual(len(lowerCAmelCase__    )       ,		len(lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= json.loads(new_tokenizer._tokenizer.to_str()    )
													self.assertNotIn('huggingface'       ,		json_tokenizer['model']['vocab']    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= 'hf-internal-testing/test-two-tokenizers'

										# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= AutoTokenizer.from_pretrained(lowerCAmelCase__    )
										self.assertEqual(len(lowerCAmelCase__    )       ,		2_8_9_9_7    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= json.loads(tokenizer._tokenizer.to_str()    )
										self.assertIn('huggingface'       ,		json_tokenizer['model']['vocab']    )

										# Testing an older version by monkey-patching the version in the module it's used.
										import transformers as old_transformers

										SCREAMING_SNAKE_CASE_						:			List[str]       						= '3.0.0'
										SCREAMING_SNAKE_CASE_						:			str       						= old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase__    )
										self.assertEqual(len(lowerCAmelCase__    )       ,		2_8_9_9_6    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= json.loads(old_tokenizer._tokenizer.to_str()    )
										self.assertNotIn('huggingface'       ,		json_tokenizer['model']['vocab']    )







@require_tokenizers
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 'The Matrix is a 1999 science fiction action film.'

										with concurrent.futures.ThreadPoolExecutor() as executor:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= [executor.submit(self.fetch       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ) for i in range(1_0    )]
													SCREAMING_SNAKE_CASE_						:			Dict       						= [future.result() for future in futures]
													self.assertEqual(lowerCAmelCase__       ,		[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0    )]    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										return tokenizer.encode(lowerCAmelCase__       ,		truncation='longest_first'       ,		padding='longest'    )









import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
				lowerCAmelCase__       :	Dict     				='platform'

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 		a__       (							A__,	A__,	A__=None,	A__=None							):
			if attention_mask is None:
						SCREAMING_SNAKE_CASE_						:			Tuple       						= np.where(input_ids != config.pad_token_id,	1,	0							)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}







@require_flax
class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=7       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=9_9       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=2_0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=0.02       ,		):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= 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_						:			List[str]       						= OPTConfig(
										    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		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       ,		embed_dim=self.embed_dim       ,		is_encoder_decoder=lowerCAmelCase__       ,		word_embed_proj_dim=self.word_embed_proj_dim       ,		initializer_range=self.initializer_range       ,		use_cache=lowerCAmelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			Dict       						= prepare_opt_inputs_dict(lowerCAmelCase__       ,		lowerCAmelCase__    )
										return config, inputs_dict






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= self.prepare_config_and_inputs()
										return config, inputs_dict






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model_class_name(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= inputs_dict['input_ids']
										SCREAMING_SNAKE_CASE_						:			Dict       						= inputs_dict['attention_mask']

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.init_cache(input_ids.shape[0]       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= jnp.ones((input_ids.shape[0], max_length)       ,		dtype='i4'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(
										    input_ids[:, :-1]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(
										    input_ids[:, -1:]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=outputs_cache.past_key_values       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model_class_name(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= (
										    inputs_dict['input_ids'],
										    inputs_dict['attention_mask'],
										)

										SCREAMING_SNAKE_CASE_						:			Any       						= jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
										    ]       ,		axis=-1       ,		)

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model.init_cache(input_ids.shape[0]       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(
										    input_ids[:, :-1]       ,		attention_mask=lowerCAmelCase__       ,		past_key_values=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(
										    input_ids[:, -1:]       ,		past_key_values=outputs_cache.past_key_values       ,		attention_mask=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Dict       						= model(lowerCAmelCase__       ,		attention_mask=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Any       						= 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  (__SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ,    __SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							_UpperCAmelCase     =		() if is_flax_available() else ()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= FlaxOPTModelTester(self    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Any       						= self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Dict       						= 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(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			str       						= model_class_name.from_pretrained('facebook/opt-125m'    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= np.ones((1, 1)    ) * model.config.eos_token_id
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(lowerCAmelCase__    )
													self.assertIsNotNone(lowerCAmelCase__    )







@require_sentencepiece
@require_flax
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(input_ids=lowerCAmelCase__    ).last_hidden_state
										SCREAMING_SNAKE_CASE_						:			int       						= (1, 1_1, 5_1_2)
										self.assertEqual(output.shape       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= jnp.array(
										    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
										self.assertTrue(jnp.allclose(output[:, :3, :3]       ,		lowerCAmelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										super().setUp()
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 'facebook/opt-350m'






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= FlaxOPTForCausalLM.from_pretrained(self.path_model    )
										SCREAMING_SNAKE_CASE_						:			int       						= GPTaTokenizer.from_pretrained(self.path_model    )

										SCREAMING_SNAKE_CASE_						:			int       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of',
										    'Paris is the capital of France and',
										    'Computers and mobile phones have taken',
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__       ,		add_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										SCREAMING_SNAKE_CASE_						:			Any       						= jnp.array(
										    [
										        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
										        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
										        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
										        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
										    ]    )
										self.assertTrue(jnp.allclose(lowerCAmelCase__       ,		lowerCAmelCase__       ,		atol=4E-2    )    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= jax.jit(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										self.assertTrue(jnp.allclose(lowerCAmelCase__       ,		lowerCAmelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 'facebook/opt-125m'

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of New York, the city',
										    'Paris is the capital of France and the capital',
										    'Computers and mobile phones have taken over the',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= []

										SCREAMING_SNAKE_CASE_						:			int       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(lowerCAmelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			int       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			int       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= 'facebook/opt-350m'

										SCREAMING_SNAKE_CASE_						:			List[str]       						= [
										    'Today is a beautiful day and I want to',
										    'In the city of San Francisco, the city',
										    'Paris is the capital of France and the capital',
										    'Computers and mobile phones have taken over the',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= []
										SCREAMING_SNAKE_CASE_						:			List[str]       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(lowerCAmelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= 'facebook/opt-125m'
										SCREAMING_SNAKE_CASE_						:			str       						= [
										    'Today is a beautiful day and I want to thank',
										    'In the city of Rome Canaver Canaver Canaver Canaver',
										]
										SCREAMING_SNAKE_CASE_						:			Any       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= tokenizer(
										    [
										        'Today is a beautiful day and I want to',
										        'In the city of',
										    ]       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			List[str]       						= jax.jit(model.generate    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= jit_generate(inputs['input_ids']       ,		attention_mask=inputs['attention_mask']    ).sequences

										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer.batch_decode(lowerCAmelCase__       ,		skip_special_tokens=lowerCAmelCase__    )

										self.assertIsNotNone(lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= 'facebook/opt-350m'

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 'left'

										# use different length sentences to test batching
										SCREAMING_SNAKE_CASE_						:			Dict       						= [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= tokenizer(lowerCAmelCase__       ,		return_tensors='jax'       ,		padding=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= inputs['input_ids']

										SCREAMING_SNAKE_CASE_						:			str       						= model.generate(input_ids=lowerCAmelCase__       ,		attention_mask=inputs['attention_mask']       ,		trace=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= tokenizer(sentences[0]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model.generate(input_ids=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer(sentences[1]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model.generate(input_ids=lowerCAmelCase__       ,		max_length=model.config.max_length - num_paddings    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= tokenizer.batch_decode(outputs[0]       ,		skip_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= tokenizer.decode(output_non_padded[0][0]       ,		skip_special_tokens=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= tokenizer.decode(output_padded[0][0]       ,		skip_special_tokens=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= [
										    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
										    'Today, I was in the middle of a conversation with a friend about the',
										]
										self.assertListEqual(lowerCAmelCase__       ,		lowerCAmelCase__    )
										self.assertListEqual(lowerCAmelCase__       ,		[non_padded_sentence, padded_sentence]    )





