



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
				__A       ="platform"

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 			a		( _UpperCAmelCase				:   Any					,  _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   int=None					,  _UpperCAmelCase				:   str=None		):







		'''simple docstring'''





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

@require_flax
class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					Optional[int]			,			a_				:					Union[str, Any]			,			a_				:					Optional[Any]=13			,			a_				:					Any=7			,			a_				:					str=True			,			a_				:					int=False			,			a_				:					Tuple=99			,			a_				:					Optional[int]=16			,			a_				:					Union[str, Any]=2			,			a_				:					str=4			,			a_				:					List[str]=4			,			a_				:					List[str]="gelu"			,			a_				:					Any=0.1			,			a_				:					str=0.1			,			a_				:					Any=20			,			a_				:					List[Any]=2			,			a_				:					Optional[Any]=1			,			a_				:					Dict=0			,			a_				:					Union[str, Any]=16			,			a_				:					List[str]=16			,			a_				:					Dict=0.0_2			,			):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





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

					__UpperCAmelCase       :	Any						=      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=a_			,			word_embed_proj_dim=self.word_embed_proj_dim			,			initializer_range=self.initializer_range			,			use_cache=a_			,			)
					__UpperCAmelCase       :	int						=      prepare_opt_inputs_dict(a_			,			a_					)
					return config, inputs_dict




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase				,   __UpperCAmelCase       :	Optional[Any]						=      self.prepare_config_and_inputs()
					return config, inputs_dict




			def 					snake_case__	(  self				:					Optional[int]			,			a_				:					str			,			a_				:					Optional[Any]			,			a_				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      20
					__UpperCAmelCase       :	Dict						=      model_class_name(a_					)

					__UpperCAmelCase       :	Any						=      inputs_dict['''input_ids''']
					__UpperCAmelCase       :	Tuple						=      inputs_dict['''attention_mask''']

					__UpperCAmelCase       :	Optional[int]						=      model.init_cache(input_ids.shape[0]			,			a_					)
					__UpperCAmelCase       :	Union[str, Any]						=      jnp.ones((input_ids.shape[0], max_length)			,			dtype='''i4'''					)

					__UpperCAmelCase       :	str						=      jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)			,			)
					__UpperCAmelCase       :	Optional[Any]						=      model(
					    input_ids[:, :-1]			,			attention_mask=a_			,			past_key_values=a_			,			position_ids=a_			,			)

					__UpperCAmelCase       :	Any						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype='''i4'''					)
					__UpperCAmelCase       :	Tuple						=      model(
					    input_ids[:, -1:]			,			attention_mask=a_			,			past_key_values=outputs_cache.past_key_values			,			position_ids=a_			,			)

					__UpperCAmelCase       :	Tuple						=      model(a_					)

					__UpperCAmelCase       :	List[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 					snake_case__	(  self				:					int			,			a_				:					List[Any]			,			a_				:					Union[str, Any]			,			a_				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Dict						=      20
					__UpperCAmelCase       :	Tuple						=      model_class_name(a_					)

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

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

					__UpperCAmelCase       :	List[str]						=      model.init_cache(input_ids.shape[0]			,			a_					)
					__UpperCAmelCase       :	Any						=      jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)			,			)

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

					__UpperCAmelCase       :	List[str]						=      model(a_			,			attention_mask=a_					)

					__UpperCAmelCase       :	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 						UpperCAmelCase__     (     __UpperCamelCase		,unittest.TestCase		,__UpperCamelCase  ):




			'''simple docstring'''






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




			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      FlaxOPTModelTester(self					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(a_			,			a_			,			a_					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase				,   __UpperCAmelCase       :	Tuple						=      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(a_			,			a_			,			a_					)




			@slow
			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					for model_class_name in self.all_model_classes:
							__UpperCAmelCase       :	Any						=      model_class_name.from_pretrained('''facebook/opt-125m'''					)
							__UpperCAmelCase       :	int						=      np.ones((1, 1)					) * model.config.eos_token_id
							__UpperCAmelCase       :	str						=      model(a_					)
							self.assertIsNotNone(a_					)

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




			'''simple docstring'''






			@slow
			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
					__UpperCAmelCase       :	Tuple						=      jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
					__UpperCAmelCase       :	Tuple						=      model(input_ids=a_					).last_hidden_state
					__UpperCAmelCase       :	Tuple						=      (1, 11, 5_12)
					self.assertEqual(output.shape			,			a_					)
					__UpperCAmelCase       :	Optional[int]						=      jnp.array(
					    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]					)
					self.assertTrue(jnp.allclose(output[:, :3, :3]			,			a_			,			atol=4e-2					)					)

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




			'''simple docstring'''






			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					super().setUp()
					__UpperCAmelCase       :	Dict						=      '''facebook/opt-350m'''




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      FlaxOPTForCausalLM.from_pretrained(self.path_model					)
					__UpperCAmelCase       :	Any						=      GPTaTokenizer.from_pretrained(self.path_model					)

					__UpperCAmelCase       :	str						=      [
					    '''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
					__UpperCAmelCase       :	Tuple						=      tokenizer(a_			,			return_tensors='''jax'''			,			padding=a_			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	List[str]						=      model(inputs.input_ids			,			attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
					__UpperCAmelCase       :	Optional[Any]						=      jnp.array(
					    [
					        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
					        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
					        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
					        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
					    ]					)
					self.assertTrue(jnp.allclose(a_			,			a_			,			atol=4e-2					)					)

					__UpperCAmelCase       :	Optional[Any]						=      jax.jit(a_					)
					__UpperCAmelCase       :	int						=      model(inputs.input_ids			,			attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
					self.assertTrue(jnp.allclose(a_			,			a_			,			atol=4e-2					)					)

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




			'''simple docstring'''






			@property
			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''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 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      '''facebook/opt-125m'''

					__UpperCAmelCase       :	int						=      [
					    '''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''',
					]

					__UpperCAmelCase       :	Union[str, Any]						=      []

					__UpperCAmelCase       :	int						=      FlaxOPTForCausalLM.from_pretrained(a_					)
					__UpperCAmelCase       :	str						=      GPTaTokenizer.from_pretrained(a_					)

					for prompt in self.prompts:
							__UpperCAmelCase       :	Optional[Any]						=      tokenizer(a_			,			return_tensors='''jax'''					).input_ids

							__UpperCAmelCase       :	List[str]						=      model.generate(a_			,			max_length=10					)
							__UpperCAmelCase       :	List[Any]						=      generated_ids[0]

							__UpperCAmelCase       :	int						=      tokenizer.batch_decode(a_			,			skip_special_tokens=a_					)
							predicted_outputs += generated_string

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      '''facebook/opt-350m'''

					__UpperCAmelCase       :	Union[str, Any]						=      [
					    '''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''',
					]

					__UpperCAmelCase       :	str						=      []
					__UpperCAmelCase       :	str						=      FlaxOPTForCausalLM.from_pretrained(a_					)
					__UpperCAmelCase       :	str						=      GPTaTokenizer.from_pretrained(a_					)

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

							__UpperCAmelCase       :	Union[str, Any]						=      model.generate(a_			,			max_length=10					)
							__UpperCAmelCase       :	Tuple						=      generated_ids[0]

							__UpperCAmelCase       :	Tuple						=      tokenizer.batch_decode(a_			,			skip_special_tokens=a_					)
							predicted_outputs += generated_string

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	Dict						=      '''facebook/opt-125m'''
					__UpperCAmelCase       :	Any						=      [
					    '''Today is a beautiful day and I want to thank''',
					    '''In the city of Rome Canaver Canaver Canaver Canaver''',
					]
					__UpperCAmelCase       :	Dict						=      FlaxOPTForCausalLM.from_pretrained(a_					)
					__UpperCAmelCase       :	List[Any]						=      GPTaTokenizer.from_pretrained(a_					)
					__UpperCAmelCase       :	List[Any]						=      tokenizer(
					    [
					        '''Today is a beautiful day and I want to''',
					        '''In the city of''',
					    ]			,			return_tensors='''jax'''			,			padding=a_			,			)

					__UpperCAmelCase       :	Optional[Any]						=      jax.jit(model.generate					)

					__UpperCAmelCase       :	Tuple						=      jit_generate(inputs['''input_ids''']			,			attention_mask=inputs['''attention_mask''']					).sequences

					__UpperCAmelCase       :	List[Any]						=      tokenizer.batch_decode(a_			,			skip_special_tokens=a_					)

					self.assertIsNotNone(a_			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      '''facebook/opt-350m'''

					__UpperCAmelCase       :	List[Any]						=      GPTaTokenizer.from_pretrained(a_					)
					__UpperCAmelCase       :	Dict						=      FlaxOPTForCausalLM.from_pretrained(a_					)

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

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

					__UpperCAmelCase       :	List[str]						=      tokenizer(a_			,			return_tensors='''jax'''			,			padding=a_					)
					__UpperCAmelCase       :	Optional[int]						=      inputs['''input_ids''']

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

					__UpperCAmelCase       :	List[str]						=      tokenizer(sentences[0]			,			return_tensors='''jax'''					).input_ids
					__UpperCAmelCase       :	List[str]						=      model.generate(input_ids=a_					)

					__UpperCAmelCase       :	List[Any]						=      inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer(sentences[1]			,			return_tensors='''jax'''					).input_ids
					__UpperCAmelCase       :	Union[str, Any]						=      model.generate(input_ids=a_			,			max_length=model.config.max_length - num_paddings					)

					__UpperCAmelCase       :	str						=      tokenizer.batch_decode(outputs[0]			,			skip_special_tokens=a_					)
					__UpperCAmelCase       :	Dict						=      tokenizer.decode(output_non_padded[0][0]			,			skip_special_tokens=a_					)
					__UpperCAmelCase       :	Optional[Any]						=      tokenizer.decode(output_padded[0][0]			,			skip_special_tokens=a_					)

					__UpperCAmelCase       :	List[str]						=      [
					    '''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(a_			,			a_					)
					self.assertListEqual(a_			,			[non_padded_sentence, padded_sentence]					)










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__)





