



"""simple docstring"""

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_	:						List[str]				=			"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def    __snake_case       (  __A			:	str       ,		__A			:	List[Any]       ,		__A			:	List[str]=None       ,		__A			:	str=None    )    ->					List[str]:






				'''simple docstring'''
				if attention_mask is None:
								SCREAMING_SNAKE_CASE	:       Dict 							=    np.where(input_ids != config.pad_token_id       ,		1       ,		0    )
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				}
@require_flax
class 						lowerCAmelCase__	:


	'''simple docstring'''







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    parent
					SCREAMING_SNAKE_CASE	:       int 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Dict 							=    seq_length
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    is_training
					SCREAMING_SNAKE_CASE	:       List[str] 							=    use_labels
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    vocab_size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    hidden_size
					SCREAMING_SNAKE_CASE	:       str 							=    num_hidden_layers
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    num_attention_heads
					SCREAMING_SNAKE_CASE	:       Any 							=    intermediate_size
					SCREAMING_SNAKE_CASE	:       Any 							=    hidden_act
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    hidden_dropout_prob
					SCREAMING_SNAKE_CASE	:       Optional[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	:       Tuple 							=    bos_token_id
					SCREAMING_SNAKE_CASE	:       int 							=    embed_dim
					SCREAMING_SNAKE_CASE	:       Any 							=    word_embed_proj_dim
					SCREAMING_SNAKE_CASE	:       str 							=    initializer_range
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    False





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Optional[Any]:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       Union[str, 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=lowerCamelCase__    ,					word_embed_proj_dim=self.word_embed_proj_dim    ,					initializer_range=self.initializer_range    ,					use_cache=lowerCamelCase__    ,					)
					SCREAMING_SNAKE_CASE	:       Any 							=    prepare_opt_inputs_dict(lowerCamelCase__    ,					lowerCamelCase__						)
					return config, inputs_dict





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.prepare_config_and_inputs()
					return config, inputs_dict





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    20
					SCREAMING_SNAKE_CASE	:       Any 							=    model_class_name(lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       str 							=    inputs_dict["input_ids"]
					SCREAMING_SNAKE_CASE	:       str 							=    inputs_dict["attention_mask"]

					SCREAMING_SNAKE_CASE	:       List[str] 							=    model.init_cache(input_ids.shape[0]    ,					lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    jnp.ones((input_ids.shape[0], max_length)    ,					dtype='i4'						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,					(input_ids.shape[0], input_ids.shape[-1] - 1)    ,					)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    model(
					    input_ids[:, :-1]    ,					attention_mask=lowerCamelCase__    ,					past_key_values=lowerCamelCase__    ,					position_ids=lowerCamelCase__    ,					)

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

					SCREAMING_SNAKE_CASE	:       str 							=    model(lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       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  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    20
					SCREAMING_SNAKE_CASE	:       Tuple 							=    model_class_name(lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       Any 							=    (
					    inputs_dict["input_ids"],
					    inputs_dict["attention_mask"],
					)

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

					SCREAMING_SNAKE_CASE	:       Tuple 							=    model.init_cache(input_ids.shape[0]    ,					lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       str 							=    jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,					(input_ids.shape[0], input_ids.shape[-1] - 1)    ,					)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    model(
					    input_ids[:, :-1]    ,					attention_mask=lowerCamelCase__    ,					past_key_values=lowerCamelCase__    ,					position_ids=lowerCamelCase__    ,					)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,					dtype='i4'						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    model(
					    input_ids[:, -1:]    ,					past_key_values=outputs_cache.past_key_values    ,					attention_mask=lowerCamelCase__    ,					position_ids=lowerCamelCase__    ,					)

					SCREAMING_SNAKE_CASE	:       int 							=    model(lowerCamelCase__    ,					attention_mask=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}"""						)




@require_flax
class 						lowerCAmelCase__	(  _a       ,       unittest.TestCase       ,       _a   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		int     	=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   () if is_flax_available() else ()





	def  _lowerCAmelCase    (			self							:					Dict						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    FlaxOPTModelTester(self						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  Union[str, Any]:





					"""simple docstring"""


					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(lowerCamelCase__    ,					lowerCamelCase__    ,					lowerCamelCase__						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    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  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  str:





					"""simple docstring"""


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




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


	'''simple docstring'''







	@slow
	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    FlaxOPTModel.from_pretrained('facebook/opt-350m'						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
					SCREAMING_SNAKE_CASE	:       int 							=    model(input_ids=lowerCamelCase__						).last_hidden_state
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (1, 11, 512)
					self.assertEqual(output.shape    ,					lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    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]    ,					lowerCamelCase__    ,					atol=4E-2						)						)




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


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					List[str]						)	->  List[str]:





					"""simple docstring"""


					super().setUp()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    "facebook/opt-350m"







	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  int:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    [
					    "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	:       List[Any] 							=    tokenizer(lowerCamelCase__    ,					return_tensors='jax'    ,					padding=lowerCamelCase__    ,					add_special_tokens=lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    model(inputs.input_ids    ,					attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    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(lowerCamelCase__    ,					lowerCamelCase__    ,					atol=4E-2						)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    jax.jit(lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    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 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	@property
	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Optional[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  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    "facebook/opt-125m"

					SCREAMING_SNAKE_CASE	:       Tuple 							=    [
					    "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	:       Tuple 							=    []

					SCREAMING_SNAKE_CASE	:       int 							=    FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       int 							=    GPTaTokenizer.from_pretrained(lowerCamelCase__						)

					for prompt in self.prompts:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    tokenizer(lowerCamelCase__    ,					return_tensors='jax'						).input_ids

									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    model.generate(lowerCamelCase__    ,					max_length=10						)
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    generated_ids[0]

									SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer.batch_decode(lowerCamelCase__    ,					skip_special_tokens=lowerCamelCase__						)
									predicted_outputs += generated_string

					self.assertListEqual(lowerCamelCase__    ,					lowerCamelCase__						)





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, 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	:       Tuple 							=    []
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       Any 							=    GPTaTokenizer.from_pretrained(lowerCamelCase__						)

					for prompt in self.prompts:
									SCREAMING_SNAKE_CASE	:       Dict 							=    tokenizer(lowerCamelCase__    ,					return_tensors='jax'						).input_ids

									SCREAMING_SNAKE_CASE	:       Dict 							=    model.generate(lowerCamelCase__    ,					max_length=10						)
									SCREAMING_SNAKE_CASE	:       str 							=    generated_ids[0]

									SCREAMING_SNAKE_CASE	:       str 							=    tokenizer.batch_decode(lowerCamelCase__    ,					skip_special_tokens=lowerCamelCase__						)
									predicted_outputs += generated_string

					self.assertListEqual(lowerCamelCase__    ,					lowerCamelCase__						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  Any:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       int 							=    jax.jit(model.generate						)

					SCREAMING_SNAKE_CASE	:       str 							=    jit_generate(inputs['input_ids']    ,					attention_mask=inputs['attention_mask']						).sequences

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tokenizer.batch_decode(lowerCamelCase__    ,					skip_special_tokens=lowerCamelCase__						)

					self.assertIsNotNone(lowerCamelCase__    ,					lowerCamelCase__						)







	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    "facebook/opt-350m"

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    GPTaTokenizer.from_pretrained(lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    "left"

					# use different length sentences to test batching
					SCREAMING_SNAKE_CASE	:       Tuple 							=    [
					    "Hello, my dog is a little",
					    "Today, I",
					]

					SCREAMING_SNAKE_CASE	:       int 							=    tokenizer(lowerCamelCase__    ,					return_tensors='jax'    ,					padding=lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    inputs["input_ids"]

					SCREAMING_SNAKE_CASE	:       Tuple 							=    model.generate(input_ids=lowerCamelCase__    ,					attention_mask=inputs['attention_mask']    ,					trace=lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    tokenizer(sentences[0]    ,					return_tensors='jax'						).input_ids
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    model.generate(input_ids=lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
					SCREAMING_SNAKE_CASE	:       Dict 							=    tokenizer(sentences[1]    ,					return_tensors='jax'						).input_ids
					SCREAMING_SNAKE_CASE	:       int 							=    model.generate(input_ids=lowerCamelCase__    ,					max_length=model.config.max_length - num_paddings						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer.batch_decode(outputs[0]    ,					skip_special_tokens=lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    tokenizer.decode(output_non_padded[0][0]    ,					skip_special_tokens=lowerCamelCase__						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    tokenizer.decode(output_padded[0][0]    ,					skip_special_tokens=lowerCamelCase__						)

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [
					    "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]						)






"""simple docstring"""

import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import FlavaImageProcessor, FlavaProcessor
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
@require_vision
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					int						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
					# fmt: on
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES['vocab_file']						)

					with open(self.vocab_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									fp.write(''.join([x + '\n' for x in vocab_tokens]						)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    'image_mean': FLAVA_IMAGE_MEAN,
					    'image_std': FLAVA_IMAGE_STD,
					    'do_normalize': True,
					    'do_resize': True,
					    'size': 224,
					    'do_center_crop': True,
					    'crop_size': 224,
					    'input_size_patches': 14,
					    'total_mask_patches': 75,
					    'mask_group_max_patches': None,
					    'mask_group_min_patches': 16,
					    'mask_group_min_aspect_ratio': 0.3,
					    'mask_group_max_aspect_ratio': None,
					    'codebook_do_resize': True,
					    'codebook_size': 112,
					    'codebook_do_center_crop': True,
					    'codebook_crop_size': 112,
					    'codebook_do_map_pixels': True,
					    'codebook_do_normalize': True,
					    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
					    'codebook_image_std': FLAVA_CODEBOOK_STD,
					}

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    os.path.join(self.tmpdirname    ,					_SCREAMING_SNAKE_CASE						)
					with open(self.image_processor_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									json.dump(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  str:





					"""simple docstring"""


					return BertTokenizer.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int    ,					**_SCREAMING_SNAKE_CASE							:					Any						)	->  Union[str, Any]:





					"""simple docstring"""


					return BertTokenizerFast.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					**_SCREAMING_SNAKE_CASE							:					Dict						)	->  str:





					"""simple docstring"""


					return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					shutil.rmtree(self.tmpdirname						)





	def  _lowerCAmelCase    (			self							:					int						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    [np.random.randint(255    ,					size=(3, 30, 400)    ,					dtype=np.uinta						)]

					SCREAMING_SNAKE_CASE	:       str 							=    [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,					0    ,					-1						)						) for x in image_inputs]

					return image_inputs





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.get_tokenizer()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_image_processor()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_slow.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname    ,					use_fast=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_fast.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname						)

					self.assertEqual(processor_slow.tokenizer.get_vocab()    ,					tokenizer_slow.get_vocab()						)
					self.assertEqual(processor_fast.tokenizer.get_vocab()    ,					tokenizer_fast.get_vocab()						)
					self.assertEqual(tokenizer_slow.get_vocab()    ,					tokenizer_fast.get_vocab()						)
					self.assertIsInstance(processor_slow.tokenizer    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.tokenizer    ,					_SCREAMING_SNAKE_CASE						)

					self.assertEqual(processor_slow.image_processor.to_json_string()    ,					image_processor.to_json_string()						)
					self.assertEqual(processor_fast.image_processor.to_json_string()    ,					image_processor.to_json_string()						)
					self.assertIsInstance(processor_slow.image_processor    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.image_processor    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    FlavaProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()						)
					processor.save_pretrained(self.tmpdirname						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_tokenizer(bos_token='(BOS)'    ,					eos_token='(EOS)'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    FlavaProcessor.from_pretrained(
					    self.tmpdirname    ,					bos_token='(BOS)'    ,					eos_token='(EOS)'    ,					do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					self.assertEqual(processor.tokenizer.get_vocab()    ,					tokenizer_add_kwargs.get_vocab()						)
					self.assertIsInstance(processor.tokenizer    ,					_SCREAMING_SNAKE_CASE						)

					self.assertEqual(processor.image_processor.to_json_string()    ,					image_processor_add_kwargs.to_json_string()						)
					self.assertIsInstance(processor.image_processor    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Any 							=    image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					SCREAMING_SNAKE_CASE	:       str 							=    processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()    ,					input_processor[key].sum()    ,					delta=1E-2						)

					# With rest of the args
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       str 							=    image_processor(
					    _SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    processor(
					    images=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()    ,					input_processor[key].sum()    ,					delta=1E-2						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'lower newer'

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer(_SCREAMING_SNAKE_CASE						)

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]    ,					encoded_processor[key]						)





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(list(inputs.keys()						)    ,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

					# add extra args
					SCREAMING_SNAKE_CASE	:       Any 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(
					    list(inputs.keys()						)    ,					[
					        'input_ids',
					        'token_type_ids',
					        'attention_mask',
					        'pixel_values',
					        'codebook_pixel_values',
					        'bool_masked_pos',
					    ]    ,					)

					# test if it raises when no input is passed
					with pytest.raises(_SCREAMING_SNAKE_CASE						):
									processor()





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    processor.batch_decode(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					int						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Any 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Dict 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(list(inputs.keys()						)    ,					processor.model_input_names						)





