



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
							UpperCamelCase_      			=  "platform"

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=None				,   __UpperCAmelCase=None      )			->			List[Any]:



						'''simple docstring'''






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







@require_flax
class 				a_    :

						def __init__(       self      :int  ,		_lowercase      :str  ,		_lowercase      :List[str]=13  ,		_lowercase      :Tuple=7  ,		_lowercase      :Tuple=True  ,		_lowercase      :List[Any]=False  ,		_lowercase      :Any=99  ,		_lowercase      :List[Any]=16  ,		_lowercase      :Union[str, Any]=2  ,		_lowercase      :List[str]=4  ,		_lowercase      :Optional[Any]=4  ,		_lowercase      :List[str]="gelu"  ,		_lowercase      :str=0.1  ,		_lowercase      :int=0.1  ,		_lowercase      :List[str]=20  ,		_lowercase      :int=2  ,		_lowercase      :List[Any]=1  ,		_lowercase      :Tuple=0  ,		_lowercase      :Optional[Any]=16  ,		_lowercase      :Dict=16  ,		_lowercase      :Optional[Any]=0.02  ,		)						->   int:
												UpperCAmelCase_					=	parent
												UpperCAmelCase_					=	batch_size
												UpperCAmelCase_					=	seq_length
												UpperCAmelCase_					=	is_training
												UpperCAmelCase_					=	use_labels
												UpperCAmelCase_					=	vocab_size
												UpperCAmelCase_					=	hidden_size
												UpperCAmelCase_					=	num_hidden_layers
												UpperCAmelCase_					=	num_attention_heads
												UpperCAmelCase_					=	intermediate_size
												UpperCAmelCase_					=	hidden_act
												UpperCAmelCase_					=	hidden_dropout_prob
												UpperCAmelCase_					=	attention_probs_dropout_prob
												UpperCAmelCase_					=	max_position_embeddings
												UpperCAmelCase_					=	eos_token_id
												UpperCAmelCase_					=	pad_token_id
												UpperCAmelCase_					=	bos_token_id
												UpperCAmelCase_					=	embed_dim
												UpperCAmelCase_					=	word_embed_proj_dim
												UpperCAmelCase_					=	initializer_range
												UpperCAmelCase_					=	False

						def       __a			(       self      :Dict)						->   str:
												UpperCAmelCase_					=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,		self.vocab_size)  ,		3  ,		self.vocab_size)
												UpperCAmelCase_					=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,		dtype=np.intaa))  ,		-1)

												UpperCAmelCase_					=	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=_lowercase  ,		word_embed_proj_dim=self.word_embed_proj_dim  ,		initializer_range=self.initializer_range  ,		use_cache=_lowercase  ,		)
												UpperCAmelCase_					=	prepare_opt_inputs_dict(_lowercase  ,		_lowercase)
												return config, inputs_dict

						def       __a			(       self      :Optional[Any])						->   int:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.prepare_config_and_inputs()
												return config, inputs_dict

						def       __a			(       self      :Optional[Any]  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Dict  ,		_lowercase      :Tuple)						->   Dict:
												UpperCAmelCase_					=	20
												UpperCAmelCase_					=	model_class_name(_lowercase)

												UpperCAmelCase_					=	inputs_dict['''input_ids''']
												UpperCAmelCase_					=	inputs_dict['''attention_mask''']

												UpperCAmelCase_					=	model.init_cache(input_ids.shape[0]  ,		_lowercase)
												UpperCAmelCase_					=	jnp.ones((input_ids.shape[0], max_length)  ,		dtype='''i4''')

												UpperCAmelCase_					=	jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,		(input_ids.shape[0], input_ids.shape[-1] - 1)  ,		)
												UpperCAmelCase_					=	model(
												    input_ids[:, :-1]  ,		attention_mask=_lowercase  ,		past_key_values=_lowercase  ,		position_ids=_lowercase  ,		)

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

												UpperCAmelCase_					=	model(_lowercase)

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




						def       __a			(       self      :Dict  ,		_lowercase      :int  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Dict)						->   int:
												UpperCAmelCase_					=	20
												UpperCAmelCase_					=	model_class_name(_lowercase)

												UpperCAmelCase_				, UpperCAmelCase_					=	(
												    inputs_dict['''input_ids'''],
												    inputs_dict['''attention_mask'''],
												)

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

												UpperCAmelCase_					=	model.init_cache(input_ids.shape[0]  ,		_lowercase)
												UpperCAmelCase_					=	jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,		(input_ids.shape[0], input_ids.shape[-1] - 1)  ,		)

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

												UpperCAmelCase_					=	model(_lowercase  ,		attention_mask=_lowercase)

												UpperCAmelCase_					=	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 				a_    (						_snake_case   ,					unittest.TestCase   ,					_snake_case							):
						UpperCamelCase__ :		Any										=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						UpperCamelCase__ :		str										=() if is_flax_available() else ()

						def       __a			(       self      :Tuple)						->   Dict:
												UpperCAmelCase_					=	FlaxOPTModelTester(self)

						def       __a			(       self      :List[str])						->   List[Any]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_lowercase  ,		_lowercase  ,		_lowercase)

						def       __a			(       self      :List[Any])						->   Union[str, Any]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase  ,		_lowercase  ,		_lowercase)




						@slow
						def       __a			(       self      :Any)						->   Any:
												for model_class_name in self.all_model_classes:
																		UpperCAmelCase_					=	model_class_name.from_pretrained('''facebook/opt-125m''')
																		UpperCAmelCase_					=	np.ones((1, 1)) * model.config.eos_token_id
																		UpperCAmelCase_					=	model(_lowercase)
																		self.assertIsNotNone(_lowercase)







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

						@slow
						def       __a			(       self      :str)						->   Tuple:
												UpperCAmelCase_					=	FlaxOPTModel.from_pretrained('''facebook/opt-350m''')
												UpperCAmelCase_					=	jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
												UpperCAmelCase_					=	model(input_ids=_lowercase).last_hidden_state
												UpperCAmelCase_					=	(1, 11, 512)
												self.assertEqual(output.shape  ,		_lowercase)
												UpperCAmelCase_					=	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]  ,		_lowercase  ,		atol=4E-2))







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

						def       __a			(       self      :Dict)						->   Optional[Any]:
												super().setUp()
												UpperCAmelCase_					=	'''facebook/opt-350m'''




						def       __a			(       self      :List[Any])						->   Tuple:
												UpperCAmelCase_					=	FlaxOPTForCausalLM.from_pretrained(self.path_model)
												UpperCAmelCase_					=	GPTaTokenizer.from_pretrained(self.path_model)

												UpperCAmelCase_					=	[
												    '''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_					=	tokenizer(_lowercase  ,		return_tensors='''jax'''  ,		padding=_lowercase  ,		add_special_tokens=_lowercase)
												UpperCAmelCase_					=	model(inputs.input_ids  ,		attention_mask=inputs.attention_mask)[0].mean(axis=-1)
												UpperCAmelCase_					=	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(_lowercase  ,		_lowercase  ,		atol=4E-2))

												UpperCAmelCase_					=	jax.jit(_lowercase)
												UpperCAmelCase_					=	model(inputs.input_ids  ,		attention_mask=inputs.attention_mask)[0].mean(axis=-1)
												self.assertTrue(jnp.allclose(_lowercase  ,		_lowercase  ,		atol=4E-2))







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

						@property
						def       __a			(       self      :Optional[int])						->   str:
												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       __a			(       self      :Any)						->   List[str]:
												UpperCAmelCase_					=	'''facebook/opt-125m'''

												UpperCAmelCase_					=	[
												    '''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_					=	[]

												UpperCAmelCase_					=	FlaxOPTForCausalLM.from_pretrained(_lowercase)
												UpperCAmelCase_					=	GPTaTokenizer.from_pretrained(_lowercase)

												for prompt in self.prompts:
																		UpperCAmelCase_					=	tokenizer(_lowercase  ,		return_tensors='''jax''').input_ids

																		UpperCAmelCase_					=	model.generate(_lowercase  ,		max_length=10)
																		UpperCAmelCase_					=	generated_ids[0]

																		UpperCAmelCase_					=	tokenizer.batch_decode(_lowercase  ,		skip_special_tokens=_lowercase)
																		predicted_outputs += generated_string

												self.assertListEqual(_lowercase  ,		_lowercase)

						def       __a			(       self      :Optional[Any])						->   Optional[Any]:
												UpperCAmelCase_					=	'''facebook/opt-350m'''

												UpperCAmelCase_					=	[
												    '''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_					=	[]
												UpperCAmelCase_					=	FlaxOPTForCausalLM.from_pretrained(_lowercase)
												UpperCAmelCase_					=	GPTaTokenizer.from_pretrained(_lowercase)

												for prompt in self.prompts:
																		UpperCAmelCase_					=	tokenizer(_lowercase  ,		return_tensors='''jax''').input_ids

																		UpperCAmelCase_					=	model.generate(_lowercase  ,		max_length=10)
																		UpperCAmelCase_					=	generated_ids[0]

																		UpperCAmelCase_					=	tokenizer.batch_decode(_lowercase  ,		skip_special_tokens=_lowercase)
																		predicted_outputs += generated_string

												self.assertListEqual(_lowercase  ,		_lowercase)

						def       __a			(       self      :int)						->   Dict:
												UpperCAmelCase_					=	'''facebook/opt-125m'''
												UpperCAmelCase_					=	[
												    '''Today is a beautiful day and I want to thank''',
												    '''In the city of Rome Canaver Canaver Canaver Canaver''',
												]
												UpperCAmelCase_					=	FlaxOPTForCausalLM.from_pretrained(_lowercase)
												UpperCAmelCase_					=	GPTaTokenizer.from_pretrained(_lowercase)
												UpperCAmelCase_					=	tokenizer(
												    [
												        '''Today is a beautiful day and I want to''',
												        '''In the city of''',
												    ]  ,		return_tensors='''jax'''  ,		padding=_lowercase  ,		)

												UpperCAmelCase_					=	jax.jit(model.generate)

												UpperCAmelCase_					=	jit_generate(inputs['''input_ids''']  ,		attention_mask=inputs['''attention_mask''']).sequences

												UpperCAmelCase_					=	tokenizer.batch_decode(_lowercase  ,		skip_special_tokens=_lowercase)

												self.assertIsNotNone(_lowercase  ,		_lowercase)




						def       __a			(       self      :List[str])						->   Union[str, Any]:
												UpperCAmelCase_					=	'''facebook/opt-350m'''

												UpperCAmelCase_					=	GPTaTokenizer.from_pretrained(_lowercase)
												UpperCAmelCase_					=	FlaxOPTForCausalLM.from_pretrained(_lowercase)

												UpperCAmelCase_					=	'''left'''

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

												UpperCAmelCase_					=	tokenizer(_lowercase  ,		return_tensors='''jax'''  ,		padding=_lowercase)
												UpperCAmelCase_					=	inputs['''input_ids''']

												UpperCAmelCase_					=	model.generate(input_ids=_lowercase  ,		attention_mask=inputs['''attention_mask''']  ,		trace=_lowercase)

												UpperCAmelCase_					=	tokenizer(sentences[0]  ,		return_tensors='''jax''').input_ids
												UpperCAmelCase_					=	model.generate(input_ids=_lowercase)

												UpperCAmelCase_					=	inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
												UpperCAmelCase_					=	tokenizer(sentences[1]  ,		return_tensors='''jax''').input_ids
												UpperCAmelCase_					=	model.generate(input_ids=_lowercase  ,		max_length=model.config.max_length - num_paddings)

												UpperCAmelCase_					=	tokenizer.batch_decode(outputs[0]  ,		skip_special_tokens=_lowercase)
												UpperCAmelCase_					=	tokenizer.decode(output_non_padded[0][0]  ,		skip_special_tokens=_lowercase)
												UpperCAmelCase_					=	tokenizer.decode(output_padded[0][0]  ,		skip_special_tokens=_lowercase)

												UpperCAmelCase_					=	[
												    '''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(_lowercase  ,		_lowercase)
												self.assertListEqual(_lowercase  ,		[non_padded_sentence, padded_sentence])










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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





