





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
		SCREAMING_SNAKE_CASE_      :			Any		  =      '''platform'''

		import jax
		import jax.numpy as jnp

		from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case=None	,      snake_case=None       )		->						Optional[Any]:
		if attention_mask is None:
				__lowercase					=    np.where(input_ids != config.pad_token_id	,      1	,      0       )
		return {
		    "input_ids": input_ids,
		    "attention_mask": attention_mask,
		}

@require_flax
class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    Union[str, Any]				,			__lowerCamelCase       :    List[str]				,			__lowerCamelCase       :    Dict=13				,			__lowerCamelCase       :    Optional[int]=7				,			__lowerCamelCase       :    Tuple=True				,			__lowerCamelCase       :    int=False				,			__lowerCamelCase       :    str=99				,			__lowerCamelCase       :    Optional[int]=16				,			__lowerCamelCase       :    Optional[int]=2				,			__lowerCamelCase       :    List[Any]=4				,			__lowerCamelCase       :    List[Any]=4				,			__lowerCamelCase       :    int="gelu"				,			__lowerCamelCase       :    Optional[int]=0.1				,			__lowerCamelCase       :    Optional[Any]=0.1				,			__lowerCamelCase       :    List[Any]=20				,			__lowerCamelCase       :    List[Any]=2				,			__lowerCamelCase       :    str=1				,			__lowerCamelCase       :    Optional[int]=0				,			__lowerCamelCase       :    Union[str, Any]=16				,			__lowerCamelCase       :    str=16				,			__lowerCamelCase       :    Any=0.02				,			)					->   List[str]:

			'''simple docstring'''




			__lowercase					=    parent
			__lowercase					=    batch_size
			__lowercase					=    seq_length
			__lowercase					=    is_training
			__lowercase					=    use_labels
			__lowercase					=    vocab_size
			__lowercase					=    hidden_size
			__lowercase					=    num_hidden_layers
			__lowercase					=    num_attention_heads
			__lowercase					=    intermediate_size
			__lowercase					=    hidden_act
			__lowercase					=    hidden_dropout_prob
			__lowercase					=    attention_probs_dropout_prob
			__lowercase					=    max_position_embeddings
			__lowercase					=    eos_token_id
			__lowercase					=    pad_token_id
			__lowercase					=    bos_token_id
			__lowercase					=    embed_dim
			__lowercase					=    word_embed_proj_dim
			__lowercase					=    initializer_range
			__lowercase					=    False




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

			'''simple docstring'''




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

			__lowercase					=    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				,			)
			__lowercase					=    prepare_opt_inputs_dict(__lowerCamelCase				,			__lowerCamelCase      )
			return config, inputs_dict




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

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_config_and_inputs()
			return config, inputs_dict




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Tuple      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    20
			__lowercase					=    model_class_name(__lowerCamelCase      )

			__lowercase					=    inputs_dict['input_ids']
			__lowercase					=    inputs_dict['attention_mask']

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

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

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

			__lowercase					=    model(__lowerCamelCase      )

			__lowercase					=    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       :    int				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    20
			__lowercase					=    model_class_name(__lowerCamelCase      )

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

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

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

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

			__lowercase					=    model(__lowerCamelCase				,			attention_mask=__lowerCamelCase      )

			__lowercase					=    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 		snake_case_      (       UpperCAmelCase_    ,    unittest.TestCase    ,    UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	__UpperCamelCase        =		() if is_flax_available() else ()




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

			'''simple docstring'''




			__lowercase					=    FlaxOPTModelTester(self      )




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

			'''simple docstring'''




			__lowercase	,			__lowercase					=    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       :    str      )					->   Dict:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    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       :    Optional[Any]      )					->   Any:

			'''simple docstring'''




			for model_class_name in self.all_model_classes:
					__lowercase					=    model_class_name.from_pretrained('facebook/opt-125m'      )
					__lowercase					=    np.ones((1, 1)      ) * model.config.eos_token_id
					__lowercase					=    model(__lowerCamelCase      )
					self.assertIsNotNone(__lowerCamelCase      )





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







	'''simple docstring'''




	@slow
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    FlaxOPTModel.from_pretrained('facebook/opt-350m'      )
			__lowercase					=    jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      )
			__lowercase					=    model(input_ids=__lowerCamelCase      ).last_hidden_state
			__lowercase					=    (1, 11, 512)
			self.assertEqual(output.shape				,			__lowerCamelCase      )
			__lowercase					=    jnp.array(
			    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]      )
			self.assertTrue(jnp.allclose(output[:, :3, :3]				,			__lowerCamelCase				,			atol=4E-2      )      )





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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    int      )					->   str:

			'''simple docstring'''




			super().setUp()
			__lowercase					=    'facebook/opt-350m'






	def       UpperCAmelCase (	self       :    Tuple      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    FlaxOPTForCausalLM.from_pretrained(self.path_model      )
			__lowercase					=    GPTaTokenizer.from_pretrained(self.path_model      )

			__lowercase					=    [
			    '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
			__lowercase					=    tokenizer(__lowerCamelCase				,			return_tensors='jax'				,			padding=__lowerCamelCase				,			add_special_tokens=__lowerCamelCase      )
			__lowercase					=    model(inputs.input_ids				,			attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
			__lowercase					=    jnp.array(
			    [
			        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
			        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
			        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
			        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
			    ]      )
			self.assertTrue(jnp.allclose(__lowerCamelCase				,			__lowerCamelCase				,			atol=4E-2      )      )

			__lowercase					=    jax.jit(__lowerCamelCase      )
			__lowercase					=    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 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   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       UpperCAmelCase (	self       :    Any      )					->   int:

			'''simple docstring'''




			__lowercase					=    'facebook/opt-125m'

			__lowercase					=    [
			    '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',
			]

			__lowercase					=    []

			__lowercase					=    FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase      )
			__lowercase					=    GPTaTokenizer.from_pretrained(__lowerCamelCase      )

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

					__lowercase					=    model.generate(__lowerCamelCase				,			max_length=10      )
					__lowercase					=    generated_ids[0]

					__lowercase					=    tokenizer.batch_decode(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )
					predicted_outputs += generated_string

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Tuple      )					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    'facebook/opt-350m'

			__lowercase					=    [
			    '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',
			]

			__lowercase					=    []
			__lowercase					=    FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase      )
			__lowercase					=    GPTaTokenizer.from_pretrained(__lowerCamelCase      )

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

					__lowercase					=    model.generate(__lowerCamelCase				,			max_length=10      )
					__lowercase					=    generated_ids[0]

					__lowercase					=    tokenizer.batch_decode(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )
					predicted_outputs += generated_string

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   Optional[Any]:

			'''simple docstring'''




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

			__lowercase					=    jax.jit(model.generate      )

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

			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )

			self.assertIsNotNone(__lowerCamelCase				,			__lowerCamelCase      )






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

			'''simple docstring'''




			__lowercase					=    'facebook/opt-350m'

			__lowercase					=    GPTaTokenizer.from_pretrained(__lowerCamelCase      )
			__lowercase					=    FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase      )

			__lowercase					=    'left'

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

			__lowercase					=    tokenizer(__lowerCamelCase				,			return_tensors='jax'				,			padding=__lowerCamelCase      )
			__lowercase					=    inputs['input_ids']

			__lowercase					=    model.generate(input_ids=__lowerCamelCase				,			attention_mask=inputs['attention_mask']				,			trace=__lowerCamelCase      )

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

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

			__lowercase					=    tokenizer.batch_decode(outputs[0]				,			skip_special_tokens=__lowerCamelCase      )
			__lowercase					=    tokenizer.decode(output_non_padded[0][0]				,			skip_special_tokens=__lowerCamelCase      )
			__lowercase					=    tokenizer.decode(output_padded[0][0]				,			skip_special_tokens=__lowerCamelCase      )

			__lowercase					=    [
			    '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]      )











def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    str(snake_case       ).strip()
		if not bin_string:
				raise ValueError('Empty string was passed to the function'       )
		__lowercase					=    bin_string[0] == '-'
		if is_negative:
				__lowercase					=    bin_string[1:]
		if not all(char in '01' for char in bin_string       ):
				raise ValueError('Non-binary value was passed to the function'       )
		__lowercase					=    0
		for char in bin_string:
				__lowercase					=    2 * decimal_number + int(snake_case       )
		return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()




