


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      :	Tuple  ="""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_=None			, lowerCAmelCase_=None			)->			str:
				if attention_mask is None:
								lowerCAmelCase_			:   Union[str, Any]     					=					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 ,    __lowercase ,    __lowercase=1_3 ,    __lowercase=7 ,    __lowercase=True ,    __lowercase=False ,    __lowercase=9_9 ,    __lowercase=1_6 ,    __lowercase=2 ,    __lowercase=4 ,    __lowercase=4 ,    __lowercase="gelu" ,    __lowercase=0.1 ,    __lowercase=0.1 ,    __lowercase=2_0 ,    __lowercase=2 ,    __lowercase=1 ,    __lowercase=0 ,    __lowercase=1_6 ,    __lowercase=1_6 ,    __lowercase=0.02 ,    )       ->     Optional[Any]:
										lowerCAmelCase_			:   str     					=					parent
										lowerCAmelCase_			:   Optional[int]     					=					batch_size
										lowerCAmelCase_			:   Union[str, Any]     					=					seq_length
										lowerCAmelCase_			:   Tuple     					=					is_training
										lowerCAmelCase_			:   List[Any]     					=					use_labels
										lowerCAmelCase_			:   Optional[int]     					=					vocab_size
										lowerCAmelCase_			:   List[str]     					=					hidden_size
										lowerCAmelCase_			:   Tuple     					=					num_hidden_layers
										lowerCAmelCase_			:   Any     					=					num_attention_heads
										lowerCAmelCase_			:   int     					=					intermediate_size
										lowerCAmelCase_			:   Optional[int]     					=					hidden_act
										lowerCAmelCase_			:   Tuple     					=					hidden_dropout_prob
										lowerCAmelCase_			:   int     					=					attention_probs_dropout_prob
										lowerCAmelCase_			:   Optional[Any]     					=					max_position_embeddings
										lowerCAmelCase_			:   Any     					=					eos_token_id
										lowerCAmelCase_			:   List[Any]     					=					pad_token_id
										lowerCAmelCase_			:   Any     					=					bos_token_id
										lowerCAmelCase_			:   Any     					=					embed_dim
										lowerCAmelCase_			:   Dict     					=					word_embed_proj_dim
										lowerCAmelCase_			:   List[str]     					=					initializer_range
										lowerCAmelCase_			:   List[str]     					=					False




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

										lowerCAmelCase_			:   int     					=					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 ,    )
										lowerCAmelCase_			:   List[str]     					=					prepare_opt_inputs_dict(__lowercase ,    __lowercase				)
										return config, inputs_dict




						def     lowercase_			(					self				)       ->     str:
										lowerCAmelCase_					,	lowerCAmelCase_			:   Tuple     					=					self.prepare_config_and_inputs()
										return config, inputs_dict




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Optional[int]:
										lowerCAmelCase_			:   int     					=					2_0
										lowerCAmelCase_			:   List[str]     					=					model_class_name(__lowercase				)

										lowerCAmelCase_			:   Optional[int]     					=					inputs_dict['''input_ids''']
										lowerCAmelCase_			:   Tuple     					=					inputs_dict['''attention_mask''']

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

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

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

										lowerCAmelCase_			:   Dict     					=					model(__lowercase				)

										lowerCAmelCase_			:   int     					=					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     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     List[str]:
										lowerCAmelCase_			:   Tuple     					=					2_0
										lowerCAmelCase_			:   Union[str, Any]     					=					model_class_name(__lowercase				)

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

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

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

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

										lowerCAmelCase_			:   Union[str, Any]     					=					model(__lowercase ,    attention_mask=__lowercase				)

										lowerCAmelCase_			:   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}"""				)







@require_flax
class 					snake_case__(				UpperCAmelCase__,    unittest.TestCase,    UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						SCREAMING_SNAKE_CASE__   :	Tuple       =				() if is_flax_available() else ()




						def     lowercase_			(					self				)       ->     Optional[int]:
										lowerCAmelCase_			:   str     					=					FlaxOPTModelTester(self				)




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[Any]     					=					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     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[str]     					=					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     lowercase_			(					self				)       ->     Dict:
										for model_class_name in self.all_model_classes:
														lowerCAmelCase_			:   Optional[int]     					=					model_class_name.from_pretrained('''facebook/opt-125m'''				)
														lowerCAmelCase_			:   str     					=					np.ones((1, 1)				) * model.config.eos_token_id
														lowerCAmelCase_			:   Any     					=					model(__lowercase				)
														self.assertIsNotNone(__lowercase				)







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







						'''simple docstring'''




						@slow
						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   int     					=					FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
										lowerCAmelCase_			:   List[str]     					=					jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										lowerCAmelCase_			:   List[str]     					=					model(input_ids=__lowercase				).last_hidden_state
										lowerCAmelCase_			:   Tuple     					=					(1, 1_1, 5_1_2)
										self.assertEqual(output.shape ,    __lowercase				)
										lowerCAmelCase_			:   Union[str, Any]     					=					jnp.array(
										    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]				)
										self.assertTrue(jnp.allclose(output[:, :3, :3] ,    __lowercase ,    atol=4e-2				)				)







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







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     Optional[int]:
										super().setUp()
										lowerCAmelCase_			:   Any     					=					'''facebook/opt-350m'''




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Optional[Any]     					=					FlaxOPTForCausalLM.from_pretrained(self.path_model				)
										lowerCAmelCase_			:   List[Any]     					=					GPTaTokenizer.from_pretrained(self.path_model				)

										lowerCAmelCase_			:   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
										lowerCAmelCase_			:   Tuple     					=					tokenizer(__lowercase ,    return_tensors='''jax''' ,    padding=__lowercase ,    add_special_tokens=__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					model(inputs.input_ids ,    attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
										lowerCAmelCase_			:   List[str]     					=					jnp.array(
										    [
										        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
										        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
										        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
										        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
										    ]				)
										self.assertTrue(jnp.allclose(__lowercase ,    __lowercase ,    atol=4e-2				)				)

										lowerCAmelCase_			:   str     					=					jax.jit(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					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 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




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

										lowerCAmelCase_			:   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''',
										]

										lowerCAmelCase_			:   Any     					=					[]

										lowerCAmelCase_			:   List[Any]     					=					FlaxOPTForCausalLM.from_pretrained(__lowercase				)
										lowerCAmelCase_			:   int     					=					GPTaTokenizer.from_pretrained(__lowercase				)

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

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

														lowerCAmelCase_			:   Union[str, Any]     					=					tokenizer.batch_decode(__lowercase ,    skip_special_tokens=__lowercase				)
														predicted_outputs += generated_string

										self.assertListEqual(__lowercase ,    __lowercase				)




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					'''facebook/opt-350m'''

										lowerCAmelCase_			:   Optional[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''',
										]

										lowerCAmelCase_			:   Optional[Any]     					=					[]
										lowerCAmelCase_			:   Optional[int]     					=					FlaxOPTForCausalLM.from_pretrained(__lowercase				)
										lowerCAmelCase_			:   Optional[Any]     					=					GPTaTokenizer.from_pretrained(__lowercase				)

										for prompt in self.prompts:
														lowerCAmelCase_			:   Optional[int]     					=					tokenizer(__lowercase ,    return_tensors='''jax'''				).input_ids

														lowerCAmelCase_			:   str     					=					model.generate(__lowercase ,    max_length=1_0				)
														lowerCAmelCase_			:   Optional[int]     					=					generated_ids[0]

														lowerCAmelCase_			:   Union[str, Any]     					=					tokenizer.batch_decode(__lowercase ,    skip_special_tokens=__lowercase				)
														predicted_outputs += generated_string

										self.assertListEqual(__lowercase ,    __lowercase				)




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

										lowerCAmelCase_			:   str     					=					jax.jit(model.generate				)

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

										lowerCAmelCase_			:   List[str]     					=					tokenizer.batch_decode(__lowercase ,    skip_special_tokens=__lowercase				)

										self.assertIsNotNone(__lowercase ,    __lowercase				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Optional[int]     					=					'''facebook/opt-350m'''

										lowerCAmelCase_			:   str     					=					GPTaTokenizer.from_pretrained(__lowercase				)
										lowerCAmelCase_			:   Optional[int]     					=					FlaxOPTForCausalLM.from_pretrained(__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					'''left'''

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

										lowerCAmelCase_			:   int     					=					tokenizer(__lowercase ,    return_tensors='''jax''' ,    padding=__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					inputs['''input_ids''']

										lowerCAmelCase_			:   Optional[Any]     					=					model.generate(input_ids=__lowercase ,    attention_mask=inputs['''attention_mask'''] ,    trace=__lowercase				)

										lowerCAmelCase_			:   List[str]     					=					tokenizer(sentences[0] ,    return_tensors='''jax'''				).input_ids
										lowerCAmelCase_			:   Optional[Any]     					=					model.generate(input_ids=__lowercase				)

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

										lowerCAmelCase_			:   List[Any]     					=					tokenizer.batch_decode(outputs[0] ,    skip_special_tokens=__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					tokenizer.decode(output_non_padded[0][0] ,    skip_special_tokens=__lowercase				)
										lowerCAmelCase_			:   Any     					=					tokenizer.decode(output_padded[0][0] ,    skip_special_tokens=__lowercase				)

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



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			set:
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_a={set_a}] is not a set"""
				assert isinstance(lowerCAmelCase_			, lowerCAmelCase_			), f"""The input value of [set_b={set_b}] is not a set"""

				return {a + b for a in set_a for b in set_b}


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

						testmod()