

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	:   Tuple	=					'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        __UpperCamelCase     (      _A					:							List[str]      ,  _A					:							Any      ,  _A					:							Any=None      ,  _A					:							str=None		)				->List[str]:



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


@require_flax
class 			_SCREAMING_SNAKE_CASE      :







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=13     ,						_SCREAMING_SNAKE_CASE=7     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=99     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=20     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						)->							Tuple:
													lowerCamelCase_								=parent
													lowerCamelCase_								=batch_size
													lowerCamelCase_								=seq_length
													lowerCamelCase_								=is_training
													lowerCamelCase_								=use_labels
													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=eos_token_id
													lowerCamelCase_								=pad_token_id
													lowerCamelCase_								=bos_token_id
													lowerCamelCase_								=embed_dim
													lowerCamelCase_								=word_embed_proj_dim
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=False







						def        _snake_case				(			self							)->							int:
													lowerCamelCase_								=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,						self.vocab_size							)     ,						3     ,						self.vocab_size							)
													lowerCamelCase_								=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,						dtype=np.intaa							))     ,						-1							)

													lowerCamelCase_								=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__     ,						)
													lowerCamelCase_								=prepare_opt_inputs_dict(lowerCamelCase__     ,						lowerCamelCase__							)
													return config, inputs_dict







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_config_and_inputs()
													return config, inputs_dict







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													lowerCamelCase_								=20
													lowerCamelCase_								=model_class_name(lowerCamelCase__							)

													lowerCamelCase_								=inputs_dict["""input_ids"""]
													lowerCamelCase_								=inputs_dict["""attention_mask"""]

													lowerCamelCase_								=model.init_cache(input_ids.shape[0]     ,						lowerCamelCase__							)
													lowerCamelCase_								=jnp.ones((input_ids.shape[0], max_length)     ,						dtype="""i4"""							)

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

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

													lowerCamelCase_								=model(lowerCamelCase__							)

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







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Any:
													lowerCamelCase_								=20
													lowerCamelCase_								=model_class_name(lowerCamelCase__							)

													lowerCamelCase_	,   lowerCamelCase_								=(
													    inputs_dict["""input_ids"""],
													    inputs_dict["""attention_mask"""],
													)

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

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

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

													lowerCamelCase_								=model(lowerCamelCase__     ,						attention_mask=lowerCamelCase__							)

													lowerCamelCase_								=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 			_SCREAMING_SNAKE_CASE      (  __UpperCAmelCase ,    unittest.TestCase ,    __UpperCAmelCase):
						_UpperCamelCase:Dict	=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						_UpperCamelCase:Optional[int]	=		() if is_flax_available() else ()







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=FlaxOPTModelTester(self							)







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_	,   lowerCamelCase_								=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        _snake_case				(			self							)->							str:
													lowerCamelCase_	,   lowerCamelCase_								=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        _snake_case				(			self							)->							List[Any]:
													for model_class_name in self.all_model_classes:
																				lowerCamelCase_								=model_class_name.from_pretrained("""facebook/opt-125m"""							)
																				lowerCamelCase_								=np.ones((1, 1)							) * model.config.eos_token_id
																				lowerCamelCase_								=model(lowerCamelCase__							)
																				self.assertIsNotNone(lowerCamelCase__							)


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







						@slow
						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								=FlaxOPTModel.from_pretrained("""facebook/opt-350m"""							)
													lowerCamelCase_								=jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]							)
													lowerCamelCase_								=model(input_ids=lowerCamelCase__							).last_hidden_state
													lowerCamelCase_								=(1, 11, 512)
													self.assertEqual(output.shape     ,						lowerCamelCase__							)
													lowerCamelCase_								=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 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						def        _snake_case				(			self							)->							Optional[Any]:
													super().setUp()
													lowerCamelCase_								="""facebook/opt-350m"""







						def        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_								=FlaxOPTForCausalLM.from_pretrained(self.path_model							)
													lowerCamelCase_								=GPTaTokenizer.from_pretrained(self.path_model							)

													lowerCamelCase_								=[
													    """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_								=tokenizer(lowerCamelCase__     ,						return_tensors="""jax"""     ,						padding=lowerCamelCase__     ,						add_special_tokens=lowerCamelCase__							)
													lowerCamelCase_								=model(inputs.input_ids     ,						attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
													lowerCamelCase_								=jnp.array(
													    [
													        [1.3_8_5_1, -13.8923, -10.5229, -10.7533, -0.2_3_0_9, -10.2384, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
													        [-4.7_0_7_3, -10.6276, -3.9_4_1_5, -21.5242, -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, -14.1650, 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, -10.7926, -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							)							)

													lowerCamelCase_								=jax.jit(lowerCamelCase__							)
													lowerCamelCase_								=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 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						@property
						def        _snake_case				(			self							)->							List[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        _snake_case				(			self							)->							str:
													lowerCamelCase_								="""facebook/opt-125m"""

													lowerCamelCase_								=[
													    """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_								=[]

													lowerCamelCase_								=FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__							)
													lowerCamelCase_								=GPTaTokenizer.from_pretrained(lowerCamelCase__							)

													for prompt in self.prompts:
																				lowerCamelCase_								=tokenizer(lowerCamelCase__     ,						return_tensors="""jax"""							).input_ids

																				lowerCamelCase_								=model.generate(lowerCamelCase__     ,						max_length=10							)
																				lowerCamelCase_								=generated_ids[0]

																				lowerCamelCase_								=tokenizer.batch_decode(lowerCamelCase__     ,						skip_special_tokens=lowerCamelCase__							)
																				predicted_outputs += generated_string

													self.assertListEqual(lowerCamelCase__     ,						lowerCamelCase__							)







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								="""facebook/opt-350m"""

													lowerCamelCase_								=[
													    """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_								=[]
													lowerCamelCase_								=FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__							)
													lowerCamelCase_								=GPTaTokenizer.from_pretrained(lowerCamelCase__							)

													for prompt in self.prompts:
																				lowerCamelCase_								=tokenizer(lowerCamelCase__     ,						return_tensors="""jax"""							).input_ids

																				lowerCamelCase_								=model.generate(lowerCamelCase__     ,						max_length=10							)
																				lowerCamelCase_								=generated_ids[0]

																				lowerCamelCase_								=tokenizer.batch_decode(lowerCamelCase__     ,						skip_special_tokens=lowerCamelCase__							)
																				predicted_outputs += generated_string

													self.assertListEqual(lowerCamelCase__     ,						lowerCamelCase__							)







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								="""facebook/opt-125m"""
													lowerCamelCase_								=[
													    """Today is a beautiful day and I want to thank""",
													    """In the city of Rome Canaver Canaver Canaver Canaver""",
													]
													lowerCamelCase_								=FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__							)
													lowerCamelCase_								=GPTaTokenizer.from_pretrained(lowerCamelCase__							)
													lowerCamelCase_								=tokenizer(
													    [
													        """Today is a beautiful day and I want to""",
													        """In the city of""",
													    ]     ,						return_tensors="""jax"""     ,						padding=lowerCamelCase__     ,						)

													lowerCamelCase_								=jax.jit(model.generate							)

													lowerCamelCase_								=jit_generate(inputs["""input_ids"""]     ,						attention_mask=inputs["""attention_mask"""]							).sequences

													lowerCamelCase_								=tokenizer.batch_decode(lowerCamelCase__     ,						skip_special_tokens=lowerCamelCase__							)

													self.assertIsNotNone(lowerCamelCase__     ,						lowerCamelCase__							)







						def        _snake_case				(			self							)->							Tuple:
													lowerCamelCase_								="""facebook/opt-350m"""

													lowerCamelCase_								=GPTaTokenizer.from_pretrained(lowerCamelCase__							)
													lowerCamelCase_								=FlaxOPTForCausalLM.from_pretrained(lowerCamelCase__							)

													lowerCamelCase_								="""left"""

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

													lowerCamelCase_								=tokenizer(lowerCamelCase__     ,						return_tensors="""jax"""     ,						padding=lowerCamelCase__							)
													lowerCamelCase_								=inputs["""input_ids"""]

													lowerCamelCase_								=model.generate(input_ids=lowerCamelCase__     ,						attention_mask=inputs["""attention_mask"""]     ,						trace=lowerCamelCase__							)

													lowerCamelCase_								=tokenizer(sentences[0]     ,						return_tensors="""jax"""							).input_ids
													lowerCamelCase_								=model.generate(input_ids=lowerCamelCase__							)

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

													lowerCamelCase_								=tokenizer.batch_decode(outputs[0]     ,						skip_special_tokens=lowerCamelCase__							)
													lowerCamelCase_								=tokenizer.decode(output_non_padded[0][0]     ,						skip_special_tokens=lowerCamelCase__							)
													lowerCamelCase_								=tokenizer.decode(output_padded[0][0]     ,						skip_special_tokens=lowerCamelCase__							)

													lowerCamelCase_								=[
													    """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"""






def     a_		(   lowerCamelCase			):
				if num < 0:
								return -1
				return 1 if num else 0



def     a_		(   ):
				assert signum(5			) == 1
				assert signum(-5			) == -1
				assert signum(0			) == 0


if __name__ == "__main__":
						print(signum(12))
						print(signum(-12))
						print(signum(0))



