"""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
						lowerCAmelCase__    :    Tuple			=      'platform'

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=None   ,     lowerCamelCase=None			):
				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 							snake_case			:




					"""simple docstring"""

					def __init__(							self  :      str       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      Dict=13       ,lowerCamelCase__  :      int=7       ,lowerCamelCase__  :      int=True       ,lowerCamelCase__  :      Dict=False       ,lowerCamelCase__  :      str=99       ,lowerCamelCase__  :      str=16       ,lowerCamelCase__  :      int=2       ,lowerCamelCase__  :      Optional[int]=4       ,lowerCamelCase__  :      Optional[int]=4       ,lowerCamelCase__  :      List[str]="gelu"       ,lowerCamelCase__  :      List[Any]=0.1       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Any=20       ,lowerCamelCase__  :      Any=2       ,lowerCamelCase__  :      Dict=1       ,lowerCamelCase__  :      List[Any]=0       ,lowerCamelCase__  :      List[Any]=16       ,lowerCamelCase__  :      Optional[Any]=16       ,lowerCamelCase__  :      List[str]=0.0_2       ,):
									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 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									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=lowerCamelCase__       ,word_embed_proj_dim=self.word_embed_proj_dim       ,initializer_range=self.initializer_range       ,use_cache=lowerCamelCase__       ,)
									UpperCAmelCase__			 =				prepare_opt_inputs_dict(lowerCamelCase__       ,lowerCamelCase__     )
									return config, inputs_dict







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									UpperCAmelCase__ ,      UpperCAmelCase__			 =				self.prepare_config_and_inputs()
									return config, inputs_dict







					def 						__lowerCAmelCase		(							self  :      Dict       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      Optional[int]     ):
									UpperCAmelCase__			 =				20
									UpperCAmelCase__			 =				model_class_name(lowerCamelCase__     )

									UpperCAmelCase__			 =				inputs_dict['input_ids']
									UpperCAmelCase__			 =				inputs_dict['attention_mask']

									UpperCAmelCase__			 =				model.init_cache(input_ids.shape[0]       ,lowerCamelCase__     )
									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=lowerCamelCase__       ,past_key_values=lowerCamelCase__       ,position_ids=lowerCamelCase__       ,)

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

									UpperCAmelCase__			 =				model(lowerCamelCase__     )

									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 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      Optional[Any]     ):
									UpperCAmelCase__			 =				20
									UpperCAmelCase__			 =				model_class_name(lowerCamelCase__     )

									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]       ,lowerCamelCase__     )
									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=lowerCamelCase__       ,past_key_values=lowerCamelCase__       ,position_ids=lowerCamelCase__       ,)
									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=lowerCamelCase__       ,position_ids=lowerCamelCase__       ,)

									UpperCAmelCase__			 =				model(lowerCamelCase__       ,attention_mask=lowerCamelCase__     )

									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 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase ,       __UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					snake_case__							       =					() if is_flax_available() else ()







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				FlaxOPTModelTester(self     )







					def 						__lowerCAmelCase		(							self  :      Optional[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(lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      str     ):
									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(lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )







					@slow
					def 						__lowerCAmelCase		(							self  :      int     ):
									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(lowerCamelCase__     )
													self.assertIsNotNone(lowerCamelCase__     )






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




					"""simple docstring"""

					@slow
					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				FlaxOPTModel.from_pretrained('facebook/opt-350m'     )
									UpperCAmelCase__			 =				jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     )
									UpperCAmelCase__			 =				model(input_ids=lowerCamelCase__     ).last_hidden_state
									UpperCAmelCase__			 =				(1, 11, 512)
									self.assertEqual(output.shape       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				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 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      Any     ):
									super().setUp()
									UpperCAmelCase__			 =				'facebook/opt-350m'







					def 						__lowerCAmelCase		(							self  :      Any     ):
									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(lowerCamelCase__       ,return_tensors='jax'       ,padding=lowerCamelCase__       ,add_special_tokens=lowerCamelCase__     )
									UpperCAmelCase__			 =				model(inputs.input_ids       ,attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
									UpperCAmelCase__			 =				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     )     )

									UpperCAmelCase__			 =				jax.jit(lowerCamelCase__     )
									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									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     ):
									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(lowerCamelCase__     )
									UpperCAmelCase__			 =				GPTaTokenizer.from_pretrained(lowerCamelCase__     )

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

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

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

									self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      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(lowerCamelCase__     )
									UpperCAmelCase__			 =				GPTaTokenizer.from_pretrained(lowerCamelCase__     )

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

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

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

									self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      str     ):
									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(lowerCamelCase__     )
									UpperCAmelCase__			 =				GPTaTokenizer.from_pretrained(lowerCamelCase__     )
									UpperCAmelCase__			 =				tokenizer(
									    [
									        'Today is a beautiful day and I want to',
									        'In the city of',
									    ]       ,return_tensors='jax'       ,padding=lowerCamelCase__       ,)

									UpperCAmelCase__			 =				jax.jit(model.generate     )

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

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

									self.assertIsNotNone(lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Dict     ):
									UpperCAmelCase__			 =				'facebook/opt-350m'

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

									UpperCAmelCase__			 =				'left'

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

									UpperCAmelCase__			 =				tokenizer(lowerCamelCase__       ,return_tensors='jax'       ,padding=lowerCamelCase__     )
									UpperCAmelCase__			 =				inputs['input_ids']

									UpperCAmelCase__			 =				model.generate(input_ids=lowerCamelCase__       ,attention_mask=inputs['attention_mask']       ,trace=lowerCamelCase__     )

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

									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=lowerCamelCase__       ,max_length=model.config.max_length - num_paddings     )

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

									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(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))



