


'''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							:     str								=			'platform'

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A=None						,				A=None ) -> int:






       """simple docstring"""







       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 						__lowerCAmelCase		:





      '''simple docstring'''






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

             '''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				):

             '''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=UpperCamelCase     ,		word_embed_proj_dim=self.word_embed_proj_dim     ,		initializer_range=self.initializer_range     ,		use_cache=UpperCamelCase     ,		)
             lowercase__       =				prepare_opt_inputs_dict(UpperCamelCase     ,		UpperCamelCase				)
             return config, inputs_dict



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







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



      def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				20
             lowercase__       =				model_class_name(UpperCamelCase				)

             lowercase__       =				inputs_dict['''input_ids''']
             lowercase__       =				inputs_dict['''attention_mask''']

             lowercase__       =				model.init_cache(input_ids.shape[0]     ,		UpperCamelCase				)
             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=UpperCamelCase     ,		past_key_values=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)

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

             lowercase__       =				model(UpperCamelCase				)

             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					:					Optional[int]     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Any				):

             '''simple docstring'''







             lowercase__       =				20
             lowercase__       =				model_class_name(UpperCamelCase				)

             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]     ,		UpperCamelCase				)
             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=UpperCamelCase     ,		past_key_values=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)
             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=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)

             lowercase__       =				model(UpperCamelCase     ,		attention_mask=UpperCamelCase				)

             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 						__lowerCAmelCase		(lowercase_						,  unittest.TestCase						,  lowercase_  ):





      '''simple docstring'''






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



      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             lowercase__       =				FlaxOPTModelTester(self				)



      def    UpperCamelCase__	(self					:					List[Any]				):

             '''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(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					List[Any]				):

             '''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(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)






      @slow
      def    UpperCamelCase__	(self					:					List[str]				):

             '''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(UpperCamelCase				)
                    self.assertIsNotNone(UpperCamelCase				)






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





      '''simple docstring'''






      @slow
      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
             lowercase__       =				jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
             lowercase__       =				model(input_ids=UpperCamelCase				).last_hidden_state
             lowercase__       =				(1, 11, 512)
             self.assertEqual(output.shape     ,		UpperCamelCase				)
             lowercase__       =				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]     ,		UpperCamelCase     ,		atol=4E-2				)				)






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





      '''simple docstring'''






      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







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






      def    UpperCamelCase__	(self					:					str				):

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

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






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





      '''simple docstring'''






      @property
      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''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				):

             '''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(UpperCamelCase				)
             lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)

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

                    lowercase__       =				model.generate(UpperCamelCase     ,		max_length=10				)
                    lowercase__       =				generated_ids[0]

                    lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)
                    predicted_outputs += generated_string

             self.assertListEqual(UpperCamelCase     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple				):

             '''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(UpperCamelCase				)
             lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)

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

                    lowercase__       =				model.generate(UpperCamelCase     ,		max_length=10				)
                    lowercase__       =				generated_ids[0]

                    lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)
                    predicted_outputs += generated_string

             self.assertListEqual(UpperCamelCase     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					List[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(UpperCamelCase				)
             lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
             lowercase__       =				tokenizer(
                 [
                     '''Today is a beautiful day and I want to''',
                     '''In the city of''',
                 ]     ,		return_tensors='''jax'''     ,		padding=UpperCamelCase     ,		)

             lowercase__       =				jax.jit(model.generate				)

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

             lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)

             self.assertIsNotNone(UpperCamelCase     ,		UpperCamelCase				)






      def    UpperCamelCase__	(self					:					List[Any]				):

             '''simple docstring'''







             lowercase__       =				'''facebook/opt-350m'''

             lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
             lowercase__       =				FlaxOPTForCausalLM.from_pretrained(UpperCamelCase				)

             lowercase__       =				'''left'''

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

             lowercase__       =				tokenizer(UpperCamelCase     ,		return_tensors='''jax'''     ,		padding=UpperCamelCase				)
             lowercase__       =				inputs['''input_ids''']

             lowercase__       =				model.generate(input_ids=UpperCamelCase     ,		attention_mask=inputs['''attention_mask''']     ,		trace=UpperCamelCase				)

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

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

             lowercase__       =				tokenizer.batch_decode(outputs[0]     ,		skip_special_tokens=UpperCamelCase				)
             lowercase__       =				tokenizer.decode(output_non_padded[0][0]     ,		skip_special_tokens=UpperCamelCase				)
             lowercase__       =				tokenizer.decode(output_padded[0][0]     ,		skip_special_tokens=UpperCamelCase				)

             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(UpperCamelCase     ,		UpperCamelCase				)
             self.assertListEqual(UpperCamelCase     ,		[non_padded_sentence, padded_sentence]				)








'''simple docstring'''

import torch

from accelerate import Accelerator, DistributedType
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __enter__(self					:					List[Any]				):

             '''simple docstring'''







             if self.enabled:
                    lowercase__       =				self.model.no_sync()
                    self.model_sync_obj.__enter__()

             return self



      def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             if self.enabled:
                    # Average all models on exit
                    self._sync_and_avg_model_params()
                    self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):

             '''simple docstring'''







             if accelerator.distributed_type not in [
                 DistributedType.NO,
                 DistributedType.MULTI_CPU,
                 DistributedType.MULTI_GPU,
             ]:
                    raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
             lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
             lowercase__       =				0
             if self.enabled:
                    lowercase__       =				accelerator
                    lowercase__       =				model
                    lowercase__       =				local_sgd_steps



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             self.num_steps += 1
             if not self.enabled:
                    return

             if self.num_steps % self.local_sgd_steps == 0:
                    self._sync_and_avg_model_params()






      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             self.accelerator.wait_for_everyone()
             with self.accelerator.autocast():
                    for param in self.model.parameters():
                           lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)




