





"""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
       __UpperCamelCase			:		int		    =   '''platform'''

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  int=None   ,			_UpperCAmelCase :  Union[str, Any]=None       ):
 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 	a				:



 def __init__(			self							,    _snake_case							,    _snake_case=13							,    _snake_case=7							,    _snake_case=True							,    _snake_case=False							,    _snake_case=99							,    _snake_case=16							,    _snake_case=2							,    _snake_case=4							,    _snake_case=4							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=20							,    _snake_case=2							,    _snake_case=1							,    _snake_case=0							,    _snake_case=16							,    _snake_case=16							,    _snake_case=0.02							,    ):







  """simple docstring"""






  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 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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=_snake_case							,    word_embed_proj_dim=self.word_embed_proj_dim							,    initializer_range=self.initializer_range							,    use_cache=_snake_case							,    )
  lowerCAmelCase  							=      prepare_opt_inputs_dict(_snake_case							,    _snake_case			)
  return config, inputs_dict



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      20
  lowerCAmelCase  							=      model_class_name(_snake_case			)

  lowerCAmelCase  							=      inputs_dict['input_ids']
  lowerCAmelCase  							=      inputs_dict['attention_mask']

  lowerCAmelCase  							=      model.init_cache(input_ids.shape[0]							,    _snake_case			)
  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=_snake_case							,    past_key_values=_snake_case							,    position_ids=_snake_case							,    )

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

  lowerCAmelCase  							=      model(_snake_case			)

  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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      20
  lowerCAmelCase  							=      model_class_name(_snake_case			)

  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]							,    _snake_case			)
  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=_snake_case							,    past_key_values=_snake_case							,    position_ids=_snake_case							,    )
  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=_snake_case							,    position_ids=_snake_case							,    )

  lowerCAmelCase  							=      model(_snake_case							,    attention_mask=_snake_case			)

  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 	a				(					a__					,      unittest.TestCase					,      a__   ):
 snake_case__    	=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 snake_case__    	=    () if is_flax_available() else ()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      FlaxOPTModelTester(self			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward(_snake_case							,    _snake_case							,    _snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    _snake_case							,    _snake_case			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case			)
   self.assertIsNotNone(_snake_case			)







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



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      FlaxOPTModel.from_pretrained('facebook/opt-350m'			)
  lowerCAmelCase  							=      jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
  lowerCAmelCase  							=      model(input_ids=_snake_case			).last_hidden_state
  lowerCAmelCase  							=      (1, 11, 5_12)
  self.assertEqual(output.shape							,    _snake_case			)
  lowerCAmelCase  							=      jnp.array(
      [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]			)
  self.assertTrue(jnp.allclose(output[:, :3, :3]							,    _snake_case							,    atol=4E-2			)			)







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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    return_tensors='jax'							,    padding=_snake_case							,    add_special_tokens=_snake_case			)
  lowerCAmelCase  							=      model(inputs.input_ids							,    attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
  lowerCAmelCase  							=      jnp.array(
      [
          [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
          [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
          [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
          [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
      ]			)
  self.assertTrue(jnp.allclose(_snake_case							,    _snake_case							,    atol=4E-2			)			)

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







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



 @property
 def 					UpperCamelCase__   (			self			):







  """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			):







  """simple docstring"""






  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(_snake_case			)
  lowerCAmelCase  							=      GPTaTokenizer.from_pretrained(_snake_case			)

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

   lowerCAmelCase  							=      model.generate(_snake_case							,    max_length=10			)
   lowerCAmelCase  							=      generated_ids[0]

   lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case							,    skip_special_tokens=_snake_case			)
   predicted_outputs += generated_string

  self.assertListEqual(_snake_case							,    _snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case			)
  lowerCAmelCase  							=      GPTaTokenizer.from_pretrained(_snake_case			)

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

   lowerCAmelCase  							=      model.generate(_snake_case							,    max_length=10			)
   lowerCAmelCase  							=      generated_ids[0]

   lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case							,    skip_special_tokens=_snake_case			)
   predicted_outputs += generated_string

  self.assertListEqual(_snake_case							,    _snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case			)
  lowerCAmelCase  							=      GPTaTokenizer.from_pretrained(_snake_case			)
  lowerCAmelCase  							=      tokenizer(
      [
          'Today is a beautiful day and I want to',
          'In the city of',
      ]							,    return_tensors='jax'							,    padding=_snake_case							,    )

  lowerCAmelCase  							=      jax.jit(model.generate			)

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

  lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case							,    skip_special_tokens=_snake_case			)

  self.assertIsNotNone(_snake_case							,    _snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      'facebook/opt-350m'

  lowerCAmelCase  							=      GPTaTokenizer.from_pretrained(_snake_case			)
  lowerCAmelCase  							=      FlaxOPTForCausalLM.from_pretrained(_snake_case			)

  lowerCAmelCase  							=      'left'

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

  lowerCAmelCase  							=      tokenizer(_snake_case							,    return_tensors='jax'							,    padding=_snake_case			)
  lowerCAmelCase  							=      inputs['input_ids']

  lowerCAmelCase  							=      model.generate(input_ids=_snake_case							,    attention_mask=inputs['attention_mask']							,    trace=_snake_case			)

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

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

  lowerCAmelCase  							=      tokenizer.batch_decode(outputs[0]							,    skip_special_tokens=_snake_case			)
  lowerCAmelCase  							=      tokenizer.decode(output_non_padded[0][0]							,    skip_special_tokens=_snake_case			)
  lowerCAmelCase  							=      tokenizer.decode(output_padded[0][0]							,    skip_special_tokens=_snake_case			)

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












"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase			:		List[Any]		    =   {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase			:		Dict		    =   [
           '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''YosoForMaskedLM''',
           '''YosoForMultipleChoice''',
           '''YosoForQuestionAnswering''',
           '''YosoForSequenceClassification''',
           '''YosoForTokenClassification''',
           '''YosoLayer''',
           '''YosoModel''',
           '''YosoPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_yoso import (
                  YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  YosoForMaskedLM,
                  YosoForMultipleChoice,
                  YosoForQuestionAnswering,
                  YosoForSequenceClassification,
                  YosoForTokenClassification,
                  YosoLayer,
                  YosoModel,
                  YosoPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase			:		Any		    =   _LazyModule(__name__, globals()['''__file__'''], _import_structure)





