

'''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
  _a			:	Optional[Any]								=  'platform'

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Dict=None    ,				SCREAMING_SNAKE_CASE :				Tuple=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     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=1_3				,	a_=7				,	a_=True				,	a_=False				,	a_=9_9				,	a_=1_6				,	a_=2				,	a_=4				,	a_=4				,	a_="gelu"				,	a_=0.1				,	a_=0.1				,	a_=2_0				,	a_=2				,	a_=1				,	a_=0				,	a_=1_6				,	a_=1_6				,	a_=0.02				,	)							->			Tuple:


    """simple docstring"""
    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   snake_case_     (       self       )							->			int:


    """simple docstring"""
    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=a_				,	word_embed_proj_dim=self.word_embed_proj_dim				,	initializer_range=self.initializer_range				,	use_cache=a_				,	)
    UpperCAmelCase  							=		prepare_opt_inputs_dict(a_				,	a_       )
    return config, inputs_dict
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		self.prepare_config_and_inputs()
    return config, inputs_dict
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		2_0
    UpperCAmelCase  							=		model_class_name(a_       )

    UpperCAmelCase  							=		inputs_dict['input_ids']
    UpperCAmelCase  							=		inputs_dict['attention_mask']

    UpperCAmelCase  							=		model.init_cache(input_ids.shape[0]				,	a_       )
    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=a_				,	past_key_values=a_				,	position_ids=a_				,	)

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

    UpperCAmelCase  							=		model(a_       )

    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   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		2_0
    UpperCAmelCase  							=		model_class_name(a_       )

    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]				,	a_       )
    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=a_				,	past_key_values=a_				,	position_ids=a_				,	)
    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=a_				,	position_ids=a_				,	)

    UpperCAmelCase  							=		model(a_				,	attention_mask=a_       )

    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     lowercase_		(						a	,    unittest.TestCase	,    a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Tuple      					=				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  __lowerCAmelCase   :	int      					=				() if is_flax_available() else ()
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		FlaxOPTModelTester(self       )
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(a_				,	a_				,	a_       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    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(a_				,	a_				,	a_       )





  @slow
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    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(a_       )
      self.assertIsNotNone(a_       )




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

  '''simple docstring'''





  @slow
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		FlaxOPTModel.from_pretrained('facebook/opt-350m'       )
    UpperCAmelCase  							=		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]]       )
    UpperCAmelCase  							=		model(input_ids=a_       ).last_hidden_state
    UpperCAmelCase  							=		(1, 1_1, 5_1_2)
    self.assertEqual(output.shape				,	a_       )
    UpperCAmelCase  							=		jnp.array(
        [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]       )
    self.assertTrue(jnp.allclose(output[:, :3, :3]				,	a_				,	atol=4E-2       )       )




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

  '''simple docstring'''





  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    super().setUp()
    UpperCAmelCase  							=		'facebook/opt-350m'





  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    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(a_				,	return_tensors='jax'				,	padding=a_				,	add_special_tokens=a_       )
    UpperCAmelCase  							=		model(inputs.input_ids				,	attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
    UpperCAmelCase  							=		jnp.array(
        [
            [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
            [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
            [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
            [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
        ]       )
    self.assertTrue(jnp.allclose(a_				,	a_				,	atol=4E-2       )       )

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




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

  '''simple docstring'''





  @property
  def   snake_case_     (       self       )							->			List[Any]:


    """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   snake_case_     (       self       )							->			str:


    """simple docstring"""
    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(a_       )
    UpperCAmelCase  							=		GPTaTokenizer.from_pretrained(a_       )

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

      UpperCAmelCase  							=		model.generate(a_				,	max_length=1_0       )
      UpperCAmelCase  							=		generated_ids[0]

      UpperCAmelCase  							=		tokenizer.batch_decode(a_				,	skip_special_tokens=a_       )
      predicted_outputs += generated_string

    self.assertListEqual(a_				,	a_       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    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(a_       )
    UpperCAmelCase  							=		GPTaTokenizer.from_pretrained(a_       )

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

      UpperCAmelCase  							=		model.generate(a_				,	max_length=1_0       )
      UpperCAmelCase  							=		generated_ids[0]

      UpperCAmelCase  							=		tokenizer.batch_decode(a_				,	skip_special_tokens=a_       )
      predicted_outputs += generated_string

    self.assertListEqual(a_				,	a_       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    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(a_       )
    UpperCAmelCase  							=		GPTaTokenizer.from_pretrained(a_       )
    UpperCAmelCase  							=		tokenizer(
        [
            'Today is a beautiful day and I want to',
            'In the city of',
        ]				,	return_tensors='jax'				,	padding=a_				,	)

    UpperCAmelCase  							=		jax.jit(model.generate       )

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

    UpperCAmelCase  							=		tokenizer.batch_decode(a_				,	skip_special_tokens=a_       )

    self.assertIsNotNone(a_				,	a_       )





  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		'facebook/opt-350m'

    UpperCAmelCase  							=		GPTaTokenizer.from_pretrained(a_       )
    UpperCAmelCase  							=		FlaxOPTForCausalLM.from_pretrained(a_       )

    UpperCAmelCase  							=		'left'

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

    UpperCAmelCase  							=		tokenizer(a_				,	return_tensors='jax'				,	padding=a_       )
    UpperCAmelCase  							=		inputs['input_ids']

    UpperCAmelCase  							=		model.generate(input_ids=a_				,	attention_mask=inputs['attention_mask']				,	trace=a_       )

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

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

    UpperCAmelCase  							=		tokenizer.batch_decode(outputs[0]				,	skip_special_tokens=a_       )
    UpperCAmelCase  							=		tokenizer.decode(output_non_padded[0][0]				,	skip_special_tokens=a_       )
    UpperCAmelCase  							=		tokenizer.decode(output_padded[0][0]				,	skip_special_tokens=a_       )

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





'''simple docstring'''




from collections import namedtuple

_a			:	int								=  namedtuple('from_to', 'from_ to')

_a			:	Dict								=  {
    'atm': from_to(1, 1),
    'pascal': from_to(0.0000098, 101_325),
    'bar': from_to(0.986923, 1.01325),
    'kilopascal': from_to(0.00986923, 101.325),
    'megapascal': from_to(9.86923, 0.101325),
    'psi': from_to(0.068046, 14.6959),
    'inHg': from_to(0.0334211, 29.9213),
    'torr': from_to(0.00131579, 760),
}


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				float    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str  ):
  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


