
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    			=					"platform"

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def 		_lowerCamelCase       (   _a			,   _a			,   _a=None			,   _a=None  ):
  """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 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__	,		a__=13	,		a__=7	,		a__=True	,		a__=False	,		a__=99	,		a__=16	,		a__=2	,		a__=4	,		a__=4	,		a__="gelu"	,		a__=0.1	,		a__=0.1	,		a__=20	,		a__=2	,		a__=1	,		a__=0	,		a__=16	,		a__=16	,		a__=0.02	,		):
    _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		):
    _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=a__	,		word_embed_proj_dim=self.word_embed_proj_dim	,		initializer_range=self.initializer_range	,		use_cache=a__	,		)
    _lowerCamelCase      =						prepare_opt_inputs_dict(a__	,		a__		)
    return config, inputs_dict


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_config_and_inputs()
    return config, inputs_dict


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    _lowerCamelCase      =						20
    _lowerCamelCase      =						model_class_name(a__		)

    _lowerCamelCase      =						inputs_dict['''input_ids''']
    _lowerCamelCase      =						inputs_dict['''attention_mask''']

    _lowerCamelCase      =						model.init_cache(input_ids.shape[0]	,		a__		)
    _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=a__	,		past_key_values=a__	,		position_ids=a__	,		)

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

    _lowerCamelCase      =						model(a__		)

    _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	,		a__	,		a__	,		a__		):
    _lowerCamelCase      =						20
    _lowerCamelCase      =						model_class_name(a__		)

    _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]	,		a__		)
    _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=a__	,		past_key_values=a__	,		position_ids=a__	,		)
    _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=a__	,		position_ids=a__	,		)

    _lowerCamelCase      =						model(a__	,		attention_mask=a__		)

    _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 		__magic_name__				( lowercase_						,unittest.TestCase						,lowercase_					):


  """simple docstring"""





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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						FlaxOPTModelTester(self		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _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(a__	,		a__	,		a__		)


  @slow
  def     _UpperCAmelCase  (					self		):
    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(a__		)
      self.assertIsNotNone(a__		)







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


  """simple docstring"""





  @slow
  def     _UpperCAmelCase  (					self		):
    _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=a__		).last_hidden_state
    _lowerCamelCase      =						(1, 11, 5_12)
    self.assertEqual(output.shape	,		a__		)
    _lowerCamelCase      =						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 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    super().setUp()
    _lowerCamelCase      =						'''facebook/opt-350m'''


  def     _UpperCAmelCase  (					self		):
    _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(a__	,		return_tensors='''jax'''	,		padding=a__	,		add_special_tokens=a__		)
    _lowerCamelCase      =						model(inputs.input_ids	,		attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
    _lowerCamelCase      =						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		)		)

    _lowerCamelCase      =						jax.jit(a__		)
    _lowerCamelCase      =						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 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  @property
  def     _UpperCAmelCase  (					self		):
    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		):
    _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(a__		)
    _lowerCamelCase      =						GPTaTokenizer.from_pretrained(a__		)

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

      _lowerCamelCase      =						model.generate(a__	,		max_length=10		)
      _lowerCamelCase      =						generated_ids[0]

      _lowerCamelCase      =						tokenizer.batch_decode(a__	,		skip_special_tokens=a__		)
      predicted_outputs += generated_string

    self.assertListEqual(a__	,		a__		)


  def     _UpperCAmelCase  (					self		):
    _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(a__		)
    _lowerCamelCase      =						GPTaTokenizer.from_pretrained(a__		)

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

      _lowerCamelCase      =						model.generate(a__	,		max_length=10		)
      _lowerCamelCase      =						generated_ids[0]

      _lowerCamelCase      =						tokenizer.batch_decode(a__	,		skip_special_tokens=a__		)
      predicted_outputs += generated_string

    self.assertListEqual(a__	,		a__		)


  def     _UpperCAmelCase  (					self		):
    _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(a__		)
    _lowerCamelCase      =						GPTaTokenizer.from_pretrained(a__		)
    _lowerCamelCase      =						tokenizer(
        [
            '''Today is a beautiful day and I want to''',
            '''In the city of''',
        ]	,		return_tensors='''jax'''	,		padding=a__	,		)

    _lowerCamelCase      =						jax.jit(model.generate		)

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

    _lowerCamelCase      =						tokenizer.batch_decode(a__	,		skip_special_tokens=a__		)

    self.assertIsNotNone(a__	,		a__		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						'''facebook/opt-350m'''

    _lowerCamelCase      =						GPTaTokenizer.from_pretrained(a__		)
    _lowerCamelCase      =						FlaxOPTForCausalLM.from_pretrained(a__		)

    _lowerCamelCase      =						'''left'''

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

    _lowerCamelCase      =						tokenizer(a__	,		return_tensors='''jax'''	,		padding=a__		)
    _lowerCamelCase      =						inputs['''input_ids''']

    _lowerCamelCase      =						model.generate(input_ids=a__	,		attention_mask=inputs['''attention_mask''']	,		trace=a__		)

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

    _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=a__	,		max_length=model.config.max_length - num_paddings		)

    _lowerCamelCase      =						tokenizer.batch_decode(outputs[0]	,		skip_special_tokens=a__		)
    _lowerCamelCase      =						tokenizer.decode(output_non_padded[0][0]	,		skip_special_tokens=a__		)
    _lowerCamelCase      =						tokenizer.decode(output_padded[0][0]	,		skip_special_tokens=a__		)

    _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(a__	,		a__		)
    self.assertListEqual(a__	,		[non_padded_sentence, padded_sentence]		)






class 		__magic_name__				:


  """simple docstring"""





  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = "_prev", "_data", "_next"


    def __init__(					self	,		a__	,		a__	,		a__		):
      _lowerCamelCase      =						link_p
      _lowerCamelCase      =						element
      _lowerCamelCase      =						link_n


    def     _UpperCAmelCase  (					self		):
      return (
          f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
      )







  def __init__(					self		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._trailer
    _lowerCamelCase      =						self._header
    _lowerCamelCase      =						0


  def __len__(					self		):
    return self._size


  def     _UpperCAmelCase  (					self		):
    return self.__len__() == 0


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # Create new_node by setting it's prev.link -> header
    # setting it's next.link -> trailer
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						new_node
    _lowerCamelCase      =						new_node
    self._size += 1
    return self


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						node._prev
    _lowerCamelCase      =						node._next

    _lowerCamelCase      =						successor
    _lowerCamelCase      =						predecessor
    self._size -= 1
    _lowerCamelCase      =						node._data
    _lowerCamelCase      =						_lowerCamelCase      =						_lowerCamelCase      =						None
    del node
    return temp







class 		__magic_name__				( _DoublyLinkedBase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._header._next._data


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._trailer._prev._data


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._header	,		a__	,		self._header._next		)


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._trailer._prev	,		a__	,		self._trailer		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._header._next		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._trailer._prev		)




