





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_   =   "platform"

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=None  )			->						Optional[int]:
       if attention_mask is None:
              _snake_case      :							Optional[int]     				=						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     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=False     ,  lowercase_=99     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=4     ,  lowercase_=4     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=20     ,  lowercase_=2     ,  lowercase_=1     ,  lowercase_=0     ,  lowercase_=16     ,  lowercase_=16     ,  lowercase_=0.02     ,  )      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							Any     				=						seq_length
             _snake_case      :							Any     				=						is_training
             _snake_case      :							Tuple     				=						use_labels
             _snake_case      :							Dict     				=						vocab_size
             _snake_case      :							str     				=						hidden_size
             _snake_case      :							Tuple     				=						num_hidden_layers
             _snake_case      :							Dict     				=						num_attention_heads
             _snake_case      :							str     				=						intermediate_size
             _snake_case      :							Any     				=						hidden_act
             _snake_case      :							Tuple     				=						hidden_dropout_prob
             _snake_case      :							List[str]     				=						attention_probs_dropout_prob
             _snake_case      :							Optional[Any]     				=						max_position_embeddings
             _snake_case      :							int     				=						eos_token_id
             _snake_case      :							Any     				=						pad_token_id
             _snake_case      :							List[Any]     				=						bos_token_id
             _snake_case      :							Dict     				=						embed_dim
             _snake_case      :							List[Any]     				=						word_embed_proj_dim
             _snake_case      :							str     				=						initializer_range
             _snake_case      :							Tuple     				=						False



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)     ,  3     ,  self.vocab_size					)
             _snake_case      :							Union[str, Any]     				=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,  dtype=np.intaa					))     ,  -1					)

             _snake_case      :							List[str]     				=						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=lowercase_     ,  word_embed_proj_dim=self.word_embed_proj_dim     ,  initializer_range=self.initializer_range     ,  use_cache=lowercase_     ,  )
             _snake_case      :							Optional[int]     				=						prepare_opt_inputs_dict(lowercase_     ,  lowercase_					)
             return config, inputs_dict



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							str     				=						self.prepare_config_and_inputs()
             return config, inputs_dict



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							str     				=						20
             _snake_case      :							Any     				=						model_class_name(lowercase_					)

             _snake_case      :							Tuple     				=						inputs_dict['''input_ids''']
             _snake_case      :							List[Any]     				=						inputs_dict['''attention_mask''']

             _snake_case      :							str     				=						model.init_cache(input_ids.shape[0]     ,  lowercase_					)
             _snake_case      :							str     				=						jnp.ones((input_ids.shape[0], max_length)     ,  dtype='''i4'''					)

             _snake_case      :							int     				=						jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1					)[None, :]     ,  (input_ids.shape[0], input_ids.shape[-1] - 1)     ,  )
             _snake_case      :							List[str]     				=						model(
                 input_ids[:, :-1]     ,  attention_mask=lowercase_     ,  past_key_values=lowercase_     ,  position_ids=lowercase_     ,  )

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

             _snake_case      :							List[Any]     				=						model(lowercase_					)

             _snake_case      :							List[Any]     				=						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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						20
             _snake_case      :							Optional[int]     				=						model_class_name(lowercase_					)

             _snake_case				,    _snake_case      :							Optional[Any]     				=						(
                 inputs_dict['''input_ids'''],
                 inputs_dict['''attention_mask'''],
             )

             _snake_case      :							List[str]     				=						jnp.concatenate(
                 [
                     attention_mask,
                     jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
                 ]     ,  axis=-1     ,  )

             _snake_case      :							int     				=						model.init_cache(input_ids.shape[0]     ,  lowercase_					)
             _snake_case      :							str     				=						jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1					)[None, :]     ,  (input_ids.shape[0], input_ids.shape[-1] - 1)     ,  )

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

             _snake_case      :							Optional[Any]     				=						model(lowercase_     ,  attention_mask=lowercase_					)

             _snake_case      :							Optional[int]     				=						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    (__UpperCAmelCase       ,unittest.TestCase       ,__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      _SCREAMING_SNAKE_CASE					=							() if is_flax_available() else ()



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						FlaxOPTModelTester(self					)



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case				,    _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(lowercase_     ,  lowercase_     ,  lowercase_					)



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



             '''simple docstring'''

             _snake_case				,    _snake_case      :							int     				=						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(lowercase_     ,  lowercase_     ,  lowercase_					)



      @slow
      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             for model_class_name in self.all_model_classes:
                    _snake_case      :							int     				=						model_class_name.from_pretrained('''facebook/opt-125m'''					)
                    _snake_case      :							List[str]     				=						np.ones((1, 1)					) * model.config.eos_token_id
                    _snake_case      :							Tuple     				=						model(lowercase_					)
                    self.assertIsNotNone(lowercase_					)


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



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



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
             _snake_case      :							List[Any]     				=						jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
             _snake_case      :							Tuple     				=						model(input_ids=lowercase_					).last_hidden_state
             _snake_case      :							int     				=						(1, 11, 512)
             self.assertEqual(output.shape     ,  lowercase_					)
             _snake_case      :							List[Any]     				=						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]     ,  lowercase_     ,  atol=4E-2					)					)


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



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



             '''simple docstring'''

             super().setUp()
             _snake_case      :							Optional[int]     				=						'''facebook/opt-350m'''



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



             '''simple docstring'''

             _snake_case      :							Any     				=						FlaxOPTForCausalLM.from_pretrained(self.path_model					)
             _snake_case      :							List[Any]     				=						GPTaTokenizer.from_pretrained(self.path_model					)

             _snake_case      :							Dict     				=						[
                 '''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
             _snake_case      :							Optional[Any]     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''     ,  padding=lowercase_     ,  add_special_tokens=lowercase_					)
             _snake_case      :							Optional[int]     				=						model(inputs.input_ids     ,  attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
             _snake_case      :							Optional[int]     				=						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(lowercase_     ,  lowercase_     ,  atol=4E-2					)					)

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


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



      @property
      def    __a (					self					)      ->   Optional[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    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Dict     				=						'''facebook/opt-125m'''

             _snake_case      :							List[Any]     				=						[
                 '''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''',
             ]

             _snake_case      :							Union[str, Any]     				=						[]

             _snake_case      :							Dict     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
             _snake_case      :							int     				=						GPTaTokenizer.from_pretrained(lowercase_					)

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

                    _snake_case      :							Optional[int]     				=						model.generate(lowercase_     ,  max_length=10					)
                    _snake_case      :							Dict     				=						generated_ids[0]

                    _snake_case      :							str     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    predicted_outputs += generated_string

             self.assertListEqual(lowercase_     ,  lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							int     				=						'''facebook/opt-350m'''

             _snake_case      :							List[Any]     				=						[
                 '''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''',
             ]

             _snake_case      :							Union[str, Any]     				=						[]
             _snake_case      :							Any     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
             _snake_case      :							Optional[Any]     				=						GPTaTokenizer.from_pretrained(lowercase_					)

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

                    _snake_case      :							int     				=						model.generate(lowercase_     ,  max_length=10					)
                    _snake_case      :							Any     				=						generated_ids[0]

                    _snake_case      :							Tuple     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    predicted_outputs += generated_string

             self.assertListEqual(lowercase_     ,  lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						'''facebook/opt-125m'''
             _snake_case      :							Any     				=						[
                 '''Today is a beautiful day and I want to thank''',
                 '''In the city of Rome Canaver Canaver Canaver Canaver''',
             ]
             _snake_case      :							Tuple     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
             _snake_case      :							Any     				=						GPTaTokenizer.from_pretrained(lowercase_					)
             _snake_case      :							Any     				=						tokenizer(
                 [
                     '''Today is a beautiful day and I want to''',
                     '''In the city of''',
                 ]     ,  return_tensors='''jax'''     ,  padding=lowercase_     ,  )

             _snake_case      :							Any     				=						jax.jit(model.generate					)

             _snake_case      :							List[str]     				=						jit_generate(inputs['''input_ids''']     ,  attention_mask=inputs['''attention_mask''']					).sequences

             _snake_case      :							List[Any]     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

             self.assertIsNotNone(lowercase_     ,  lowercase_					)



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							str     				=						'''facebook/opt-350m'''

             _snake_case      :							Optional[Any]     				=						GPTaTokenizer.from_pretrained(lowercase_					)
             _snake_case      :							Union[str, Any]     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)

             _snake_case      :							int     				=						'''left'''

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

             _snake_case      :							str     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''     ,  padding=lowercase_					)
             _snake_case      :							List[str]     				=						inputs['''input_ids''']

             _snake_case      :							Union[str, Any]     				=						model.generate(input_ids=lowercase_     ,  attention_mask=inputs['''attention_mask''']     ,  trace=lowercase_					)

             _snake_case      :							List[Any]     				=						tokenizer(sentences[0]     ,  return_tensors='''jax'''					).input_ids
             _snake_case      :							int     				=						model.generate(input_ids=lowercase_					)

             _snake_case      :							List[str]     				=						inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
             _snake_case      :							Any     				=						tokenizer(sentences[1]     ,  return_tensors='''jax'''					).input_ids
             _snake_case      :							Optional[Any]     				=						model.generate(input_ids=lowercase_     ,  max_length=model.config.max_length - num_paddings					)

             _snake_case      :							int     				=						tokenizer.batch_decode(outputs[0]     ,  skip_special_tokens=lowercase_					)
             _snake_case      :							List[str]     				=						tokenizer.decode(output_non_padded[0][0]     ,  skip_special_tokens=lowercase_					)
             _snake_case      :							List[Any]     				=						tokenizer.decode(output_padded[0][0]     ,  skip_special_tokens=lowercase_					)

             _snake_case      :							List[str]     				=						[
                 '''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(lowercase_     ,  lowercase_					)
             self.assertListEqual(lowercase_     ,  [non_padded_sentence, padded_sentence]					)













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






