






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 		A_			(		lowercase_					,       lowercase_					,       lowercase_=None					,       lowercase_=None	)							->List[str]:







 """simple docstring"""

 if attention_mask is None:
  SCREAMING_SNAKE_CASE							  =						np.where(input_ids != config.pad_token_id					,       1					,       0	)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }
@require_flax
class 					a_:



     """simple docstring"""



     def __init__(						self			:					List[str]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					str=1_3       ,       lowerCAmelCase__			:					Union[str, Any]=7       ,       lowerCAmelCase__			:					str=True       ,       lowerCAmelCase__			:					Optional[Any]=False       ,       lowerCAmelCase__			:					List[str]=9_9       ,       lowerCAmelCase__			:					Any=1_6       ,       lowerCAmelCase__			:					List[Any]=2       ,       lowerCAmelCase__			:					Any=4       ,       lowerCAmelCase__			:					Optional[Any]=4       ,       lowerCAmelCase__			:					Union[str, Any]="gelu"       ,       lowerCAmelCase__			:					List[Any]=0.1       ,       lowerCAmelCase__			:					str=0.1       ,       lowerCAmelCase__			:					int=2_0       ,       lowerCAmelCase__			:					str=2       ,       lowerCAmelCase__			:					Any=1       ,       lowerCAmelCase__			:					List[str]=0       ,       lowerCAmelCase__			:					str=1_6       ,       lowerCAmelCase__			:					str=1_6       ,       lowerCAmelCase__			:					List[str]=0.02       ,       )    ->					Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						parent
      SCREAMING_SNAKE_CASE							  =						batch_size
      SCREAMING_SNAKE_CASE							  =						seq_length
      SCREAMING_SNAKE_CASE							  =						is_training
      SCREAMING_SNAKE_CASE							  =						use_labels
      SCREAMING_SNAKE_CASE							  =						vocab_size
      SCREAMING_SNAKE_CASE							  =						hidden_size
      SCREAMING_SNAKE_CASE							  =						num_hidden_layers
      SCREAMING_SNAKE_CASE							  =						num_attention_heads
      SCREAMING_SNAKE_CASE							  =						intermediate_size
      SCREAMING_SNAKE_CASE							  =						hidden_act
      SCREAMING_SNAKE_CASE							  =						hidden_dropout_prob
      SCREAMING_SNAKE_CASE							  =						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE							  =						max_position_embeddings
      SCREAMING_SNAKE_CASE							  =						eos_token_id
      SCREAMING_SNAKE_CASE							  =						pad_token_id
      SCREAMING_SNAKE_CASE							  =						bos_token_id
      SCREAMING_SNAKE_CASE							  =						embed_dim
      SCREAMING_SNAKE_CASE							  =						word_embed_proj_dim
      SCREAMING_SNAKE_CASE							  =						initializer_range
      SCREAMING_SNAKE_CASE							  =						False





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size)       ,       3       ,       self.vocab_size)
      SCREAMING_SNAKE_CASE							  =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,       dtype=np.intaa))       ,       -1)

      SCREAMING_SNAKE_CASE							  =						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=lowerCAmelCase__       ,       word_embed_proj_dim=self.word_embed_proj_dim       ,       initializer_range=self.initializer_range       ,       use_cache=lowerCAmelCase__       ,       )
      SCREAMING_SNAKE_CASE							  =						prepare_opt_inputs_dict(lowerCAmelCase__       ,       lowerCAmelCase__)
      return config, inputs_dict





     def   __UpperCamelCase		(						self			:					Dict)    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self.prepare_config_and_inputs()
      return config, inputs_dict





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					Union[str, Any])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						2_0
      SCREAMING_SNAKE_CASE							  =						model_class_name(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						inputs_dict['input_ids']
      SCREAMING_SNAKE_CASE							  =						inputs_dict['attention_mask']

      SCREAMING_SNAKE_CASE							  =						model.init_cache(input_ids.shape[0]       ,       lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						jnp.ones((input_ids.shape[0], max_length)       ,       dtype='i4')

      SCREAMING_SNAKE_CASE							  =						jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1)       ,       )
      SCREAMING_SNAKE_CASE							  =						model(
          input_ids[:, :-1]       ,       attention_mask=lowerCAmelCase__       ,       past_key_values=lowerCAmelCase__       ,       position_ids=lowerCAmelCase__       ,       )

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

      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						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			:					Any       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					int)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						2_0
      SCREAMING_SNAKE_CASE							  =						model_class_name(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						(
          inputs_dict['input_ids'],
          inputs_dict['attention_mask'],
      )

      SCREAMING_SNAKE_CASE							  =						jnp.concatenate(
          [
              attention_mask,
              jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
          ]       ,       axis=-1       ,       )

      SCREAMING_SNAKE_CASE							  =						model.init_cache(input_ids.shape[0]       ,       lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,       (input_ids.shape[0], input_ids.shape[-1] - 1)       ,       )

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

      SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__       ,       attention_mask=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						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_(						lowercase__	,    unittest.TestCase	,    lowercase__					):



     """simple docstring"""



     __snake_case						:							Dict       			=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     __snake_case						:							Optional[Any]       			=() if is_flax_available() else ()





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						FlaxOPTModelTester(self)





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					int)    ->					int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__)





     @slow
     def   __UpperCamelCase		(						self			:					Optional[int])    ->					str:

      """simple docstring"""
      for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE							  =						model_class_name.from_pretrained('facebook/opt-125m')
       SCREAMING_SNAKE_CASE							  =						np.ones((1, 1)) * model.config.eos_token_id
       SCREAMING_SNAKE_CASE							  =						model(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

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



     """simple docstring"""



     @slow
     def   __UpperCamelCase		(						self			:					int)    ->					int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						FlaxOPTModel.from_pretrained('facebook/opt-350m')
      SCREAMING_SNAKE_CASE							  =						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]])
      SCREAMING_SNAKE_CASE							  =						model(input_ids=lowerCAmelCase__).last_hidden_state
      SCREAMING_SNAKE_CASE							  =						(1, 1_1, 5_1_2)
      self.assertEqual(output.shape       ,       lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						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]       ,       lowerCAmelCase__       ,       atol=4e-2))

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



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					List[str])    ->					Optional[int]:

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





     def   __UpperCamelCase		(						self			:					List[Any])    ->					List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						FlaxOPTForCausalLM.from_pretrained(self.path_model)
      SCREAMING_SNAKE_CASE							  =						GPTaTokenizer.from_pretrained(self.path_model)

      SCREAMING_SNAKE_CASE							  =						[
          '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
      SCREAMING_SNAKE_CASE							  =						tokenizer(lowerCAmelCase__       ,       return_tensors='jax'       ,       padding=lowerCAmelCase__       ,       add_special_tokens=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						model(inputs.input_ids       ,       attention_mask=inputs.attention_mask)[0].mean(axis=-1)
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__       ,       lowerCAmelCase__       ,       atol=4e-2))

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

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



     """simple docstring"""



     @property
     def   __UpperCamelCase		(						self			:					List[str])    ->					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   __UpperCamelCase		(						self			:					List[str])    ->					Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						'facebook/opt-125m'

      SCREAMING_SNAKE_CASE							  =						[
          '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',
      ]

      SCREAMING_SNAKE_CASE							  =						[]

      SCREAMING_SNAKE_CASE							  =						FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						GPTaTokenizer.from_pretrained(lowerCAmelCase__)

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

       SCREAMING_SNAKE_CASE							  =						model.generate(lowerCAmelCase__       ,       max_length=1_0)
       SCREAMING_SNAKE_CASE							  =						generated_ids[0]

       SCREAMING_SNAKE_CASE							  =						tokenizer.batch_decode(lowerCAmelCase__       ,       skip_special_tokens=lowerCAmelCase__)
       predicted_outputs += generated_string

      self.assertListEqual(lowerCAmelCase__       ,       lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					int)    ->					str:

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

      SCREAMING_SNAKE_CASE							  =						[
          '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',
      ]

      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						GPTaTokenizer.from_pretrained(lowerCAmelCase__)

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

       SCREAMING_SNAKE_CASE							  =						model.generate(lowerCAmelCase__       ,       max_length=1_0)
       SCREAMING_SNAKE_CASE							  =						generated_ids[0]

       SCREAMING_SNAKE_CASE							  =						tokenizer.batch_decode(lowerCAmelCase__       ,       skip_special_tokens=lowerCAmelCase__)
       predicted_outputs += generated_string

      self.assertListEqual(lowerCAmelCase__       ,       lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					int)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						'facebook/opt-125m'
      SCREAMING_SNAKE_CASE							  =						[
          'Today is a beautiful day and I want to thank',
          'In the city of Rome Canaver Canaver Canaver Canaver',
      ]
      SCREAMING_SNAKE_CASE							  =						FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						GPTaTokenizer.from_pretrained(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						tokenizer(
          [
              'Today is a beautiful day and I want to',
              'In the city of',
          ]       ,       return_tensors='jax'       ,       padding=lowerCAmelCase__       ,       )

      SCREAMING_SNAKE_CASE							  =						jax.jit(model.generate)

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

      SCREAMING_SNAKE_CASE							  =						tokenizer.batch_decode(lowerCAmelCase__       ,       skip_special_tokens=lowerCAmelCase__)

      self.assertIsNotNone(lowerCAmelCase__       ,       lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					int)    ->					Any:

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

      SCREAMING_SNAKE_CASE							  =						GPTaTokenizer.from_pretrained(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						'left'

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

      SCREAMING_SNAKE_CASE							  =						tokenizer(lowerCAmelCase__       ,       return_tensors='jax'       ,       padding=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						inputs['input_ids']

      SCREAMING_SNAKE_CASE							  =						model.generate(input_ids=lowerCAmelCase__       ,       attention_mask=inputs['attention_mask']       ,       trace=lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE							  =						inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
      SCREAMING_SNAKE_CASE							  =						tokenizer(sentences[1]       ,       return_tensors='jax').input_ids
      SCREAMING_SNAKE_CASE							  =						model.generate(input_ids=lowerCAmelCase__       ,       max_length=model.config.max_length - num_paddings)

      SCREAMING_SNAKE_CASE							  =						tokenizer.batch_decode(outputs[0]       ,       skip_special_tokens=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						tokenizer.decode(output_non_padded[0][0]       ,       skip_special_tokens=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						tokenizer.decode(output_padded[0][0]       ,       skip_special_tokens=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						[
          '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(lowerCAmelCase__       ,       lowerCAmelCase__)
      self.assertListEqual(lowerCAmelCase__       ,       [non_padded_sentence, padded_sentence])












def 		A_			(		lowercase_	)							->int:







 """simple docstring"""

 if not isinstance(lowercase_					,       lowercase_	):
  SCREAMING_SNAKE_CASE							  =						f'''Input value of [number={number}] must be an integer'''
  raise TypeError(lowercase_	)
 if number < 1:
  raise ValueError('Input must be a positive integer'	)
 return number * (2 * number - 1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




