




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 			_UpperCamelCase (_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Optional[int]   ,	_lowerCamelCase							:     str=None   ,	_lowerCamelCase							:     Any=None	)->   Tuple:




     '''simple docstring'''





     if attention_mask is None:
          __snake_case    					=  np.where(input_ids != config.pad_token_id   ,	1   ,	0	)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }





@require_flax
class     lowerCAmelCase   :


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=13    ,  __SCREAMING_SNAKE_CASE=7    ,  __SCREAMING_SNAKE_CASE=True    ,  __SCREAMING_SNAKE_CASE=False    ,  __SCREAMING_SNAKE_CASE=99    ,  __SCREAMING_SNAKE_CASE=16    ,  __SCREAMING_SNAKE_CASE=2    ,  __SCREAMING_SNAKE_CASE=4    ,  __SCREAMING_SNAKE_CASE=4    ,  __SCREAMING_SNAKE_CASE="gelu"    ,  __SCREAMING_SNAKE_CASE=0.1    ,  __SCREAMING_SNAKE_CASE=0.1    ,  __SCREAMING_SNAKE_CASE=20    ,  __SCREAMING_SNAKE_CASE=2    ,  __SCREAMING_SNAKE_CASE=1    ,  __SCREAMING_SNAKE_CASE=0    ,  __SCREAMING_SNAKE_CASE=16    ,  __SCREAMING_SNAKE_CASE=16    ,  __SCREAMING_SNAKE_CASE=0.02    ,  )      ->  Optional[int]:



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




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

            __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=__SCREAMING_SNAKE_CASE    ,  word_embed_proj_dim=self.word_embed_proj_dim    ,  initializer_range=self.initializer_range    ,  use_cache=__SCREAMING_SNAKE_CASE    ,  )
            __snake_case    					=  prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            return config, inputs_dict


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



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  20
            __snake_case    					=  model_class_name(__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  inputs_dict['''input_ids''']
            __snake_case    					=  inputs_dict['''attention_mask''']

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

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

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

            __snake_case    					=  model(__SCREAMING_SNAKE_CASE  )

            __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       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  20
            __snake_case    					=  model_class_name(__SCREAMING_SNAKE_CASE  )

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

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

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

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

            __snake_case    					=  model(__SCREAMING_SNAKE_CASE    ,  attention_mask=__SCREAMING_SNAKE_CASE  )

            __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     lowerCAmelCase   ( UpperCamelCase_				,   unittest.TestCase				,   UpperCamelCase_):
       __lowercase  :      List[str]        =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       __lowercase  :      int        =			() if is_flax_available() else ()


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  FlaxOPTModelTester(self  )


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



            '''simple docstring'''




            __snake_case					,    __snake_case    					=  self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )


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



            '''simple docstring'''




            __snake_case					,    __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(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )




       @slow
       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case    					=  model_class_name.from_pretrained('''facebook/opt-125m'''  )
                 __snake_case    					=  np.ones((1, 1)  ) * model.config.eos_token_id
                 __snake_case    					=  model(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )





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


       @slow
       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




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





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


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



            '''simple docstring'''




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




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



            '''simple docstring'''




            __snake_case    					=  FlaxOPTForCausalLM.from_pretrained(self.path_model  )
            __snake_case    					=  GPTaTokenizer.from_pretrained(self.path_model  )

            __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
            __snake_case    					=  tokenizer(__SCREAMING_SNAKE_CASE    ,  return_tensors='''jax'''    ,  padding=__SCREAMING_SNAKE_CASE    ,  add_special_tokens=__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  model(inputs.input_ids    ,  attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
            __snake_case    					=  jnp.array(
                [
                    [1.3_851, -13.8923, -10.5229, -10.7533, -0.2_309, -10.2384, -0.5_365, -9.0_947, -5.1_670],
                    [-4.7_073, -10.6276, -3.9_415, -21.5242, -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, -14.1650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                    [6.4_783, -1.9_913, -10.7926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
                ]  )
            self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  atol=4E-2  )  )

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





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


       @property
       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''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       lowerCAmelCase      (   self  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  '''facebook/opt-125m'''

            __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''',
            ]

            __snake_case    					=  []

            __snake_case    					=  FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )

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

                 __snake_case    					=  model.generate(__SCREAMING_SNAKE_CASE    ,  max_length=10  )
                 __snake_case    					=  generated_ids[0]

                 __snake_case    					=  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )
                 predicted_outputs += generated_string

            self.assertListEqual(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )


       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  '''facebook/opt-350m'''

            __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''',
            ]

            __snake_case    					=  []
            __snake_case    					=  FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )

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

                 __snake_case    					=  model.generate(__SCREAMING_SNAKE_CASE    ,  max_length=10  )
                 __snake_case    					=  generated_ids[0]

                 __snake_case    					=  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )
                 predicted_outputs += generated_string

            self.assertListEqual(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )


       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




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

            __snake_case    					=  jax.jit(model.generate  )

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

            __snake_case    					=  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )




       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  '''facebook/opt-350m'''

            __snake_case    					=  GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  '''left'''

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

            __snake_case    					=  tokenizer(__SCREAMING_SNAKE_CASE    ,  return_tensors='''jax'''    ,  padding=__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  inputs['''input_ids''']

            __snake_case    					=  model.generate(input_ids=__SCREAMING_SNAKE_CASE    ,  attention_mask=inputs['''attention_mask''']    ,  trace=__SCREAMING_SNAKE_CASE  )

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

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

            __snake_case    					=  tokenizer.batch_decode(outputs[0]    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  tokenizer.decode(output_non_padded[0][0]    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  tokenizer.decode(output_padded[0][0]    ,  skip_special_tokens=__SCREAMING_SNAKE_CASE  )

            __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(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            self.assertListEqual(__SCREAMING_SNAKE_CASE    ,  [non_padded_sentence, padded_sentence]  )












'''simple docstring'''



from transformers import ProcessorMixin





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			'''AutoFeatureExtractor'''
       __lowercase  :      List[Any]        =			'''AutoTokenizer'''






