




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
   snake_case__ :						List[str]     =			'''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase=None   ,     _lowerCAmelCase=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     snake_case  :


   '''simple docstring'''







   def __init__(					self     :	Any						,    lowerCamelCase_     :	Any						,    lowerCamelCase_     :	Optional[Any]=13						,    lowerCamelCase_     :	Dict=7						,    lowerCamelCase_     :	Any=True						,    lowerCamelCase_     :	List[Any]=False						,    lowerCamelCase_     :	List[Any]=99						,    lowerCamelCase_     :	Optional[Any]=16						,    lowerCamelCase_     :	Tuple=2						,    lowerCamelCase_     :	Dict=4						,    lowerCamelCase_     :	Tuple=4						,    lowerCamelCase_     :	Tuple="gelu"						,    lowerCamelCase_     :	Optional[int]=0.1						,    lowerCamelCase_     :	Optional[Any]=0.1						,    lowerCamelCase_     :	Tuple=20						,    lowerCamelCase_     :	Dict=2						,    lowerCamelCase_     :	Optional[int]=1						,    lowerCamelCase_     :	Optional[Any]=0						,    lowerCamelCase_     :	Tuple=16						,    lowerCamelCase_     :	int=16						,    lowerCamelCase_     :	Dict=0.02						,    )					->Dict:
     '''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 	UpperCAmelCase       (					self     :	Any			)					->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=lowerCamelCase_						,    word_embed_proj_dim=self.word_embed_proj_dim						,    initializer_range=self.initializer_range						,    use_cache=lowerCamelCase_						,    )
     UpperCAmelCase__    						=   prepare_opt_inputs_dict(lowerCamelCase_						,    lowerCamelCase_			)
     return config, inputs_dict




   def 	UpperCAmelCase       (					self     :	Tuple			)					->Optional[int]:
     '''simple docstring'''







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




   def 	UpperCAmelCase       (					self     :	Union[str, Any]						,    lowerCamelCase_     :	str						,    lowerCamelCase_     :	str						,    lowerCamelCase_     :	List[Any]			)					->List[str]:
     '''simple docstring'''







     UpperCAmelCase__    						=   20
     UpperCAmelCase__    						=   model_class_name(lowerCamelCase_			)

     UpperCAmelCase__    						=   inputs_dict["""input_ids"""]
     UpperCAmelCase__    						=   inputs_dict["""attention_mask"""]

     UpperCAmelCase__    						=   model.init_cache(input_ids.shape[0]						,    lowerCamelCase_			)
     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=lowerCamelCase_						,    past_key_values=lowerCamelCase_						,    position_ids=lowerCamelCase_						,    )

     UpperCAmelCase__    						=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,    dtype="""i4"""			)
     UpperCAmelCase__    						=   model(
         input_ids[:, -1:]						,    attention_mask=lowerCamelCase_						,    past_key_values=outputs_cache.past_key_values						,    position_ids=lowerCamelCase_						,    )

     UpperCAmelCase__    						=   model(lowerCamelCase_			)

     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 	UpperCAmelCase       (					self     :	str						,    lowerCamelCase_     :	Union[str, Any]						,    lowerCamelCase_     :	Dict						,    lowerCamelCase_     :	Tuple			)					->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   20
     UpperCAmelCase__    						=   model_class_name(lowerCamelCase_			)

     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]						,    lowerCamelCase_			)
     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=lowerCamelCase_						,    past_key_values=lowerCamelCase_						,    position_ids=lowerCamelCase_						,    )
     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=lowerCamelCase_						,    position_ids=lowerCamelCase_						,    )

     UpperCAmelCase__    						=   model(lowerCamelCase_						,    attention_mask=lowerCamelCase_			)

     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     snake_case  (	_snake_case ,  unittest.TestCase ,  _snake_case							):


   '''simple docstring'''







   UpperCamelCase__	:       Optional[Any]     =      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   UpperCamelCase__	:       List[str]     =      () if is_flax_available() else ()




   def 	UpperCAmelCase       (					self     :	Tuple			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   FlaxOPTModelTester(self			)




   def 	UpperCAmelCase       (					self     :	Optional[int]			)					->List[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(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Tuple:
     '''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(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)







   @slow
   def 	UpperCAmelCase       (					self     :	Dict			)					->Union[str, Any]:
     '''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(lowerCamelCase_			)
       self.assertIsNotNone(lowerCamelCase_			)





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


   '''simple docstring'''







   @slow
   def 	UpperCAmelCase       (					self     :	Any			)					->Union[str, Any]:
     '''simple docstring'''







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





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


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->int:
     '''simple docstring'''







     super().setUp()
     UpperCAmelCase__    						=   """facebook/opt-350m"""







   def 	UpperCAmelCase       (					self     :	List[Any]			)					->Dict:
     '''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(lowerCamelCase_						,    return_tensors="""jax"""						,    padding=lowerCamelCase_						,    add_special_tokens=lowerCamelCase_			)
     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(lowerCamelCase_						,    lowerCamelCase_						,    atol=4E-2			)			)

     UpperCAmelCase__    						=   jax.jit(lowerCamelCase_			)
     UpperCAmelCase__    						=   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     snake_case  (	unittest.TestCase							):


   '''simple docstring'''







   @property
   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Union[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     :	str			)					->Any:
     '''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(lowerCamelCase_			)
     UpperCAmelCase__    						=   GPTaTokenizer.from_pretrained(lowerCamelCase_			)

     for prompt in self.prompts:
       UpperCAmelCase__    						=   tokenizer(lowerCamelCase_						,    return_tensors="""jax"""			).input_ids

       UpperCAmelCase__    						=   model.generate(lowerCamelCase_						,    max_length=10			)
       UpperCAmelCase__    						=   generated_ids[0]

       UpperCAmelCase__    						=   tokenizer.batch_decode(lowerCamelCase_						,    skip_special_tokens=lowerCamelCase_			)
       predicted_outputs += generated_string

     self.assertListEqual(lowerCamelCase_						,    lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Dict			)					->Any:
     '''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(lowerCamelCase_			)
     UpperCAmelCase__    						=   GPTaTokenizer.from_pretrained(lowerCamelCase_			)

     for prompt in self.prompts:
       UpperCAmelCase__    						=   tokenizer(lowerCamelCase_						,    return_tensors="""jax"""			).input_ids

       UpperCAmelCase__    						=   model.generate(lowerCamelCase_						,    max_length=10			)
       UpperCAmelCase__    						=   generated_ids[0]

       UpperCAmelCase__    						=   tokenizer.batch_decode(lowerCamelCase_						,    skip_special_tokens=lowerCamelCase_			)
       predicted_outputs += generated_string

     self.assertListEqual(lowerCamelCase_						,    lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Optional[int]			)					->Tuple:
     '''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(lowerCamelCase_			)
     UpperCAmelCase__    						=   GPTaTokenizer.from_pretrained(lowerCamelCase_			)
     UpperCAmelCase__    						=   tokenizer(
         [
             """Today is a beautiful day and I want to""",
             """In the city of""",
         ]						,    return_tensors="""jax"""						,    padding=lowerCamelCase_						,    )

     UpperCAmelCase__    						=   jax.jit(model.generate			)

     UpperCAmelCase__    						=   jit_generate(inputs["""input_ids"""]						,    attention_mask=inputs["""attention_mask"""]			).sequences

     UpperCAmelCase__    						=   tokenizer.batch_decode(lowerCamelCase_						,    skip_special_tokens=lowerCamelCase_			)

     self.assertIsNotNone(lowerCamelCase_						,    lowerCamelCase_			)







   def 	UpperCAmelCase       (					self     :	Optional[int]			)					->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   """facebook/opt-350m"""

     UpperCAmelCase__    						=   GPTaTokenizer.from_pretrained(lowerCamelCase_			)
     UpperCAmelCase__    						=   FlaxOPTForCausalLM.from_pretrained(lowerCamelCase_			)

     UpperCAmelCase__    						=   """left"""

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

     UpperCAmelCase__    						=   tokenizer(lowerCamelCase_						,    return_tensors="""jax"""						,    padding=lowerCamelCase_			)
     UpperCAmelCase__    						=   inputs["""input_ids"""]

     UpperCAmelCase__    						=   model.generate(input_ids=lowerCamelCase_						,    attention_mask=inputs["""attention_mask"""]						,    trace=lowerCamelCase_			)

     UpperCAmelCase__    						=   tokenizer(sentences[0]						,    return_tensors="""jax"""			).input_ids
     UpperCAmelCase__    						=   model.generate(input_ids=lowerCamelCase_			)

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

     UpperCAmelCase__    						=   tokenizer.batch_decode(outputs[0]						,    skip_special_tokens=lowerCamelCase_			)
     UpperCAmelCase__    						=   tokenizer.decode(output_non_padded[0][0]						,    skip_special_tokens=lowerCamelCase_			)
     UpperCAmelCase__    						=   tokenizer.decode(output_padded[0][0]						,    skip_special_tokens=lowerCamelCase_			)

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










def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   5381
  for x in s:
    UpperCAmelCase__    						=   ((hash_value << 5) + hash_value) + ord(_lowerCAmelCase      )
  return hash_value & 0xffffffff




