
"""simple docstring"""





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
    _a									=				'platform'

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=None    ):
   if attention_mask is None:
      UpperCAmelCase_   :   Optional[Any]     	=       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							,  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							,  ):

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







  def 			UpperCamelCase__   (						self    ):

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

     UpperCAmelCase_   :   Optional[int]     	=       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_							,  )
     UpperCAmelCase_   :   Any     	=       prepare_opt_inputs_dict(lowercase_							,  lowercase_    )
     return config, inputs_dict







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       self.prepare_config_and_inputs()
     return config, inputs_dict







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       20
     UpperCAmelCase_   :   str     	=       model_class_name(lowercase_    )

     UpperCAmelCase_   :   Optional[Any]     	=       inputs_dict["input_ids"]
     UpperCAmelCase_   :   Union[str, Any]     	=       inputs_dict["attention_mask"]

     UpperCAmelCase_   :   Tuple     	=       model.init_cache(input_ids.shape[0]							,  lowercase_    )
     UpperCAmelCase_   :   List[str]     	=       jnp.ones((input_ids.shape[0], max_length)							,  dtype="i4"    )

     UpperCAmelCase_   :   Optional[int]     	=       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1    )[None, :]							,  (input_ids.shape[0], input_ids.shape[-1] - 1)							,  )
     UpperCAmelCase_   :   Optional[Any]     	=       model(
         input_ids[:, :-1]							,  attention_mask=lowercase_							,  past_key_values=lowercase_							,  position_ids=lowercase_							,  )

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

     UpperCAmelCase_   :   List[str]     	=       model(lowercase_    )

     UpperCAmelCase_   :   List[str]     	=       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							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       20
     UpperCAmelCase_   :   Optional[int]     	=       model_class_name(lowercase_    )

     UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       (
         inputs_dict["input_ids"],
         inputs_dict["attention_mask"],
     )

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

     UpperCAmelCase_   :   List[Any]     	=       model.init_cache(input_ids.shape[0]							,  lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1    )[None, :]							,  (input_ids.shape[0], input_ids.shape[-1] - 1)							,  )

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

     UpperCAmelCase_   :   List[Any]     	=       model(lowercase_							,  attention_mask=lowercase_    )

     UpperCAmelCase_   :   Dict     	=       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'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						() if is_flax_available() else ()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       FlaxOPTModelTester(self    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_   :   Tuple     	=       model_class_name.from_pretrained("facebook/opt-125m"    )
        UpperCAmelCase_   :   Optional[Any]     	=       np.ones((1, 1)    ) * model.config.eos_token_id
        UpperCAmelCase_   :   List[str]     	=       model(lowercase_    )
        self.assertIsNotNone(lowercase_    )




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




  '''simple docstring'''







  @slow
  def 			UpperCamelCase__   (						self    ):

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




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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().setUp()
     UpperCAmelCase_   :   Tuple     	=       "facebook/opt-350m"







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       FlaxOPTForCausalLM.from_pretrained(self.path_model    )
     UpperCAmelCase_   :   List[Any]     	=       GPTaTokenizer.from_pretrained(self.path_model    )

     UpperCAmelCase_   :   Optional[int]     	=       [
         "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_   :   List[str]     	=       tokenizer(lowercase_							,  return_tensors="jax"							,  padding=lowercase_							,  add_special_tokens=lowercase_    )
     UpperCAmelCase_   :   Union[str, Any]     	=       model(inputs.input_ids							,  attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
     UpperCAmelCase_   :   Dict     	=       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(lowercase_							,  lowercase_							,  atol=4E-2    )    )

     UpperCAmelCase_   :   Union[str, Any]     	=       jax.jit(lowercase_    )
     UpperCAmelCase_   :   Dict     	=       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 ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """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    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       "facebook/opt-125m"

     UpperCAmelCase_   :   Tuple     	=       [
         "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_   :   List[str]     	=       []

     UpperCAmelCase_   :   Any     	=       FlaxOPTForCausalLM.from_pretrained(lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       GPTaTokenizer.from_pretrained(lowercase_    )

     for prompt in self.prompts:
        UpperCAmelCase_   :   Optional[int]     	=       tokenizer(lowercase_							,  return_tensors="jax"    ).input_ids

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

        UpperCAmelCase_   :   int     	=       tokenizer.batch_decode(lowercase_							,  skip_special_tokens=lowercase_    )
        predicted_outputs += generated_string

     self.assertListEqual(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       "facebook/opt-350m"

     UpperCAmelCase_   :   int     	=       [
         "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_   :   Union[str, Any]     	=       []
     UpperCAmelCase_   :   List[str]     	=       FlaxOPTForCausalLM.from_pretrained(lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       GPTaTokenizer.from_pretrained(lowercase_    )

     for prompt in self.prompts:
        UpperCAmelCase_   :   List[Any]     	=       tokenizer(lowercase_							,  return_tensors="jax"    ).input_ids

        UpperCAmelCase_   :   Union[str, Any]     	=       model.generate(lowercase_							,  max_length=10    )
        UpperCAmelCase_   :   Optional[Any]     	=       generated_ids[0]

        UpperCAmelCase_   :   Union[str, Any]     	=       tokenizer.batch_decode(lowercase_							,  skip_special_tokens=lowercase_    )
        predicted_outputs += generated_string

     self.assertListEqual(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       "facebook/opt-125m"
     UpperCAmelCase_   :   Dict     	=       [
         "Today is a beautiful day and I want to thank",
         "In the city of Rome Canaver Canaver Canaver Canaver",
     ]
     UpperCAmelCase_   :   Optional[int]     	=       FlaxOPTForCausalLM.from_pretrained(lowercase_    )
     UpperCAmelCase_   :   str     	=       GPTaTokenizer.from_pretrained(lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       tokenizer(
         [
             "Today is a beautiful day and I want to",
             "In the city of",
         ]							,  return_tensors="jax"							,  padding=lowercase_							,  )

     UpperCAmelCase_   :   Union[str, Any]     	=       jax.jit(model.generate    )

     UpperCAmelCase_   :   int     	=       jit_generate(inputs["input_ids"]							,  attention_mask=inputs["attention_mask"]    ).sequences

     UpperCAmelCase_   :   str     	=       tokenizer.batch_decode(lowercase_							,  skip_special_tokens=lowercase_    )

     self.assertIsNotNone(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       "facebook/opt-350m"

     UpperCAmelCase_   :   Any     	=       GPTaTokenizer.from_pretrained(lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       FlaxOPTForCausalLM.from_pretrained(lowercase_    )

     UpperCAmelCase_   :   Optional[int]     	=       "left"

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

     UpperCAmelCase_   :   str     	=       tokenizer(lowercase_							,  return_tensors="jax"							,  padding=lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       inputs["input_ids"]

     UpperCAmelCase_   :   List[str]     	=       model.generate(input_ids=lowercase_							,  attention_mask=inputs["attention_mask"]							,  trace=lowercase_    )

     UpperCAmelCase_   :   Dict     	=       tokenizer(sentences[0]							,  return_tensors="jax"    ).input_ids
     UpperCAmelCase_   :   List[str]     	=       model.generate(input_ids=lowercase_    )

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

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

     UpperCAmelCase_   :   Union[str, Any]     	=       [
         "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]    )




"""simple docstring"""





import math
from timeit import timeit







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Any     	=       abs(__lowerCamelCase    )
   while True:
      UpperCAmelCase_   :   Optional[int]     	=       n // 10
      digits += 1
      if n == 0:
         break
   return digits







def       __a	(						__lowerCamelCase    ):
   return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase    ),						10    ) + 1    )







def       __a	(						__lowerCamelCase    ):
   return len(str(abs(__lowerCamelCase    )    )    )







def       __a	(						):
   from collections.abc import Callable

   def benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    ) -> None:
      UpperCAmelCase_   :   Optional[Any]     	=       f"""{func.__name__}({value})"""
      UpperCAmelCase_   :   Optional[int]     	=       timeit(f"""__main__.{call}""",						setup="import __main__"    )
      print(f"""{call}: {func(__lowerCamelCase    )} -- {timing} seconds"""    )

   for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
      for func in (num_digits, num_digits_fast, num_digits_faster):
         benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    )
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


