

"""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
      __UpperCAmelCase	=			'platform'

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase=None							,      __UpperCamelCase=None ):
 '''simple docstring'''





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







@require_flax
class        __lowercase     :

  def __init__(					self						:     List[str]   ,A						:     Dict   ,A						:     Any=13   ,A						:     Union[str, Any]=7   ,A						:     Dict=True   ,A						:     List[str]=False   ,A						:     Tuple=99   ,A						:     Tuple=16   ,A						:     Any=2   ,A						:     Any=4   ,A						:     Optional[Any]=4   ,A						:     str="gelu"   ,A						:     List[Any]=0.1   ,A						:     Optional[int]=0.1   ,A						:     List[str]=20   ,A						:     Optional[int]=2   ,A						:     Optional[int]=1   ,A						:     Optional[Any]=0   ,A						:     Optional[int]=16   ,A						:     Dict=16   ,A						:     str=0.0_2   ,):






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

  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size      )   ,3   ,self.vocab_size      )
   UpperCAmelCase__   :					Union[str, Any]   				=   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=A   ,word_embed_proj_dim=self.word_embed_proj_dim   ,initializer_range=self.initializer_range   ,use_cache=A   ,)
   UpperCAmelCase__   :					List[Any]   				=   prepare_opt_inputs_dict(A   ,A      )
   return config, inputs_dict

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__				, UpperCAmelCase__   :					int   				=   self.prepare_config_and_inputs()
   return config, inputs_dict

  def 				__lowercase    (					self						:     Tuple   ,A						:     Optional[int]   ,A						:     List[Any]   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   20
   UpperCAmelCase__   :					List[Any]   				=   model_class_name(A      )

   UpperCAmelCase__   :					List[str]   				=   inputs_dict["""input_ids"""]
   UpperCAmelCase__   :					int   				=   inputs_dict["""attention_mask"""]

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

   UpperCAmelCase__   :					str   				=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1      )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)
   UpperCAmelCase__   :					Any   				=   model(
       input_ids[:, :-1]   ,attention_mask=A   ,past_key_values=A   ,position_ids=A   ,)

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

   UpperCAmelCase__   :					int   				=   model(A      )

   UpperCAmelCase__   :					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 				__lowercase    (					self						:     Any   ,A						:     Optional[Any]   ,A						:     Tuple   ,A						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   20
   UpperCAmelCase__   :					str   				=   model_class_name(A      )

   UpperCAmelCase__				, UpperCAmelCase__   :					Any   				=   (
       inputs_dict["""input_ids"""],
       inputs_dict["""attention_mask"""],
   )

   UpperCAmelCase__   :					Tuple   				=   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]   ,A      )
   UpperCAmelCase__   :					str   				=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1      )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)

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

   UpperCAmelCase__   :					List[Any]   				=   model(A   ,attention_mask=A      )

   UpperCAmelCase__   :					Optional[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}"      )







@require_flax
class        __lowercase     (      __lowerCamelCase		,     unittest.TestCase		,     __lowerCamelCase   ):
  snake_case_			    =    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  snake_case_			    =    () if is_flax_available() else ()

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   FlaxOPTModelTester(self      )

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__				, UpperCAmelCase__   :					Tuple   				=   self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward(A   ,A   ,A      )

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__				, UpperCAmelCase__   :					Tuple   				=   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(A   ,A   ,A      )




  @slow
  def 				__lowercase    (					self						:     Union[str, Any]      ):






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







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

  @slow
  def 				__lowercase    (					self						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   FlaxOPTModel.from_pretrained("""facebook/opt-350m"""      )
   UpperCAmelCase__   :					str   				=   jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]      )
   UpperCAmelCase__   :					int   				=   model(input_ids=A      ).last_hidden_state
   UpperCAmelCase__   :					Any   				=   (1, 11, 512)
   self.assertEqual(output.shape   ,A      )
   UpperCAmelCase__   :					int   				=   jnp.array(
       [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]      )
   self.assertTrue(jnp.allclose(output[:, :3, :3]   ,A   ,atol=4e-2      )      )







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

  def 				__lowercase    (					self						:     Dict      ):






   '''simple docstring'''
   super().setUp()
   UpperCAmelCase__   :					Union[str, Any]   				=   """facebook/opt-350m"""




  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   FlaxOPTForCausalLM.from_pretrained(self.path_model      )
   UpperCAmelCase__   :					Optional[Any]   				=   GPTaTokenizer.from_pretrained(self.path_model      )

   UpperCAmelCase__   :					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
   UpperCAmelCase__   :					Dict   				=   tokenizer(A   ,return_tensors="""jax"""   ,padding=A   ,add_special_tokens=A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   model(inputs.input_ids   ,attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
   UpperCAmelCase__   :					Optional[int]   				=   jnp.array(
       [
           [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
           [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
           [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
           [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
       ]      )
   self.assertTrue(jnp.allclose(A   ,A   ,atol=4e-2      )      )

   UpperCAmelCase__   :					List[Any]   				=   jax.jit(A      )
   UpperCAmelCase__   :					Optional[int]   				=   model(inputs.input_ids   ,attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
   self.assertTrue(jnp.allclose(A   ,A   ,atol=4e-2      )      )







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

  @property
  def 				__lowercase    (					self						:     List[str]      ):






   '''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 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   """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__   :					Any   				=   []

   UpperCAmelCase__   :					str   				=   FlaxOPTForCausalLM.from_pretrained(A      )
   UpperCAmelCase__   :					Optional[Any]   				=   GPTaTokenizer.from_pretrained(A      )

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

    UpperCAmelCase__   :					Optional[int]   				=   model.generate(A   ,max_length=10      )
    UpperCAmelCase__   :					Tuple   				=   generated_ids[0]

    UpperCAmelCase__   :					str   				=   tokenizer.batch_decode(A   ,skip_special_tokens=A      )
    predicted_outputs += generated_string

   self.assertListEqual(A   ,A      )

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   """facebook/opt-350m"""

   UpperCAmelCase__   :					str   				=   [
       """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__   :					str   				=   []
   UpperCAmelCase__   :					List[Any]   				=   FlaxOPTForCausalLM.from_pretrained(A      )
   UpperCAmelCase__   :					Any   				=   GPTaTokenizer.from_pretrained(A      )

   for prompt in self.prompts:
    UpperCAmelCase__   :					Union[str, Any]   				=   tokenizer(A   ,return_tensors="""jax"""      ).input_ids

    UpperCAmelCase__   :					Any   				=   model.generate(A   ,max_length=10      )
    UpperCAmelCase__   :					Any   				=   generated_ids[0]

    UpperCAmelCase__   :					List[Any]   				=   tokenizer.batch_decode(A   ,skip_special_tokens=A      )
    predicted_outputs += generated_string

   self.assertListEqual(A   ,A      )

  def 				__lowercase    (					self						:     Optional[int]      ):






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

   UpperCAmelCase__   :					Optional[int]   				=   jax.jit(model.generate      )

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

   UpperCAmelCase__   :					int   				=   tokenizer.batch_decode(A   ,skip_special_tokens=A      )

   self.assertIsNotNone(A   ,A      )




  def 				__lowercase    (					self						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   """facebook/opt-350m"""

   UpperCAmelCase__   :					Union[str, Any]   				=   GPTaTokenizer.from_pretrained(A      )
   UpperCAmelCase__   :					Any   				=   FlaxOPTForCausalLM.from_pretrained(A      )

   UpperCAmelCase__   :					Optional[Any]   				=   """left"""

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

   UpperCAmelCase__   :					Tuple   				=   tokenizer(A   ,return_tensors="""jax"""   ,padding=A      )
   UpperCAmelCase__   :					Dict   				=   inputs["""input_ids"""]

   UpperCAmelCase__   :					Tuple   				=   model.generate(input_ids=A   ,attention_mask=inputs["""attention_mask"""]   ,trace=A      )

   UpperCAmelCase__   :					Optional[int]   				=   tokenizer(sentences[0]   ,return_tensors="""jax"""      ).input_ids
   UpperCAmelCase__   :					int   				=   model.generate(input_ids=A      )

   UpperCAmelCase__   :					Dict   				=   inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
   UpperCAmelCase__   :					int   				=   tokenizer(sentences[1]   ,return_tensors="""jax"""      ).input_ids
   UpperCAmelCase__   :					Optional[int]   				=   model.generate(input_ids=A   ,max_length=model.config.max_length - num_paddings      )

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

   UpperCAmelCase__   :					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(A   ,A      )
   self.assertListEqual(A   ,[non_padded_sentence, padded_sentence]      )








"""simple docstring"""




def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   1
 UpperCAmelCase__   :					Union[str, Any]   				=   0
 UpperCAmelCase__   :					List[str]   				=   1
 UpperCAmelCase__   :					Any   				=   1
 UpperCAmelCase__   :					Optional[int]   				=   3
 UpperCAmelCase__   :					Any   				=   3
 UpperCAmelCase__   :					Optional[Any]   				=   limit
 UpperCAmelCase__   :					Dict   				=   0

 UpperCAmelCase__   :					int   				=   """"""
 while counter != decimal + 1:
  if 4 * q + r - t < n * t:
   result += str(__UpperCamelCase )
   if counter == 0:
    result += "."

   if decimal == counter:
    break

   counter += 1
   UpperCAmelCase__   :					Union[str, Any]   				=   10 * (r - n * t)
   UpperCAmelCase__   :					int   				=   ((10 * (3 * q + r)) // t) - 10 * n
   q *= 10
   UpperCAmelCase__   :					str   				=   nr
  else:
   UpperCAmelCase__   :					List[str]   				=   (2 * q + r) * l
   UpperCAmelCase__   :					Union[str, Any]   				=   (q * (7 * k) + 2 + (r * l)) // (t * l)
   q *= k
   t *= l
   l += 2
   k += 1
   UpperCAmelCase__   :					Optional[Any]   				=   nn
   UpperCAmelCase__   :					str   				=   nr
 return result
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 print(F"{calculate_pi(50 ) = }" )
 import doctest

 doctest.testmod()


if __name__ == "__main__":
      main()





