
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
  __SCREAMING_SNAKE_CASE			:   Optional[int]         =     "platform"

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def     UpperCAmelCase__							(       __magic_name__						:     Optional[Any]							,  __magic_name__						:     List[Any]							,  __magic_name__						:     List[Any]=None							,  __magic_name__						:     List[Any]=None  ):


     '''simple docstring'''

     if attention_mask is None:
          lowerCAmelCase		:					Optional[Any]  	=     np.where(input_ids != config.pad_token_id							,  1							,  0  )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }


@require_flax
class 		__magic_name__   :





  def __init__(							self    :	str						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	Tuple=1_3						,  lowerCamelCase__    :	Optional[Any]=7						,  lowerCamelCase__    :	int=True						,  lowerCamelCase__    :	List[Any]=False						,  lowerCamelCase__    :	int=9_9						,  lowerCamelCase__    :	Tuple=1_6						,  lowerCamelCase__    :	Dict=2						,  lowerCamelCase__    :	int=4						,  lowerCamelCase__    :	List[Any]=4						,  lowerCamelCase__    :	Tuple="gelu"						,  lowerCamelCase__    :	Optional[Any]=0.1						,  lowerCamelCase__    :	int=0.1						,  lowerCamelCase__    :	Optional[Any]=2_0						,  lowerCamelCase__    :	Tuple=2						,  lowerCamelCase__    :	List[str]=1						,  lowerCamelCase__    :	List[str]=0						,  lowerCamelCase__    :	Optional[Any]=1_6						,  lowerCamelCase__    :	Optional[int]=1_6						,  lowerCamelCase__    :	Optional[int]=0.0_2						,  ):
       lowerCAmelCase		:					Union[str, Any]  	=     parent
       lowerCAmelCase		:					Any  	=     batch_size
       lowerCAmelCase		:					Optional[int]  	=     seq_length
       lowerCAmelCase		:					Tuple  	=     is_training
       lowerCAmelCase		:					List[Any]  	=     use_labels
       lowerCAmelCase		:					List[Any]  	=     vocab_size
       lowerCAmelCase		:					int  	=     hidden_size
       lowerCAmelCase		:					Union[str, Any]  	=     num_hidden_layers
       lowerCAmelCase		:					str  	=     num_attention_heads
       lowerCAmelCase		:					Tuple  	=     intermediate_size
       lowerCAmelCase		:					List[Any]  	=     hidden_act
       lowerCAmelCase		:					Union[str, Any]  	=     hidden_dropout_prob
       lowerCAmelCase		:					Dict  	=     attention_probs_dropout_prob
       lowerCAmelCase		:					Optional[int]  	=     max_position_embeddings
       lowerCAmelCase		:					int  	=     eos_token_id
       lowerCAmelCase		:					Optional[int]  	=     pad_token_id
       lowerCAmelCase		:					Tuple  	=     bos_token_id
       lowerCAmelCase		:					List[str]  	=     embed_dim
       lowerCAmelCase		:					Tuple  	=     word_embed_proj_dim
       lowerCAmelCase		:					Dict  	=     initializer_range
       lowerCAmelCase		:					Optional[int]  	=     False





  def       _A							(							self    :	Union[str, Any]				):
       lowerCAmelCase		:					Union[str, Any]  	=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,  self.vocab_size				)						,  3						,  self.vocab_size				)
       lowerCAmelCase		:					List[Any]  	=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,  dtype=np.intaa				))						,  -1				)

       lowerCAmelCase		:					Union[str, Any]  	=     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=__UpperCamelCase						,  word_embed_proj_dim=self.word_embed_proj_dim						,  initializer_range=self.initializer_range						,  use_cache=__UpperCamelCase						,  )
       lowerCAmelCase		:					Union[str, Any]  	=     prepare_opt_inputs_dict(__UpperCamelCase						,  __UpperCamelCase				)
       return config, inputs_dict





  def       _A							(							self    :	Tuple				):
       lowerCAmelCase		:					Tuple  	=     self.prepare_config_and_inputs()
       return config, inputs_dict





  def       _A							(							self    :	Any						,  lowerCamelCase__    :	Dict						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	int				):
       lowerCAmelCase		:					List[str]  	=     2_0
       lowerCAmelCase		:					Optional[Any]  	=     model_class_name(__UpperCamelCase				)

       lowerCAmelCase		:					List[Any]  	=     inputs_dict['''input_ids''']
       lowerCAmelCase		:					Any  	=     inputs_dict['''attention_mask''']

       lowerCAmelCase		:					List[str]  	=     model.init_cache(input_ids.shape[0]						,  __UpperCamelCase				)
       lowerCAmelCase		:					int  	=     jnp.ones((input_ids.shape[0], max_length)						,  dtype='''i4'''				)

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

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

       lowerCAmelCase		:					Dict  	=     model(__UpperCamelCase				)

       lowerCAmelCase		:					Union[str, 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}'''				)





  def       _A							(							self    :	Any						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
       lowerCAmelCase		:					int  	=     2_0
       lowerCAmelCase		:					Dict  	=     model_class_name(__UpperCamelCase				)

       lowerCAmelCase		:					Union[str, Any]  	=     (
           inputs_dict['''input_ids'''],
           inputs_dict['''attention_mask'''],
       )

       lowerCAmelCase		:					Optional[Any]  	=     jnp.concatenate(
           [
               attention_mask,
               jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])				),
           ]						,  axis=-1						,  )

       lowerCAmelCase		:					Tuple  	=     model.init_cache(input_ids.shape[0]						,  __UpperCamelCase				)
       lowerCAmelCase		:					Union[str, Any]  	=     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)						,  )

       lowerCAmelCase		:					Dict  	=     model(
           input_ids[:, :-1]						,  attention_mask=__UpperCamelCase						,  past_key_values=__UpperCamelCase						,  position_ids=__UpperCamelCase						,  )
       lowerCAmelCase		:					Union[str, Any]  	=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,  dtype='''i4'''				)
       lowerCAmelCase		:					str  	=     model(
           input_ids[:, -1:]						,  past_key_values=outputs_cache.past_key_values						,  attention_mask=__UpperCamelCase						,  position_ids=__UpperCamelCase						,  )

       lowerCAmelCase		:					Optional[Any]  	=     model(__UpperCamelCase						,  attention_mask=__UpperCamelCase				)

       lowerCAmelCase		:					int  	=     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 		__magic_name__   (						UpperCamelCase_,				unittest.TestCase,				UpperCamelCase_    ):
  _lowerCAmelCase		=	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  _lowerCAmelCase		=	() if is_flax_available() else ()





  def       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					Union[str, Any]  	=     FlaxOPTModelTester(self				)





  def       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					Optional[int]  	=     self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(__UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)





  def       _A							(							self    :	Union[str, Any]				):
       lowerCAmelCase		:					Dict  	=     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(__UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)





  @slow
  def       _A							(							self    :	Optional[Any]				):


       for model_class_name in self.all_model_classes:
            lowerCAmelCase		:					List[str]  	=     model_class_name.from_pretrained('''facebook/opt-125m'''				)
            lowerCAmelCase		:					int  	=     np.ones((1, 1)				) * model.config.eos_token_id
            lowerCAmelCase		:					Any  	=     model(__UpperCamelCase				)
            self.assertIsNotNone(__UpperCamelCase				)



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





  @slow
  def       _A							(							self    :	List[str]				):
       lowerCAmelCase		:					Dict  	=     FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
       lowerCAmelCase		:					Optional[int]  	=     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]]				)
       lowerCAmelCase		:					List[str]  	=     model(input_ids=__UpperCamelCase				).last_hidden_state
       lowerCAmelCase		:					str  	=     (1, 1_1, 5_1_2)
       self.assertEqual(output.shape						,  __UpperCamelCase				)
       lowerCAmelCase		:					Any  	=     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]						,  __UpperCamelCase						,  atol=4E-2				)				)



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





  def       _A							(							self    :	Union[str, Any]				):
       super().setUp()
       lowerCAmelCase		:					Union[str, Any]  	=     '''facebook/opt-350m'''





  def       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					List[Any]  	=     FlaxOPTForCausalLM.from_pretrained(self.path_model				)
       lowerCAmelCase		:					Union[str, Any]  	=     GPTaTokenizer.from_pretrained(self.path_model				)

       lowerCAmelCase		:					Union[str, Any]  	=     [
           '''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
       lowerCAmelCase		:					int  	=     tokenizer(__UpperCamelCase						,  return_tensors='''jax'''						,  padding=__UpperCamelCase						,  add_special_tokens=__UpperCamelCase				)
       lowerCAmelCase		:					Optional[Any]  	=     model(inputs.input_ids						,  attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
       lowerCAmelCase		:					Optional[Any]  	=     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(__UpperCamelCase						,  __UpperCamelCase						,  atol=4E-2				)				)

       lowerCAmelCase		:					Union[str, Any]  	=     jax.jit(__UpperCamelCase				)
       lowerCAmelCase		:					str  	=     model(inputs.input_ids						,  attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
       self.assertTrue(jnp.allclose(__UpperCamelCase						,  __UpperCamelCase						,  atol=4E-2				)				)



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





  @property
  def       _A							(							self    :	Optional[int]				):
       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       _A							(							self    :	Tuple				):
       lowerCAmelCase		:					int  	=     '''facebook/opt-125m'''

       lowerCAmelCase		:					Dict  	=     [
           '''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''',
       ]

       lowerCAmelCase		:					Union[str, Any]  	=     []

       lowerCAmelCase		:					Optional[int]  	=     FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase				)
       lowerCAmelCase		:					Optional[int]  	=     GPTaTokenizer.from_pretrained(__UpperCamelCase				)

       for prompt in self.prompts:
            lowerCAmelCase		:					str  	=     tokenizer(__UpperCamelCase						,  return_tensors='''jax'''				).input_ids

            lowerCAmelCase		:					Optional[int]  	=     model.generate(__UpperCamelCase						,  max_length=1_0				)
            lowerCAmelCase		:					Optional[int]  	=     generated_ids[0]

            lowerCAmelCase		:					int  	=     tokenizer.batch_decode(__UpperCamelCase						,  skip_special_tokens=__UpperCamelCase				)
            predicted_outputs += generated_string

       self.assertListEqual(__UpperCamelCase						,  __UpperCamelCase				)





  def       _A							(							self    :	str				):
       lowerCAmelCase		:					Optional[int]  	=     '''facebook/opt-350m'''

       lowerCAmelCase		:					List[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''',
       ]

       lowerCAmelCase		:					int  	=     []
       lowerCAmelCase		:					Union[str, Any]  	=     FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase				)
       lowerCAmelCase		:					Tuple  	=     GPTaTokenizer.from_pretrained(__UpperCamelCase				)

       for prompt in self.prompts:
            lowerCAmelCase		:					Any  	=     tokenizer(__UpperCamelCase						,  return_tensors='''jax'''				).input_ids

            lowerCAmelCase		:					Union[str, Any]  	=     model.generate(__UpperCamelCase						,  max_length=1_0				)
            lowerCAmelCase		:					int  	=     generated_ids[0]

            lowerCAmelCase		:					Optional[Any]  	=     tokenizer.batch_decode(__UpperCamelCase						,  skip_special_tokens=__UpperCamelCase				)
            predicted_outputs += generated_string

       self.assertListEqual(__UpperCamelCase						,  __UpperCamelCase				)





  def       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					List[str]  	=     '''facebook/opt-125m'''
       lowerCAmelCase		:					int  	=     [
           '''Today is a beautiful day and I want to thank''',
           '''In the city of Rome Canaver Canaver Canaver Canaver''',
       ]
       lowerCAmelCase		:					Optional[Any]  	=     FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase				)
       lowerCAmelCase		:					List[Any]  	=     GPTaTokenizer.from_pretrained(__UpperCamelCase				)
       lowerCAmelCase		:					Union[str, Any]  	=     tokenizer(
           [
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
           ]						,  return_tensors='''jax'''						,  padding=__UpperCamelCase						,  )

       lowerCAmelCase		:					Dict  	=     jax.jit(model.generate				)

       lowerCAmelCase		:					Optional[int]  	=     jit_generate(inputs['''input_ids''']						,  attention_mask=inputs['''attention_mask''']				).sequences

       lowerCAmelCase		:					List[str]  	=     tokenizer.batch_decode(__UpperCamelCase						,  skip_special_tokens=__UpperCamelCase				)

       self.assertIsNotNone(__UpperCamelCase						,  __UpperCamelCase				)





  def       _A							(							self    :	Tuple				):
       lowerCAmelCase		:					Tuple  	=     '''facebook/opt-350m'''

       lowerCAmelCase		:					Union[str, Any]  	=     GPTaTokenizer.from_pretrained(__UpperCamelCase				)
       lowerCAmelCase		:					Tuple  	=     FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase				)

       lowerCAmelCase		:					Any  	=     '''left'''

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

       lowerCAmelCase		:					Union[str, Any]  	=     tokenizer(__UpperCamelCase						,  return_tensors='''jax'''						,  padding=__UpperCamelCase				)
       lowerCAmelCase		:					str  	=     inputs['''input_ids''']

       lowerCAmelCase		:					int  	=     model.generate(input_ids=__UpperCamelCase						,  attention_mask=inputs['''attention_mask''']						,  trace=__UpperCamelCase				)

       lowerCAmelCase		:					int  	=     tokenizer(sentences[0]						,  return_tensors='''jax'''				).input_ids
       lowerCAmelCase		:					Union[str, Any]  	=     model.generate(input_ids=__UpperCamelCase				)

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

       lowerCAmelCase		:					Union[str, Any]  	=     tokenizer.batch_decode(outputs[0]						,  skip_special_tokens=__UpperCamelCase				)
       lowerCAmelCase		:					str  	=     tokenizer.decode(output_non_padded[0][0]						,  skip_special_tokens=__UpperCamelCase				)
       lowerCAmelCase		:					Union[str, Any]  	=     tokenizer.decode(output_padded[0][0]						,  skip_special_tokens=__UpperCamelCase				)

       lowerCAmelCase		:					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(__UpperCamelCase						,  __UpperCamelCase				)
       self.assertListEqual(__UpperCamelCase						,  [non_padded_sentence, padded_sentence]				)


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
