'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      List[Any]=None			,	_UpperCAmelCase  :      Optional[Any]=None    )    ->    Union[str, Any]:


       """simple docstring"""







       if attention_mask is None:
              _UpperCAmelCase     :  Tuple							      =    tf.cast(tf.math.not_equal(_UpperCAmelCase			,	config.pad_token_id    )			,	tf.inta    )
       return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						OPTConfig
      __UpperCamelCase:  Union[str, Any]						=						{}
      __UpperCamelCase:  List[Any]						=						"gelu"




      def __init__(       self						:				List[Any]						,					A						:				str						,					A						:				Any=13						,					A						:				Dict=7						,					A						:				List[str]=True						,					A						:				int=False						,					A						:				Union[str, Any]=99						,					A						:				Optional[Any]=16						,					A						:				Optional[Any]=2						,					A						:				List[str]=4						,					A						:				str=4						,					A						:				List[Any]="gelu"						,					A						:				Optional[Any]=0.1						,					A						:				Optional[Any]=0.1						,					A						:				Optional[Any]=20						,					A						:				Any=2						,					A						:				Optional[int]=1						,					A						:				Dict=0						,					A						:				Optional[int]=16						,					A						:				Union[str, Any]=16						,					):
             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Dict							      =    batch_size
             _UpperCAmelCase     :  Any							      =    seq_length
             _UpperCAmelCase     :  Any							      =    is_training
             _UpperCAmelCase     :  Union[str, Any]							      =    use_labels
             _UpperCAmelCase     :  Any							      =    vocab_size
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_hidden_layers
             _UpperCAmelCase     :  Union[str, Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Tuple							      =    intermediate_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  List[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Union[str, Any]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Tuple							      =    max_position_embeddings
             _UpperCAmelCase     :  Any							      =    eos_token_id
             _UpperCAmelCase     :  str							      =    pad_token_id
             _UpperCAmelCase     :  str							      =    bos_token_id
             _UpperCAmelCase     :  Optional[int]							      =    embed_dim
             _UpperCAmelCase     :  Optional[int]							      =    word_embed_proj_dim
             _UpperCAmelCase     :  int							      =    False




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    ids_tensor([self.batch_size, self.seq_length - 1]						,					self.vocab_size       )
             _UpperCAmelCase     :  Optional[int]							      =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )						,					1       )
             _UpperCAmelCase     :  Optional[Any]							      =    tf.concat([input_ids, eos_tensor]						,					axis=1       )

             _UpperCAmelCase     :  str							      =    self.config_cls(
                 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						,					word_embed_proj_dim=self.word_embed_proj_dim						,					is_encoder_decoder=_lowerCAmelCase						,					**self.config_updates						,					)
             _UpperCAmelCase     :  Any							      =    prepare_opt_inputs_dict(_lowerCAmelCase						,					_lowerCAmelCase       )
             return config, inputs_dict





      def      _A	(       self						:				Any						,					A						:				Optional[int]						,					A						:				str       ):
             _UpperCAmelCase     :  List[str]							      =    TFOPTModel(config=_lowerCAmelCase       )
             _UpperCAmelCase     :  str							      =    inputs_dict["input_ids"]

             _UpperCAmelCase     :  str							      =    input_ids[:1, :]
             _UpperCAmelCase     :  Dict							      =    inputs_dict["attention_mask"][:1, :]
             _UpperCAmelCase     :  Dict							      =    1

             # first forward pass
             _UpperCAmelCase     :  List[Any]							      =    model(_lowerCAmelCase						,					attention_mask=_lowerCAmelCase						,					use_cache=_lowerCAmelCase       )

             _UpperCAmelCase     :  int							      =    outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _UpperCAmelCase     :  str							      =    ids_tensor((self.batch_size, 3)						,					config.vocab_size       )
             _UpperCAmelCase     :  str							      =    tf.cast(ids_tensor((self.batch_size, 3)						,					2       )						,					tf.inta       )

             # append to next input_ids and
             _UpperCAmelCase     :  Optional[Any]							      =    tf.concat([input_ids, next_tokens]						,					axis=-1       )
             _UpperCAmelCase     :  Any							      =    tf.concat([attention_mask, next_attn_mask]						,					axis=-1       )

             _UpperCAmelCase     :  List[Any]							      =    model(_lowerCAmelCase						,					attention_mask=_lowerCAmelCase       )[0]
             _UpperCAmelCase     :  List[str]							      =    model(_lowerCAmelCase						,					attention_mask=_lowerCAmelCase						,					past_key_values=_lowerCAmelCase       )[0]

             self.parent.assertEqual(next_tokens.shape[1]						,					output_from_past.shape[1]       )

             # select random slice
             _UpperCAmelCase     :  Tuple							      =    int(ids_tensor((1,)						,					output_from_past.shape[-1]       )       )
             _UpperCAmelCase     :  Union[str, Any]							      =    output_from_no_past[:, -3:, random_slice_idx]
             _UpperCAmelCase     :  List[str]							      =    output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(_lowerCAmelCase						,					_lowerCAmelCase						,					rtol=1E-3       )

@require_tf
class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
      __UpperCamelCase:  Dict						=						(TFOPTForCausalLM,) if is_tf_available() else ()
      __UpperCamelCase:  Optional[Any]						=						(
          {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
      )
      __UpperCamelCase:  List[Any]						=						False
      __UpperCamelCase:  List[Any]						=						False
      __UpperCamelCase:  Union[str, Any]						=						False
      __UpperCamelCase:  int						=						1_0




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  int							      =    TFOPTModelTester(self       )
             _UpperCAmelCase     :  Dict							      =    ConfigTester(self						,					config_class=_lowerCAmelCase       )




      def      _A	(       self						:				Dict       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase       )





      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()

             def _get_word_embedding_weight(A						:				str						,					A						:				Optional[int]       ):
                    if hasattr(_lowerCAmelCase						,					"weight"       ):
                           return embedding_layer.weight
                    else:
                           # Here we build the word embeddings weights if not exists.
                           # And then we retry to get the attribute once built.
                           model.build()
                           if hasattr(_lowerCAmelCase						,					"weight"       ):
                                  return embedding_layer.weight
                           else:
                                  return None

             for model_class in self.all_model_classes:
                    for size in [config.vocab_size - 10, config.vocab_size + 10]:
                           # build the embeddings
                           _UpperCAmelCase     :  int							      =    model_class(config=_lowerCAmelCase       )
                           _UpperCAmelCase     :  str							      =    _get_word_embedding_weight(_lowerCAmelCase						,					model.get_input_embeddings()       )
                           _UpperCAmelCase     :  Dict							      =    _get_word_embedding_weight(_lowerCAmelCase						,					model.get_output_embeddings()       )

                           # reshape the embeddings
                           model.resize_token_embeddings(_lowerCAmelCase       )
                           _UpperCAmelCase     :  Dict							      =    _get_word_embedding_weight(_lowerCAmelCase						,					model.get_input_embeddings()       )
                           _UpperCAmelCase     :  Dict							      =    _get_word_embedding_weight(_lowerCAmelCase						,					model.get_output_embeddings()       )

                           # check that the resized embeddings size matches the desired size.
                           _UpperCAmelCase     :  int							      =    size if size is not None else config.vocab_size

                           self.assertEqual(new_input_embeddings.shape[0]						,					_lowerCAmelCase       )

                           # check that weights remain the same after resizing
                           _UpperCAmelCase     :  List[Any]							      =    True
                           for pa, pa in zip(old_input_embeddings.value()						,					new_input_embeddings.value()       ):
                                  if tf.math.reduce_sum(tf.math.abs(pa - pa       )       ) > 0:
                                         _UpperCAmelCase     :  int							      =    False
                           self.assertTrue(_lowerCAmelCase       )

                           if old_output_embeddings is not None and new_output_embeddings is not None:
                                  self.assertEqual(new_output_embeddings.shape[0]						,					_lowerCAmelCase       )

                                  _UpperCAmelCase     :  Tuple							      =    True
                                  for pa, pa in zip(old_output_embeddings.value()						,					new_output_embeddings.value()       ):
                                         if tf.math.reduce_sum(tf.math.abs(pa - pa       )       ) > 0:
                                                _UpperCAmelCase     :  List[Any]							      =    False
                                  self.assertTrue(_lowerCAmelCase       )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]    )    ->    Any:


       """simple docstring"""







       return tf.constant(_UpperCAmelCase			,	dtype=tf.intaa    )

@require_tf
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						9_9




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    tf.ones((4, 1)						,					dtype=tf.intaa       ) * 2
             _UpperCAmelCase     :  int							      =    tf.concat([ids_tensor((4, 6)						,					self.vocab_size - 3       ) + 3, eos_column_vector]						,					axis=1       )
             _UpperCAmelCase     :  List[str]							      =    input_ids.shape[0]
             _UpperCAmelCase     :  int							      =    OPTConfig(
                 vocab_size=self.vocab_size						,					hidden_size=24						,					num_hidden_layers=2						,					num_attention_heads=2						,					ffn_dim=32						,					max_position_embeddings=48						,					eos_token_id=2						,					pad_token_id=1						,					bos_token_id=0						,					)
             return config, input_ids, batch_size

@require_sentencepiece
@require_tf
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @slow
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Dict							      =    TFOPTModel.from_pretrained("facebook/opt-350m"       )
             _UpperCAmelCase     :  Optional[int]							      =    _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       )
             _UpperCAmelCase     :  Union[str, Any]							      =    tf.not_equal(_lowerCAmelCase						,					model.config.pad_token_id       )
             with tf.GradientTape():
                    _UpperCAmelCase     :  Dict							      =    model(input_ids=_lowerCAmelCase						,					attention_mask=_lowerCAmelCase       ).last_hidden_state
             _UpperCAmelCase     :  Optional[int]							      =    (1, 11, 512)
             self.assertEqual(output.shape						,					_lowerCAmelCase       )
             _UpperCAmelCase     :  Optional[int]							      =    tf.constant(
                 [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]       )
             self.assertTrue(np.allclose(output[:, :3, :3]						,					_lowerCAmelCase						,					atol=4E-3       )       )

             _UpperCAmelCase     :  List[Any]							      =    tf.function(_lowerCAmelCase						,					jit_compile=_lowerCAmelCase       )
             _UpperCAmelCase     :  Optional[int]							      =    xla_generate(_lowerCAmelCase						,					_lowerCAmelCase       )[0]
             self.assertTrue(np.allclose(output[:, :3, :3]						,					_lowerCAmelCase						,					atol=4E-2       )       )

@require_tf
@slow
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				List[str]       ):
             super().setUp()
             _UpperCAmelCase     :  List[Any]							      =    "facebook/opt-350m"





      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    TFOPTForCausalLM.from_pretrained(self.path_model       )
             _UpperCAmelCase     :  Optional[Any]							      =    GPTaTokenizer.from_pretrained(self.path_model       )

             _UpperCAmelCase     :  Optional[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
             _UpperCAmelCase     :  List[Any]							      =    tokenizer(_lowerCAmelCase						,					return_tensors="tf"						,					padding=_lowerCAmelCase						,					add_special_tokens=_lowerCAmelCase       )
             _UpperCAmelCase     :  List[str]							      =    tf.math.reduce_mean(model(inputs.input_ids						,					attention_mask=inputs.attention_mask       )[0]						,					axis=-1       )
             _UpperCAmelCase     :  Any							      =    tf.constant(
                 [
                     [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                     [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -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, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                     [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
                 ]       )
             self.assertTrue(np.allclose(_lowerCAmelCase						,					_lowerCAmelCase						,					atol=1E-4       )       )

             _UpperCAmelCase     :  Dict							      =    tf.function(_lowerCAmelCase						,					jit_compile=_lowerCAmelCase       )
             _UpperCAmelCase     :  int							      =    tf.math.reduce_mean(xla_generate(inputs.input_ids						,					attention_mask=inputs.attention_mask       )[0]						,					axis=-1       )
             self.assertTrue(np.allclose(_lowerCAmelCase						,					_lowerCAmelCase						,					atol=1E-4       )       )

@require_tf
@slow
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @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       ):
             _UpperCAmelCase     :  Optional[int]							      =    "facebook/opt-125m"

             _UpperCAmelCase     :  int							      =    [
                 "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     :  Optional[Any]							      =    GPTaTokenizer.from_pretrained(_lowerCAmelCase       )
             _UpperCAmelCase     :  Union[str, Any]							      =    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase       )

             for prompt in self.prompts:
                    _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer(_lowerCAmelCase						,					return_tensors="tf"       ).input_ids

                    _UpperCAmelCase     :  Optional[Any]							      =    model.generate(_lowerCAmelCase						,					max_length=10       )

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

             self.assertListEqual(_lowerCAmelCase						,					_lowerCAmelCase       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Any							      =    "facebook/opt-350m"

             _UpperCAmelCase     :  int							      =    GPTaTokenizer.from_pretrained(_lowerCAmelCase       )
             _UpperCAmelCase     :  Dict							      =    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase       )

             _UpperCAmelCase     :  Union[str, Any]							      =    "left"

             # use different length sentences to test batching
             _UpperCAmelCase     :  Union[str, Any]							      =    [
                 "Hello, my dog is a little",
                 "Today, I",
             ]

             _UpperCAmelCase     :  List[str]							      =    tokenizer(_lowerCAmelCase						,					return_tensors="tf"						,					padding=_lowerCAmelCase       )
             _UpperCAmelCase     :  str							      =    inputs["input_ids"]

             _UpperCAmelCase     :  Tuple							      =    model.generate(input_ids=_lowerCAmelCase						,					attention_mask=inputs["attention_mask"]       )

             _UpperCAmelCase     :  Dict							      =    tokenizer(sentences[0]						,					return_tensors="tf"       ).input_ids
             _UpperCAmelCase     :  str							      =    model.generate(input_ids=_lowerCAmelCase       )

             _UpperCAmelCase     :  Optional[int]							      =    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
                 tf.cast(inputs["attention_mask"][-1]						,					tf.intaa       )       )
             _UpperCAmelCase     :  int							      =    tokenizer(sentences[1]						,					return_tensors="tf"       ).input_ids
             _UpperCAmelCase     :  Dict							      =    model.generate(input_ids=_lowerCAmelCase						,					max_length=model.config.max_length - num_paddings       )

             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.batch_decode(_lowerCAmelCase						,					skip_special_tokens=_lowerCAmelCase       )
             _UpperCAmelCase     :  str							      =    tokenizer.decode(output_non_padded[0]						,					skip_special_tokens=_lowerCAmelCase       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.decode(output_padded[0]						,					skip_special_tokens=_lowerCAmelCase       )

             _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(_lowerCAmelCase						,					_lowerCAmelCase       )
             self.assertListEqual(_lowerCAmelCase						,					[non_padded_sentence, padded_sentence]       )





      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Optional[Any]							      =    "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     :  int							      =    []
             _UpperCAmelCase     :  Any							      =    GPTaTokenizer.from_pretrained(_lowerCAmelCase       )
             _UpperCAmelCase     :  Optional[int]							      =    TFOPTForCausalLM.from_pretrained(_lowerCAmelCase       )

             for prompt in self.prompts:
                    _UpperCAmelCase     :  List[str]							      =    tokenizer(_lowerCAmelCase						,					return_tensors="tf"       ).input_ids

                    _UpperCAmelCase     :  Union[str, Any]							      =    model.generate(_lowerCAmelCase						,					max_length=10       )

                    _UpperCAmelCase     :  int							      =    tokenizer.batch_decode(_lowerCAmelCase						,					skip_special_tokens=_lowerCAmelCase       )
                    predicted_outputs += generated_string

             self.assertListEqual(_lowerCAmelCase						,					_lowerCAmelCase       )







import torch



def 					UpperCAmelCase__     (   ):
  if torch.cuda.is_available():
    lowercase			:Optional[int]							=  torch.cuda.device_count()
  else:
    lowercase			:Dict							=  0
  print(F"Successfully ran on {num_gpus} GPUs"      )


if __name__ == "__main__":
     main()



