





'''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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					List[Any]		,       SCREAMING_SNAKE_CASE_  :					Tuple=None		,       SCREAMING_SNAKE_CASE_  :					List[str]=None    ):



    '''simple docstring'''





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

@require_tf
class    lowerCAmelCase_		:
 __lowerCamelCase						:							Dict         =					OPTConfig
 __lowerCamelCase						:							Optional[Any]         =					{}
 __lowerCamelCase						:							Optional[Any]         =					"gelu"




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




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size		)
     _lowerCAmelCase				=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)			,     1		)
     _lowerCAmelCase				=      tf.concat([input_ids, eos_tensor]			,     axis=1		)

     _lowerCAmelCase				=      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			,     )
     _lowerCAmelCase				=      prepare_opt_inputs_dict(_lowerCAmelCase			,     _lowerCAmelCase		)
     return config, inputs_dict





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase				=      TFOPTModel(config=_lowerCAmelCase		)
     _lowerCAmelCase				=      inputs_dict["input_ids"]

     _lowerCAmelCase				=      input_ids[:1, :]
     _lowerCAmelCase				=      inputs_dict["attention_mask"][:1, :]
     _lowerCAmelCase				=      1

     # first forward pass
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     attention_mask=_lowerCAmelCase			,     use_cache=_lowerCAmelCase		)

     _lowerCAmelCase			,			_lowerCAmelCase				=      outputs.to_tuple()

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

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

     _lowerCAmelCase				=      model(_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)[0]
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      int(ids_tensor((1,)			,     output_from_past.shape[-1]		)		)
     _lowerCAmelCase				=      output_from_no_past[:, -3:, random_slice_idx]
     _lowerCAmelCase				=      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_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Optional[int]         =					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 __lowerCamelCase						:							Tuple         =					(TFOPTForCausalLM,) if is_tf_available() else ()
 __lowerCamelCase						:							int         =					(
     {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
 )
 __lowerCamelCase						:							Tuple         =					False
 __lowerCamelCase						:							Optional[Any]         =					False
 __lowerCamelCase						:							int         =					False
 __lowerCamelCase						:							Tuple         =					10




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      TFOPTModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     self.config_tester.run_common_tests()




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)





 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(_lowerCAmelCase			,     _lowerCAmelCase		):
         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
             _lowerCAmelCase				=      model_class(config=_lowerCAmelCase		)
             _lowerCAmelCase				=      _get_word_embedding_weight(_lowerCAmelCase			,     model.get_input_embeddings()		)
             _lowerCAmelCase				=      _get_word_embedding_weight(_lowerCAmelCase			,     model.get_output_embeddings()		)

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

             # check that the resized embeddings size matches the desired size.
             _lowerCAmelCase				=      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
             _lowerCAmelCase				=      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:
                     _lowerCAmelCase				=      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		)

                 _lowerCAmelCase				=      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:
                         _lowerCAmelCase				=      False
                 self.assertTrue(_lowerCAmelCase		)







def 	__a(SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





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

@require_tf
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							List[str]         =					99




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      tf.ones((4, 1)			,     dtype=tf.intaa		) * 2
     _lowerCAmelCase				=      tf.concat([ids_tensor((4, 6)			,     self.vocab_size - 3		) + 3, eos_column_vector]			,     axis=1		)
     _lowerCAmelCase				=      input_ids.shape[0]
     _lowerCAmelCase				=      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		):




 @slow
 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      TFOPTModel.from_pretrained("facebook/opt-350m"		)
     _lowerCAmelCase				=      _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
     _lowerCAmelCase				=      tf.not_equal(_lowerCAmelCase			,     model.config.pad_token_id		)
     with tf.GradientTape():
         _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		).last_hidden_state
     _lowerCAmelCase				=      (1, 11, 512)
     self.assertEqual(output.shape			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      tf.constant(
         [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]		)
     self.assertTrue(np.allclose(output[:, :3, :3]			,     _lowerCAmelCase			,     atol=4E-3		)		)

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







@require_tf
@slow
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     super().setUp()
     _lowerCAmelCase				=      "facebook/opt-350m"





 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      TFOPTForCausalLM.from_pretrained(self.path_model		)
     _lowerCAmelCase				=      GPTaTokenizer.from_pretrained(self.path_model		)

     _lowerCAmelCase				=      [
         "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				=      tokenizer(_lowerCAmelCase			,     return_tensors="tf"			,     padding=_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase				=      tf.math.reduce_mean(model(inputs.input_ids			,     attention_mask=inputs.attention_mask		)[0]			,     axis=-1		)
     _lowerCAmelCase				=      tf.constant(
         [
             [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(np.allclose(_lowerCAmelCase			,     _lowerCAmelCase			,     atol=1E-4		)		)

     _lowerCAmelCase				=      tf.function(_lowerCAmelCase			,     jit_compile=_lowerCAmelCase		)
     _lowerCAmelCase				=      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		):




 @property
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     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 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "facebook/opt-125m"

     _lowerCAmelCase				=      [
         "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				=      []
     _lowerCAmelCase				=      GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
     _lowerCAmelCase				=      TFOPTForCausalLM.from_pretrained(_lowerCAmelCase		)

     for prompt in self.prompts:
         _lowerCAmelCase				=      tokenizer(_lowerCAmelCase			,     return_tensors="tf"		).input_ids

         _lowerCAmelCase				=      model.generate(_lowerCAmelCase			,     max_length=10		)

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

     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      "facebook/opt-350m"

     _lowerCAmelCase				=      GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
     _lowerCAmelCase				=      TFOPTForCausalLM.from_pretrained(_lowerCAmelCase		)

     _lowerCAmelCase				=      "left"

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

     _lowerCAmelCase				=      tokenizer(_lowerCAmelCase			,     return_tensors="tf"			,     padding=_lowerCAmelCase		)
     _lowerCAmelCase				=      inputs["input_ids"]

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

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

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

     _lowerCAmelCase				=      tokenizer.batch_decode(_lowerCAmelCase			,     skip_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase				=      tokenizer.decode(output_non_padded[0]			,     skip_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase				=      tokenizer.decode(output_padded[0]			,     skip_special_tokens=_lowerCAmelCase		)

     _lowerCAmelCase				=      [
         "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 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      "facebook/opt-350m"

     _lowerCAmelCase				=      [
         "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				=      []
     _lowerCAmelCase				=      GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
     _lowerCAmelCase				=      TFOPTForCausalLM.from_pretrained(_lowerCAmelCase		)

     for prompt in self.prompts:
         _lowerCAmelCase				=      tokenizer(_lowerCAmelCase			,     return_tensors="tf"		).input_ids

         _lowerCAmelCase				=      model.generate(_lowerCAmelCase			,     max_length=10		)

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

     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


