




'''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 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]				,					a_	:							Tuple				,					a_	:							int=None				,					a_	:							Optional[Any]=None			):
  if attention_mask is None:
    __a			=					tf.cast(tf.math.not_equal(a_				,					config.pad_token_id			)				,					tf.inta			)
  return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class    __lowercase						:
   _a							=      OPTConfig
   _a							=      {}
   _a							=      """gelu"""







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







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size      )
     __a			=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )      ,							1      )
     __a			=					tf.concat([input_ids, eos_tensor]      ,							axis=1      )

     __a			=					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=UpperCamelCase      ,							**self.config_updates      ,							)
     __a			=					prepare_opt_inputs_dict(UpperCamelCase      ,							UpperCamelCase      )
     return config, inputs_dict







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[Any]:
     __a			=					TFOPTModel(config=UpperCamelCase      )
     __a			=					inputs_dict['input_ids']

     __a			=					input_ids[:1, :]
     __a			=					inputs_dict['attention_mask'][:1, :]
     __a			=					1

     # first forward pass
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							use_cache=UpperCamelCase      )

     __a	,       __a			=					outputs.to_tuple()

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

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

     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      )[0]
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							past_key_values=UpperCamelCase      )[0]

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

     # select random slice
     __a			=					int(ids_tensor((1,)      ,							output_from_past.shape[-1]      )      )
     __a			=					output_from_no_past[:, -3:, random_slice_idx]
     __a			=					output_from_past[:, :, random_slice_idx]

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





@require_tf
class    __lowercase						(  __magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
   _a							=      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
   _a							=      (TFOPTForCausalLM,) if is_tf_available() else ()
   _a							=      (
       {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
   )
   _a							=      False
   _a							=      False
   _a							=      False
   _a							=      10







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					TFOPTModelTester(self      )
     __a			=					ConfigTester(self      ,							config_class=UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     self.config_tester.run_common_tests()







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a	,       __a			=					self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(UpperCamelCase      ,							UpperCamelCase      ):
       if hasattr(UpperCamelCase      ,							'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(UpperCamelCase      ,							'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
         __a			=					model_class(config=UpperCamelCase      )
         __a			=					_get_word_embedding_weight(UpperCamelCase      ,							model.get_input_embeddings()      )
         __a			=					_get_word_embedding_weight(UpperCamelCase      ,							model.get_output_embeddings()      )

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

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

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

         # check that weights remain the same after resizing
         __a			=					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:
             __a			=					False
         self.assertTrue(UpperCamelCase      )

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

           __a			=					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:
               __a			=					False
           self.assertTrue(UpperCamelCase      )


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]			):
  return tf.constant(a_				,					dtype=tf.intaa			)





@require_tf
class    __lowercase						(  unittest.TestCase ):
   _a							=      99







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







   @slow
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					TFOPTModel.from_pretrained('facebook/opt-350m'      )
     __a			=					_long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
     __a			=					tf.not_equal(UpperCamelCase      ,							model.config.pad_token_id      )
     with tf.GradientTape():
       __a			=					model(input_ids=UpperCamelCase      ,							attention_mask=UpperCamelCase      ).last_hidden_state
     __a			=					(1, 11, 512)
     self.assertEqual(output.shape      ,							UpperCamelCase      )
     __a			=					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]      ,							UpperCamelCase      ,							atol=4e-3      )      )

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





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







   def 		UpperCamelCase__			(	self      )				->    Any:
     super().setUp()
     __a			=					'facebook/opt-350m'







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					TFOPTForCausalLM.from_pretrained(self.path_model      )
     __a			=					GPTaTokenizer.from_pretrained(self.path_model      )

     __a			=					[
         '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
     __a			=					tokenizer(UpperCamelCase      ,							return_tensors='tf'      ,							padding=UpperCamelCase      ,							add_special_tokens=UpperCamelCase      )
     __a			=					tf.math.reduce_mean(model(inputs.input_ids      ,							attention_mask=inputs.attention_mask      )[0]      ,							axis=-1      )
     __a			=					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(UpperCamelCase      ,							UpperCamelCase      ,							atol=1e-4      )      )

     __a			=					tf.function(UpperCamelCase      ,							jit_compile=UpperCamelCase      )
     __a			=					tf.math.reduce_mean(xla_generate(inputs.input_ids      ,							attention_mask=inputs.attention_mask      )[0]      ,							axis=-1      )
     self.assertTrue(np.allclose(UpperCamelCase      ,							UpperCamelCase      ,							atol=1e-4      )      )





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







   @property
   def 		UpperCamelCase__			(	self      )				->    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 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					'facebook/opt-125m'

     __a			=					[
         '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',
     ]

     __a			=					[]
     __a			=					GPTaTokenizer.from_pretrained(UpperCamelCase      )
     __a			=					TFOPTForCausalLM.from_pretrained(UpperCamelCase      )

     for prompt in self.prompts:
       __a			=					tokenizer(UpperCamelCase      ,							return_tensors='tf'      ).input_ids

       __a			=					model.generate(UpperCamelCase      ,							max_length=10      )

       __a			=					tokenizer.batch_decode(UpperCamelCase      ,							skip_special_tokens=UpperCamelCase      )
       predicted_outputs += generated_string

     self.assertListEqual(UpperCamelCase      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					'facebook/opt-350m'

     __a			=					GPTaTokenizer.from_pretrained(UpperCamelCase      )
     __a			=					TFOPTForCausalLM.from_pretrained(UpperCamelCase      )

     __a			=					'left'

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

     __a			=					tokenizer(UpperCamelCase      ,							return_tensors='tf'      ,							padding=UpperCamelCase      )
     __a			=					inputs['input_ids']

     __a			=					model.generate(input_ids=UpperCamelCase      ,							attention_mask=inputs['attention_mask']      )

     __a			=					tokenizer(sentences[0]      ,							return_tensors='tf'      ).input_ids
     __a			=					model.generate(input_ids=UpperCamelCase      )

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

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

     __a			=					[
         '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]      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					'facebook/opt-350m'

     __a			=					[
         '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',
     ]

     __a			=					[]
     __a			=					GPTaTokenizer.from_pretrained(UpperCamelCase      )
     __a			=					TFOPTForCausalLM.from_pretrained(UpperCamelCase      )

     for prompt in self.prompts:
       __a			=					tokenizer(UpperCamelCase      ,							return_tensors='tf'      ).input_ids

       __a			=					model.generate(UpperCamelCase      ,							max_length=10      )

       __a			=					tokenizer.batch_decode(UpperCamelCase      ,							skip_special_tokens=UpperCamelCase      )
       predicted_outputs += generated_string

     self.assertListEqual(UpperCamelCase      ,							UpperCamelCase      )









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


if __name__ == "__main__":
  print(f"""{solution() = }""")



