





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel



@require_tf
class    _a     :






       """simple docstring"""

       snake_case_							 =						PegasusConfig
       snake_case_							 =						{}
       snake_case_							 =						"gelu"

       def __init__(      self				:     str							,	a				:     Tuple							,	a				:     Optional[Any]=13							,	a				:     Tuple=7							,	a				:     List[str]=True							,	a				:     Any=False							,	a				:     Tuple=99							,	a				:     List[Any]=32							,	a				:     Union[str, Any]=2							,	a				:     Dict=4							,	a				:     int=37							,	a				:     Optional[Any]=0.1							,	a				:     Optional[Any]=0.1							,	a				:     Optional[int]=40							,	a				:     List[str]=2							,	a				:     List[str]=1							,	a				:     List[Any]=0							,	)	->str:
             SCREAMING_SNAKE_CASE__     :				Any   =					parent
             SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					batch_size
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					seq_length
             SCREAMING_SNAKE_CASE__     :				Tuple   =					is_training
             SCREAMING_SNAKE_CASE__     :				Any   =					use_labels
             SCREAMING_SNAKE_CASE__     :				Tuple   =					vocab_size
             SCREAMING_SNAKE_CASE__     :				Dict   =					hidden_size
             SCREAMING_SNAKE_CASE__     :				int   =					num_hidden_layers
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					num_attention_heads
             SCREAMING_SNAKE_CASE__     :				int   =					intermediate_size

             SCREAMING_SNAKE_CASE__     :				Any   =					hidden_dropout_prob
             SCREAMING_SNAKE_CASE__     :				List[Any]   =					attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					max_position_embeddings
             SCREAMING_SNAKE_CASE__     :				List[str]   =					eos_token_id
             SCREAMING_SNAKE_CASE__     :				List[str]   =					pad_token_id
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					bos_token_id

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

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size    )

             SCREAMING_SNAKE_CASE__     :				Tuple   =					self.config_cls(
                 vocab_size=self.vocab_size							,	d_model=self.hidden_size							,	encoder_layers=self.num_hidden_layers							,	decoder_layers=self.num_hidden_layers							,	encoder_attention_heads=self.num_attention_heads							,	decoder_attention_heads=self.num_attention_heads							,	encoder_ffn_dim=self.intermediate_size							,	decoder_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_ids=[2]							,	bos_token_id=self.bos_token_id							,	pad_token_id=self.pad_token_id							,	decoder_start_token_id=self.pad_token_id							,	**self.config_updates							,	)
             SCREAMING_SNAKE_CASE__     :				str   =					prepare_pegasus_inputs_dict(__A							,	__A							,	__A    )
             return config, inputs_dict






       def      A_  (      self				:     Optional[Any]							,	a				:     int							,	a				:     int    )	->Tuple:
             SCREAMING_SNAKE_CASE__     :				Any   =					TFPegasusModel(config=__A    ).get_decoder()
             SCREAMING_SNAKE_CASE__     :				List[str]   =					inputs_dict["input_ids"]

             SCREAMING_SNAKE_CASE__     :				str   =					input_ids[:1, :]
             SCREAMING_SNAKE_CASE__     :				Any   =					inputs_dict["attention_mask"][:1, :]
             SCREAMING_SNAKE_CASE__     :				Tuple   =					inputs_dict["head_mask"]
             SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1

             # first forward pass
             SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(__A							,	attention_mask=__A							,	head_mask=__A							,	use_cache=__A    )

             SCREAMING_SNAKE_CASE__     :				int   =					outputs.to_tuple()

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

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

             SCREAMING_SNAKE_CASE__     :				Dict   =					model(__A							,	attention_mask=__A    )[0]
             SCREAMING_SNAKE_CASE__     :				List[str]   =					model(__A							,	attention_mask=__A							,	past_key_values=__A    )[0]

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

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

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







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  List[str]=None			,   _lowerCamelCase      :  Tuple=None			,   _lowerCamelCase      :  List[Any]=None			,   _lowerCamelCase      :  Optional[Any]=None			,   _lowerCamelCase      :  List[str]=None			,   ):





      '''simple docstring'''


      if attention_mask is None:
            SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.cast(tf.math.not_equal(_lowerCAmelCase			,   config.pad_token_id     )			,   tf.inta     )
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape			,   dtype=tf.inta     ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,   config.pad_token_id     )			,   tf.inta     ),
                ]			,   axis=-1			,   )
      if head_mask is None:
            SCREAMING_SNAKE_CASE__     :				Dict   =					tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__     :				Dict   =					tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }



@require_tf
class    _a     (				lowercase__					,							lowercase__					,							unittest.TestCase							):






       """simple docstring"""

       snake_case_							 =						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
       snake_case_							 =						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
       snake_case_							 =						(
           {
               "conversational": TFPegasusForConditionalGeneration,
               "feature-extraction": TFPegasusModel,
               "summarization": TFPegasusForConditionalGeneration,
               "text2text-generation": TFPegasusForConditionalGeneration,
               "translation": TFPegasusForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       snake_case_							 =						True
       snake_case_							 =						False
       snake_case_							 =						False

       def      A_  (      self				:     Tuple    )	->List[str]:
             SCREAMING_SNAKE_CASE__     :				Tuple   =					TFPegasusModelTester(self    )
             SCREAMING_SNAKE_CASE__     :				Tuple   =					ConfigTester(self							,	config_class=__A    )

       def      A_  (      self				:     int    )	->Optional[Any]:
             self.config_tester.run_common_tests()






       def      A_  (      self				:     Dict    )	->List[str]:
             SCREAMING_SNAKE_CASE__     :				List[str]   =					self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*__A    )







@require_sentencepiece
@require_tokenizers
@require_tf
class    _a     (				unittest.TestCase							):






       """simple docstring"""

       snake_case_							 =						[
           " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
           " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
       ]
       snake_case_							 =						[
           "California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
           " reduce the risk of wildfires.",
           "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
       ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
       snake_case_							 =						"google/pegasus-xsum"

       @cached_property
       def      A_  (      self				:     Union[str, Any]    )	->List[str]:
             return AutoTokenizer.from_pretrained(self.model_name    )

       @cached_property
       def      A_  (      self				:     Tuple    )	->Optional[int]:
             SCREAMING_SNAKE_CASE__     :				Any   =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
             return model

       def      A_  (      self				:     List[Any]							,	**a				:     Dict    )	->Dict:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.translate_src_text(**__A    )
             assert self.expected_text == generated_words

       def      A_  (      self				:     str							,	**a				:     Dict    )	->Optional[int]:
             SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer(self.src_text							,	**__A							,	padding=__A							,	return_tensors="tf"    )
             SCREAMING_SNAKE_CASE__     :				Any   =					self.model.generate(
                 model_inputs.input_ids							,	attention_mask=model_inputs.attention_mask							,	num_beams=2							,	use_cache=__A							,	)
             SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.tokenizer.batch_decode(generated_ids.numpy()							,	skip_special_tokens=__A    )
             return generated_words






       @slow
       def      A_  (      self				:     int    )	->Any:
             self._assert_generated_batch_equal_expected()






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int | float]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) == 0:
												raise ValueError("find_max() arg is an empty sequence"     )
						if (
						    left >= len(_lowerCamelCase     )
						    or left < -len(_lowerCamelCase     )
						    or right >= len(_lowerCamelCase     )
						    or right < -len(_lowerCamelCase     )
						):
												raise IndexError("list index out of range"     )
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					find_max(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )  # find max in range[left, mid]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					find_max(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )  # find max in range[mid + 1, right]

						return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)