






'''simple docstring'''




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    _UpperCAmelCase			:




   """simple docstring"""

   snake_case    				=						PegasusConfig
   snake_case    				=						{}
   snake_case    				=						'''gelu'''

   def __init__(		self :	Tuple      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Optional[Any]=13      ,      __UpperCAmelCase :	int=7      ,      __UpperCAmelCase :	List[str]=True      ,      __UpperCAmelCase :	str=False      ,      __UpperCAmelCase :	Union[str, Any]=99      ,      __UpperCAmelCase :	Tuple=32      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	int=4      ,      __UpperCAmelCase :	Tuple=37      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	List[str]=40      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	Optional[int]=1      ,      __UpperCAmelCase :	Any=0      ,      ):





         '''simple docstring'''





         _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_dropout_prob
         _A			     =	attention_probs_dropout_prob
         _A			     =	max_position_embeddings
         _A			     =	eos_token_id
         _A			     =	pad_token_id
         _A			     =	bos_token_id

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _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			     =	ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

         _A			     =	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      ,      )
         _A			     =	prepare_pegasus_inputs_dict(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         return config, inputs_dict

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Optional[int]      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	TFPegasusModel(config=__UpperCAmelCase		).get_decoder()
         _A			     =	inputs_dict["input_ids"]

         _A			     =	input_ids[:1, :]
         _A			     =	inputs_dict["attention_mask"][:1, :]
         _A			     =	inputs_dict["head_mask"]
         _A			     =	1

         # first forward pass
         _A			     =	model(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase      ,      head_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		)





def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  )       ->						Union[str, Any]:

      '''simple docstring'''


      if attention_mask is None:
            _A			     =	tf.cast(tf.math.not_equal(__lowercase   ,  config.pad_token_id       )   ,  tf.inta       )
      if decoder_attention_mask is None:
            _A			     =	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:
            _A			     =	tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
      if decoder_head_mask is None:
            _A			     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
      if cross_attn_head_mask is None:
            _A			     =	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    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    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 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	TFPegasusModelTester(self		)
         _A			     =	ConfigTester(self      ,      config_class=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A			     =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase		)





@require_sentencepiece
@require_tokenizers
@require_tf
class    _UpperCAmelCase			(     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 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         return AutoTokenizer.from_pretrained(self.model_name		)

   @cached_property
   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         _A			     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
         return model

   def 						lowerCAmelCase (		self :	List[Any]      ,      **__UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.translate_src_text(**__UpperCAmelCase		)
         assert self.expected_text == generated_words

   def 						lowerCAmelCase (		self :	Dict      ,      **__UpperCAmelCase :	Optional[int]		):





         '''simple docstring'''





         _A			     =	self.tokenizer(self.src_text      ,      **__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      return_tensors="tf"		)
         _A			     =	self.model.generate(
             model_inputs.input_ids      ,      attention_mask=model_inputs.attention_mask      ,      num_beams=2      ,      use_cache=__UpperCAmelCase      ,      )
         _A			     =	self.tokenizer.batch_decode(generated_ids.numpy()      ,      skip_special_tokens=__UpperCAmelCase		)
         return generated_words

   @slow
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         self._assert_generated_batch_equal_expected()









import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf





class       A    (   enum.Enum ):
							UpperCamelCase_   :							Optional[int]       =0
							UpperCamelCase_   :							Tuple       =1
							UpperCamelCase_   :							Optional[int]       =2





@add_end_docstrings(A_ )
class       A    (   A_ ):
							UpperCamelCase_   :							Union[str, Any]       ='''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''


							def __init__(self      ,				*lowerCAmelCase      ,				**lowerCAmelCase					):
									super().__init__(*lowerCAmelCase      ,				**lowerCAmelCase					)
									self.check_model_type(
									    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING					)
									if "prefix" not in self._preprocess_params:
											# This is very specific. The logic is quite complex and needs to be done
											# as a "default".
											# It also defines both some preprocess_kwargs and generate_kwargs
											# which is why we cannot put them in their respective methods.
											__lowercase=			None
											if self.model.config.prefix is not None:
													__lowercase=			self.model.config.prefix
											if prefix is None and self.model.__class__.__name__ in [
											    "XLNetLMHeadModel",
											    "TransfoXLLMHeadModel",
											    "TFXLNetLMHeadModel",
											    "TFTransfoXLLMHeadModel",
											]:
													# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
													__lowercase=			self.XL_PREFIX
											if prefix is not None:
													# Recalculate some generate_kwargs linked to prefix.
													__lowercase,							__lowercase,							__lowercase=			self._sanitize_parameters(prefix=lowerCAmelCase      ,				**self._forward_params					)
													__lowercase=			{**self._preprocess_params, **preprocess_params}
													__lowercase=			{**self._forward_params, **forward_params}


							def 			_A       (self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									__lowercase=			{}
									if prefix is not None:
											__lowercase=			prefix
									if prefix:
											__lowercase=			self.tokenizer(
											    lowerCAmelCase      ,				padding=lowerCAmelCase      ,				add_special_tokens=lowerCAmelCase      ,				return_tensors=self.framework					)
											__lowercase=			prefix_inputs['input_ids'].shape[-1]

									if handle_long_generation is not None:
											if handle_long_generation not in {"hole"}:
													raise ValueError(
													    f'{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'
													    ' [None, \'hole\']'					)
											__lowercase=			handle_long_generation

									preprocess_params.update(lowerCAmelCase					)
									__lowercase=			generate_kwargs

									__lowercase=			{}
									if return_full_text is not None and return_type is None:
											if return_text is not None:
													raise ValueError('`return_text` is mutually exclusive with `return_full_text`'					)
											if return_tensors is not None:
													raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'					)
											__lowercase=			ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
									if return_tensors is not None and return_type is None:
											if return_text is not None:
													raise ValueError('`return_text` is mutually exclusive with `return_tensors`'					)
											__lowercase=			ReturnType.TENSORS
									if return_type is not None:
											__lowercase=			return_type
									if clean_up_tokenization_spaces is not None:
											__lowercase=			clean_up_tokenization_spaces

									if stop_sequence is not None:
											__lowercase=			self.tokenizer.encode(lowerCAmelCase      ,				add_special_tokens=lowerCAmelCase					)
											if len(lowerCAmelCase					) > 1:
													warnings.warn(
													    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
													    ' the stop sequence will be used as the stop sequence string in the interim.'					)
											__lowercase=			stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params


							def 			_A       (self      ,				*lowerCAmelCase      ,				**lowerCAmelCase					):
									# Parse arguments
									if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
											kwargs.update({'add_space_before_punct_symbol': True}					)

									return super()._parse_and_tokenize(*lowerCAmelCase      ,				**lowerCAmelCase					)


							def __call__(self      ,				lowerCAmelCase      ,				**lowerCAmelCase					):
									return super().__call__(lowerCAmelCase      ,				**lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=""      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									__lowercase=			self.tokenizer(
									    prefix + prompt_text      ,				padding=lowerCAmelCase      ,				add_special_tokens=lowerCAmelCase      ,				return_tensors=self.framework					)
									__lowercase=			prompt_text

									if handle_long_generation == "hole":
											__lowercase=			inputs['input_ids'].shape[-1]
											if "max_new_tokens" in generate_kwargs:
													__lowercase=			generate_kwargs['max_new_tokens']
											else:
													__lowercase=			generate_kwargs.get('max_length'      ,				self.model.config.max_length					) - cur_len
													if new_tokens < 0:
															raise ValueError('We cannot infer how many new tokens are expected'					)
											if cur_len + new_tokens > self.tokenizer.model_max_length:
													__lowercase=			self.tokenizer.model_max_length - new_tokens
													if keep_length <= 0:
															raise ValueError(
															    'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
															    ' models max length'					)

													__lowercase=			inputs['input_ids'][:, -keep_length:]
													if "attention_mask" in inputs:
															__lowercase=			inputs['attention_mask'][:, -keep_length:]

									return inputs


							def 			_A       (self      ,				lowerCAmelCase      ,				**lowerCAmelCase					):
									__lowercase=			model_inputs['input_ids']
									__lowercase=			model_inputs.get('attention_mask'      ,				lowerCAmelCase					)
									# Allow empty prompts
									if input_ids.shape[1] == 0:
											__lowercase=			None
											__lowercase=			None
											__lowercase=			1
									else:
											__lowercase=			input_ids.shape[0]
									__lowercase=			model_inputs.pop('prompt_text'					)

									# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
									# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
									__lowercase=			generate_kwargs.pop('prefix_length'      ,				0					)
									if prefix_length > 0:
											__lowercase=			'max_new_tokens' in generate_kwargs or (
											    'generation_config' in generate_kwargs
											    and generate_kwargs['generation_config'].max_new_tokens is not None
											)
											if not has_max_new_tokens:
													__lowercase=			generate_kwargs.get('max_length'					) or self.model.config.max_length
													generate_kwargs["max_length"] += prefix_length
											__lowercase=			'min_new_tokens' in generate_kwargs or (
											    'generation_config' in generate_kwargs
											    and generate_kwargs['generation_config'].min_new_tokens is not None
											)
											if not has_min_new_tokens and "min_length" in generate_kwargs:
													generate_kwargs["min_length"] += prefix_length

        # BS x SL
									__lowercase=			self.model.generate(input_ids=lowerCAmelCase      ,				attention_mask=lowerCAmelCase      ,				**lowerCAmelCase					)
									__lowercase=			generated_sequence.shape[0]
									if self.framework == "pt":
											__lowercase=			generated_sequence.reshape(lowerCAmelCase      ,				out_b // in_b      ,				*generated_sequence.shape[1:]					)
									elif self.framework == "tf":
											__lowercase=			tf.reshape(lowerCAmelCase      ,				(in_b, out_b // in_b, *generated_sequence.shape[1:])					)
									return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=ReturnType.FULL_TEXT      ,				lowerCAmelCase=True					):
									__lowercase=			model_outputs['generated_sequence'][0]
									__lowercase=			model_outputs['input_ids']
									__lowercase=			model_outputs['prompt_text']
									__lowercase=			generated_sequence.numpy().tolist()
									__lowercase=			[]
									for sequence in generated_sequence:
											if return_type == ReturnType.TENSORS:
													__lowercase=			{'generated_token_ids': sequence}
											elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
													# Decode text
													__lowercase=			self.tokenizer.decode(
													    lowerCAmelCase      ,				skip_special_tokens=lowerCAmelCase      ,				clean_up_tokenization_spaces=lowerCAmelCase      ,				)

													# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
													if input_ids is None:
															__lowercase=			0
													else:
															__lowercase=			len(
															    self.tokenizer.decode(
															        input_ids[0]      ,				skip_special_tokens=lowerCAmelCase      ,				clean_up_tokenization_spaces=lowerCAmelCase      ,				)					)

													if return_type == ReturnType.FULL_TEXT:
															__lowercase=			prompt_text + text[prompt_length:]
													else:
															__lowercase=			text[prompt_length:]

													__lowercase=			{'generated_text': all_text}
											records.append(lowerCAmelCase					)

									return records






