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 SCREAMING_SNAKE_CASE_						:
   __lowerCAmelCase      =							PegasusConfig
   __lowerCAmelCase      =							{}
   __lowerCAmelCase      =							"""gelu"""







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





    """simple docstring"""


    UpperCamelCase          =	parent
    UpperCamelCase          =	batch_size
    UpperCamelCase          =	seq_length
    UpperCamelCase          =	is_training
    UpperCamelCase          =	use_labels
    UpperCamelCase          =	vocab_size
    UpperCamelCase          =	hidden_size
    UpperCamelCase          =	num_hidden_layers
    UpperCamelCase          =	num_attention_heads
    UpperCamelCase          =	intermediate_size

    UpperCamelCase          =	hidden_dropout_prob
    UpperCamelCase          =	attention_probs_dropout_prob
    UpperCamelCase          =	max_position_embeddings
    UpperCamelCase          =	eos_token_id
    UpperCamelCase          =	pad_token_id
    UpperCamelCase          =	bos_token_id







   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    UpperCamelCase          =	ids_tensor([self.batch_size, self.seq_length - 1]					,					self.vocab_size							)
    UpperCamelCase          =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)					,					1							)
    UpperCamelCase          =	tf.concat([input_ids, eos_tensor]					,					axis=1							)

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

    UpperCamelCase          =	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					,					)
    UpperCamelCase          =	prepare_pegasus_inputs_dict(A_					,					A_					,					A_							)
    return config, inputs_dict







   def 		lowerCamelCase_     (			self		:		List[Any]					,					lowerCamelCase_		:		Optional[int]					,					lowerCamelCase_		:		Optional[Any]							):





    """simple docstring"""


    UpperCamelCase          =	TFPegasusModel(config=A_							).get_decoder()
    UpperCamelCase          =	inputs_dict['''input_ids''']

    UpperCamelCase          =	input_ids[:1, :]
    UpperCamelCase          =	inputs_dict['''attention_mask'''][:1, :]
    UpperCamelCase          =	inputs_dict['''head_mask''']
    UpperCamelCase          =	1

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

    UpperCamelCase          =	outputs.to_tuple()

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

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

    UpperCamelCase          =	model(A_					,					attention_mask=A_							)[0]
    UpperCamelCase          =	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
    UpperCamelCase          =	int(ids_tensor((1,)					,					output_from_past.shape[-1]							)							)
    UpperCamelCase          =	output_from_no_past[:, -3:, random_slice_idx]
    UpperCamelCase          =	output_from_past[:, :, random_slice_idx]

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





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_=None				, UpperCamelCase_=None				, UpperCamelCase_=None				, UpperCamelCase_=None				, UpperCamelCase_=None				, )						->     Optional[Any]:







 '''simple docstring'''
 if attention_mask is None:
  UpperCamelCase          =	tf.cast(tf.math.not_equal(lowercase__				, config.pad_token_id    )				, tf.inta    )
 if decoder_attention_mask is None:
  UpperCamelCase          =	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:
  UpperCamelCase          =	tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
 if decoder_head_mask is None:
  UpperCamelCase          =	tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
 if cross_attn_head_mask is None:
  UpperCamelCase          =	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 SCREAMING_SNAKE_CASE_						(	_UpperCAmelCase   ,			_UpperCAmelCase   ,			unittest.TestCase						):
   __lowerCAmelCase      =							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   __lowerCAmelCase      =							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   __lowerCAmelCase      =							(
       {
           """conversational""": TFPegasusForConditionalGeneration,
           """feature-extraction""": TFPegasusModel,
           """summarization""": TFPegasusForConditionalGeneration,
           """text2text-generation""": TFPegasusForConditionalGeneration,
           """translation""": TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase      =							True
   __lowerCAmelCase      =							False
   __lowerCAmelCase      =							False







   def 		lowerCamelCase_     (			self		:		Optional[int]							):





    """simple docstring"""


    UpperCamelCase          =	TFPegasusModelTester(self							)
    UpperCamelCase          =	ConfigTester(self					,					config_class=A_							)







   def 		lowerCamelCase_     (			self		:		int							):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    UpperCamelCase          =	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 SCREAMING_SNAKE_CASE_						(	unittest.TestCase						):
   __lowerCAmelCase      =							[
       """ 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!\" """,
   ]
   __lowerCAmelCase      =							[
       """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
   __lowerCAmelCase      =							"""google/pegasus-xsum"""







   @cached_property
   def 		lowerCamelCase_     (			self		:		str							):





    """simple docstring"""


    return AutoTokenizer.from_pretrained(self.model_name							)







   @cached_property
   def 		lowerCamelCase_     (			self		:		Tuple							):





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		Dict					,					**lowerCamelCase_		:		int							):





    """simple docstring"""


    UpperCamelCase          =	self.translate_src_text(**A_							)
    assert self.expected_text == generated_words







   def 		lowerCamelCase_     (			self		:		Tuple					,					**lowerCamelCase_		:		List[Any]							):





    """simple docstring"""


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







   @slow
   def 		lowerCamelCase_     (			self		:		Any							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


