





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_ :





					"""simple docstring"""


					A__				 =    PegasusConfig
					A__				 =    {}
					A__				 =    "gelu"





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=13	,  _lowerCAmelCase=7	,  _lowerCAmelCase=True	,  _lowerCAmelCase=False	,  _lowerCAmelCase=99	,  _lowerCAmelCase=32	,  _lowerCAmelCase=2	,  _lowerCAmelCase=4	,  _lowerCAmelCase=37	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=40	,  _lowerCAmelCase=2	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  ):
										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_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       eos_token_id
										lowerCamelCase__       =       pad_token_id
										lowerCamelCase__       =       bos_token_id





					def 					__magic_name__ ( self	):
										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__       =       ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size	)

										lowerCamelCase__       =       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	,  )
										lowerCamelCase__       =       prepare_pegasus_inputs_dict(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										return config, inputs_dict






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       TFPegasusModel(config=_lowerCAmelCase	).get_decoder()
										lowerCamelCase__       =       inputs_dict["input_ids"]

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

										# first forward pass
										lowerCamelCase__       =       model(_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	,  head_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	)





def    __UpperCamelCase      (    a,     a,     a,     a=None,     a=None,     a=None,     a=None,     a=None,     ) ->List[Any]:
					if attention_mask is None:
										lowerCamelCase__       =       tf.cast(tf.math.not_equal(a,     config.pad_token_id),     tf.inta)
					if decoder_attention_mask is None:
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       tf.ones((config.encoder_layers, config.encoder_attention_heads))
					if decoder_head_mask is None:
										lowerCamelCase__       =       tf.ones((config.decoder_layers, config.decoder_attention_heads))
					if cross_attn_head_mask is None:
										lowerCamelCase__       =       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_ (					lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFPegasusModelTester(self	)
										lowerCamelCase__       =       ConfigTester(self	,  config_class=_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										self.config_tester.run_common_tests()






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase	)

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





					"""simple docstring"""


					A__				 =    [
					    " 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!\" ",
					]
					A__				 =    [
					    "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
					A__				 =    "google/pegasus-xsum"





					@cached_property
					def 					__magic_name__ ( self	):
										return AutoTokenizer.from_pretrained(self.model_name	)





					@cached_property
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
										return model





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       self.translate_src_text(**_lowerCAmelCase	)
										assert self.expected_text == generated_words





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       self.tokenizer(self.src_text	,  **_lowerCAmelCase	,  padding=_lowerCAmelCase	,  return_tensors="tf"	)
										lowerCamelCase__       =       self.model.generate(
										    model_inputs.input_ids	,  attention_mask=model_inputs.attention_mask	,  num_beams=2	,  use_cache=_lowerCAmelCase	,  )
										lowerCamelCase__       =       self.tokenizer.batch_decode(generated_ids.numpy()	,  skip_special_tokens=_lowerCAmelCase	)
										return generated_words






					@slow
					def 					__magic_name__ ( self	):
										self._assert_generated_batch_equal_expected()








from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

A_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->List[List[ImageInput]]:
					if isinstance(a,     (list, tuple)) and isinstance(videos[0],     (list, tuple)) and is_valid_image(videos[0][0]):
										return videos

					elif isinstance(a,     (list, tuple)) and is_valid_image(videos[0]):
										return [videos]

					elif is_valid_image(a):
										return [[videos]]

					raise ValueError(f"Could not make batched video from {videos}")







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["pixel_values"]





					def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 256}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										lowerCamelCase__       =       do_resize
										lowerCamelCase__       =       size
										lowerCamelCase__       =       do_center_crop
										lowerCamelCase__       =       crop_size
										lowerCamelCase__       =       resample
										lowerCamelCase__       =       do_rescale
										lowerCamelCase__       =       rescale_factor
										lowerCamelCase__       =       offset
										lowerCamelCase__       =       do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCamelCase__       =       image_std if image_std is not None else IMAGENET_STANDARD_STD





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										if "shortest_edge" in size:
															lowerCamelCase__       =       get_resize_output_image_size(_lowerCAmelCase	,  size["shortest_edge"]	,  default_to_square=_lowerCAmelCase	)
										elif "height" in size and "width" in size:
															lowerCamelCase__       =       (size["height"], size["width"])
										else:
															raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	)
										if "height" not in size or "width" not in size:
															raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"	)
										return center_crop(_lowerCAmelCase	,  size=(size["height"], size["width"])	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       image.astype(np.floataa	)
										if offset:
															lowerCamelCase__       =       image - (scale / 2)
										return rescale(_lowerCAmelCase	,  scale=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										return normalize(_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  ):
										if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."	)

										if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."	)

										if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."	)

										if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."	)

										if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."	)

										# All transformations expect numpy arrays.
										lowerCamelCase__       =       to_numpy_array(_lowerCAmelCase	)

										if do_resize:
															lowerCamelCase__       =       self.resize(image=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	)

										if do_center_crop:
															lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	,  size=_lowerCAmelCase	)

										if do_rescale:
															lowerCamelCase__       =       self.rescale(image=_lowerCAmelCase	,  scale=_lowerCAmelCase	,  offset=_lowerCAmelCase	)

										if do_normalize:
															lowerCamelCase__       =       self.normalize(image=_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	)

										lowerCamelCase__       =       to_channel_dimension_format(_lowerCAmelCase	,  _lowerCAmelCase	)
										return image






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = ChannelDimension.FIRST	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__       =       resample if resample is not None else self.resample
										lowerCamelCase__       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__       =       do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__       =       offset if offset is not None else self.offset
										lowerCamelCase__       =       do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__       =       image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__       =       image_std if image_std is not None else self.image_std

										lowerCamelCase__       =       size if size is not None else self.size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										if not valid_images(_lowerCAmelCase	):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."	)

										lowerCamelCase__       =       make_batched(_lowerCAmelCase	)

										lowerCamelCase__       =       [
										    [
										        self._preprocess_image(
										            image=_lowerCAmelCase	,  do_resize=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  do_center_crop=_lowerCAmelCase	,  crop_size=_lowerCAmelCase	,  do_rescale=_lowerCAmelCase	,  rescale_factor=_lowerCAmelCase	,  offset=_lowerCAmelCase	,  do_normalize=_lowerCAmelCase	,  image_mean=_lowerCAmelCase	,  image_std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  )
										        for img in video
										    ]
										    for video in videos
										]

										lowerCamelCase__       =       {"pixel_values": videos}
										return BatchFeature(data=_lowerCAmelCase	,  tensor_type=_lowerCAmelCase	)

