




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 						lowercase							:
     _a							 =						PegasusConfig
     _a							 =						{}
     _a							 =						"gelu"

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

           _A							:     Dict			=						hidden_dropout_prob
           _A							:     Dict			=						attention_probs_dropout_prob
           _A							:     Union[str, Any]			=						max_position_embeddings
           _A							:     int			=						eos_token_id
           _A							:     Dict			=						pad_token_id
           _A							:     List[str]			=						bos_token_id

     def 					a__	(  self				)						->							str:
           _A							:     str			=						ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size				)
           _A							:     Optional[Any]			=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,     1				)
           _A							:     Any			=						tf.concat([input_ids, eos_tensor]		,     axis=1				)

           _A							:     Optional[int]			=						ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

           _A							:     Optional[Any]			=						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							:     List[Any]			=						prepare_pegasus_inputs_dict(_a		,     _a		,     _a				)
           return config, inputs_dict



     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:
           _A							:     Any			=						TFPegasusModel(config=_a				).get_decoder()
           _A							:     Union[str, Any]			=						inputs_dict["""input_ids"""]

           _A							:     Union[str, Any]			=						input_ids[:1, :]
           _A							:     List[str]			=						inputs_dict["""attention_mask"""][:1, :]
           _A							:     Any			=						inputs_dict["""head_mask"""]
           _A							:     Any			=						1

           # first forward pass
           _A							:     int			=						model(_a		,     attention_mask=_a		,     head_mask=_a		,     use_cache=_a				)

           _A			,   _A							:     Union[str, Any]			=						outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           _A							:     List[str]			=						ids_tensor((self.batch_size, 3)		,     config.vocab_size				)
           _A							:     Optional[Any]			=						tf.cast(ids_tensor((self.batch_size, 3)		,     2				)		,     tf.inta				)

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

           _A							:     Any			=						model(_a		,     attention_mask=_a				)[0]
           _A							:     List[Any]			=						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
           _A							:     List[Any]			=						int(ids_tensor((1,)		,     output_from_past.shape[-1]				)				)
           _A							:     Optional[int]			=						output_from_no_past[:, -3:, random_slice_idx]
           _A							:     int			=						output_from_past[:, :, random_slice_idx]

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







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,):
      if attention_mask is None:
            _A							:     List[str]			=						tf.cast(tf.math.not_equal(snake_case_,config.pad_token_id     ),tf.inta     )
      if decoder_attention_mask is None:
            _A							:     int			=						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							:     List[Any]			=						tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            _A							:     Dict			=						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            _A							:     Optional[int]			=						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 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _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 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						TFPegasusModelTester(self				)
           _A							:     List[Any]			=						ConfigTester(self		,     config_class=_a				)

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



     def 					a__	(  self				)						->							Any:
           _A							:     Union[str, Any]			=						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 						lowercase							(   unittest.TestCase		):
     _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 					a__	(  self				)						->							List[Any]:
           return AutoTokenizer.from_pretrained(self.model_name				)

     @cached_property
     def 					a__	(  self				)						->							List[Any]:
           _A							:     str			=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
           return model

     def 					a__	(  self		,     **_a				)						->							Tuple:
           _A							:     Dict			=						self.translate_src_text(**_a				)
           assert self.expected_text == generated_words

     def 					a__	(  self		,     **_a				)						->							Any:
           _A							:     str			=						self.tokenizer(self.src_text		,     **_a		,     padding=_a		,     return_tensors="""tf"""				)
           _A							:     Tuple			=						self.model.generate(
               model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     use_cache=_a		,     )
           _A							:     List[str]			=						self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=_a				)
           return generated_words



     @slow
     def 					a__	(  self				)						->							Optional[int]:
           self._assert_generated_batch_equal_expected()







from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

lowerCamelCase				:		List[str]     =   logging.get_logger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return [
          int(10_00 * (box[0] / width)     ),
          int(10_00 * (box[1] / height)     ),
          int(10_00 * (box[2] / width)     ),
          int(10_00 * (box[3] / height)     ),
      ]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       Optional[str]	,							lowercase	:       Optional[str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    to_pil_image(lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_		     =    pil_image.size
      lowerCamelCase_		     =    pytesseract.image_to_data(lowercase	,							lang=lowercase	,							output_type='dict'	,							config=lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    data['text'], data['left'], data['top'], data['width'], data['height']

      # filter empty words and corresponding coordinates
      lowerCamelCase_		     =    [idx for idx, word in enumerate(lowercase     ) if not word.strip()]
      lowerCamelCase_		     =    [word for idx, word in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      lowerCamelCase_		     =    []
      for x, y, w, h in zip(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    [x, y, x + w, y + h]
            actual_boxes.append(lowercase     )

      # finally, normalize the bounding boxes
      lowerCamelCase_		     =    []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(lowercase	,							lowercase	,							lowercase     )     )

      assert len(lowercase     ) == len(lowercase     ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  int						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  bool = True						,							A_					:  float = 1 / 255						,							A_					:  bool = True						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = True						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = ""						,							**A_					:  Optional[int]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_value
       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
       lowerCamelCase_		     =    apply_ocr
       lowerCamelCase_		     =    ocr_lang
       lowerCamelCase_		     =    tesseract_config

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
       lowerCamelCase_		     =    (size['height'], size['width'])
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Any						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Optional[Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  int						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  List[Any]						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  Dict=None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  ChannelDimension = ChannelDimension.FIRST						,							**A_					:  Any						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       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_		     =    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_		     =    apply_ocr if apply_ocr is not None else self.apply_ocr
       lowerCamelCase_		     =    ocr_lang if ocr_lang is not None else self.ocr_lang
       lowerCamelCase_		     =    tesseract_config if tesseract_config is not None else self.tesseract_config

       lowerCamelCase_		     =    make_list_of_images(A_  )

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

       if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize 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('If do_normalize is True, image_mean and image_std must be specified.'  )

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
             requires_backends(self						,							'pytesseract'  )
             lowerCamelCase_		     =    []
             lowerCamelCase_		     =    []
             for image in images:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    apply_tesseract(A_						,							A_						,							A_  )
                   words_batch.append(A_  )
                   boxes_batch.append(A_  )

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							resample=A_  ) for image in images]

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    BatchFeature(data={'pixel_values': images}						,							tensor_type=A_  )

       if apply_ocr:
             lowerCamelCase_		     =    words_batch
             lowerCamelCase_		     =    boxes_batch
       return data



