





'''simple docstring'''



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__   :						Any    =			get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")


@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						snake_case__					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   PegasusTokenizer
     UpperCAmelCase__       :						List[Any]      =   PegasusTokenizerFast
     UpperCAmelCase__       :						Optional[Any]      =   True
     UpperCAmelCase__       :						Dict      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _snake_case     		=				PegasusTokenizer(UpperCAmelCase_	)
       tokenizer.save_pretrained(self.tmpdirname	)

     @cached_property
     def 	UpperCamelCase(					self	):
       return PegasusTokenizer.from_pretrained("google/pegasus-large"	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return PegasusTokenizer.from_pretrained(self.tmpdirname						,   **UpperCAmelCase_	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return ("This is a test", "This is a test")

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"</s>"
       _snake_case     		=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_	)						,   UpperCAmelCase_	)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_	)						,   UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				list(self.get_tokenizer().get_vocab().keys()	)

       self.assertEqual(vocab_keys[0]						,   "<pad>"	)
       self.assertEqual(vocab_keys[1]						,   "</s>"	)
       self.assertEqual(vocab_keys[-1]						,   "v"	)
       self.assertEqual(len(UpperCAmelCase_	)						,   1_103	)

     def 	UpperCamelCase(					self	):
       self.assertEqual(self.get_tokenizer().vocab_size						,   1_103	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname	)
       _snake_case     		=				self.tokenizer_class.from_pretrained(self.tmpdirname	)
       _snake_case     		=				(
           "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
           " </s> <pad> <pad> <pad>"
       )
       _snake_case     		=				rust_tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_						,   add_special_tokens=UpperCAmelCase_	).input_ids[0]
       _snake_case     		=				py_tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_						,   add_special_tokens=UpperCAmelCase_	).input_ids[0]
       self.assertListEqual(UpperCAmelCase_						,   UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       _snake_case     		=				"<mask_1> To ensure a <mask_2> flow of bank resolutions."
       _snake_case     		=				[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
       _snake_case     		=				tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_	).input_ids[0]
       self.assertListEqual(UpperCAmelCase_						,   UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96_103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1_024
       _snake_case     		=				"To ensure a smooth flow of bank resolutions."
       _snake_case     		=				[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
       _snake_case     		=				tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_	).input_ids[0]
       self.assertListEqual(UpperCAmelCase_						,   UpperCAmelCase_	)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]	) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				["This is going to be way too long." * 150, "short example"]
       _snake_case     		=				["not super long but more than 5 tokens", "tiny"]
       _snake_case     		=				self._large_tokenizer(UpperCAmelCase_						,   padding=UpperCAmelCase_						,   truncation=UpperCAmelCase_						,   return_tensors="pt"	)
       _snake_case     		=				self._large_tokenizer(
           text_target=UpperCAmelCase_						,   max_length=5						,   padding=UpperCAmelCase_						,   truncation=UpperCAmelCase_						,   return_tensors="pt"	)

       assert batch.input_ids.shape == (2, 1_024)
       assert batch.attention_mask.shape == (2, 1_024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(UpperCAmelCase_	) == 2  # input_ids, attention_mask.






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=UpperCAmelCase_						,   model_name="google/bigbird-pegasus-large-arxiv"						,   revision="ba85d0851d708441f91440d509690f1ab6353415"						,   )







@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						snake_case__					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Tuple      =   PegasusTokenizer
     UpperCAmelCase__       :						Tuple      =   PegasusTokenizerFast
     UpperCAmelCase__       :						str      =   True
     UpperCAmelCase__       :						Dict      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _snake_case     		=				PegasusTokenizer(UpperCAmelCase_						,   offset=0						,   mask_token_sent=UpperCAmelCase_						,   mask_token="[MASK]"	)
       tokenizer.save_pretrained(self.tmpdirname	)

     @cached_property
     def 	UpperCamelCase(					self	):
       return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return PegasusTokenizer.from_pretrained(self.tmpdirname						,   **UpperCAmelCase_	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return ("This is a test", "This is a test")

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname	)
       _snake_case     		=				self.tokenizer_class.from_pretrained(self.tmpdirname	)
       _snake_case     		=				(
           "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
           " <pad> <pad> <pad>"
       )
       _snake_case     		=				rust_tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_						,   add_special_tokens=UpperCAmelCase_	).input_ids[0]
       _snake_case     		=				py_tokenizer([raw_input_str]						,   return_tensors=UpperCAmelCase_						,   add_special_tokens=UpperCAmelCase_	).input_ids[0]
       self.assertListEqual(UpperCAmelCase_						,   UpperCAmelCase_	)

     @require_torch
     def 	UpperCamelCase(					self	):
       _snake_case     		=				["This is going to be way too long." * 1_000, "short example"]
       _snake_case     		=				["not super long but more than 5 tokens", "tiny"]
       _snake_case     		=				self._large_tokenizer(UpperCAmelCase_						,   padding=UpperCAmelCase_						,   truncation=UpperCAmelCase_						,   return_tensors="pt"	)
       _snake_case     		=				self._large_tokenizer(
           text_target=UpperCAmelCase_						,   max_length=5						,   padding=UpperCAmelCase_						,   truncation=UpperCAmelCase_						,   return_tensors="pt"	)

       assert batch.input_ids.shape == (2, 4_096)
       assert batch.attention_mask.shape == (2, 4_096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(UpperCAmelCase_	) == 2  # input_ids, attention_mask.






     def 	UpperCamelCase(					self	):
       _snake_case     		=				(
           "This is an example string that is used to test the original TF implementation against the HF"
           " implementation"
       )

       _snake_case     		=				self._large_tokenizer(UpperCAmelCase_	).input_ids

       self.assertListEqual(
           UpperCAmelCase_						,   [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]						,   )









'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

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

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					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						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       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."	)

       if do_resize and size is None:
         raise ValueError("Size 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."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

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

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

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

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

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

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


