



"""simple docstring"""
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

SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



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

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

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

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




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["pixel_values"]






							def __init__(   self			,     a_ = True			,     a_ = None			,     a_ = PILImageResampling.BILINEAR			,     a_ = True			,     a_ = None			,     a_ = True			,     a_ = 1 / 2_5_5			,     a_ = True			,     a_ = True			,     a_ = None			,     a_ = None			,     **a_			,     ):
									super().__init__(**a_      )
									a_     :      List[str] 						=     size if size is not None else {"shortest_edge": 2_5_6}
									a_     :      Optional[int] 						=     get_size_dict(a_			,     default_to_square=a_      )
									a_     :      Dict 						=     crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
									a_     :      List[Any] 						=     get_size_dict(a_			,     param_name="crop_size"      )

									a_     :      Any 						=     do_resize
									a_     :      str 						=     size
									a_     :      str 						=     do_center_crop
									a_     :      List[str] 						=     crop_size
									a_     :      Dict 						=     resample
									a_     :      str 						=     do_rescale
									a_     :      List[str] 						=     rescale_factor
									a_     :      Optional[int] 						=     offset
									a_     :      Tuple 						=     do_normalize
									a_     :      List[Any] 						=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									a_     :      int 						=     image_std if image_std is not None else IMAGENET_STANDARD_STD






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






							def snake_case_  (   self			,     a_			,     a_			,     a_ = None			,     **a_			,     ):
									a_     :      Any 						=     get_size_dict(a_      )
									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(a_			,     size=(size["height"], size["width"])			,     data_format=a_			,     **a_      )






							def snake_case_  (   self			,     a_			,     a_			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Any 						=     image.astype(np.floataa      )
									if offset:
											a_     :      List[str] 						=     image - (scale / 2)
									return rescale(a_			,     scale=a_			,     data_format=a_			,     **a_      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_ = None			,     **a_			,     ):
									return normalize(a_			,     mean=a_			,     std=a_			,     data_format=a_			,     **a_      )






							def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = 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.
									a_     :      List[Any] 						=     to_numpy_array(a_      )

									if do_resize:
											a_     :      int 						=     self.resize(image=a_			,     size=a_			,     resample=a_      )

									if do_center_crop:
											a_     :      Any 						=     self.center_crop(a_			,     size=a_      )

									if do_rescale:
											a_     :      int 						=     self.rescale(image=a_			,     scale=a_			,     offset=a_      )

									if do_normalize:
											a_     :      List[Any] 						=     self.normalize(image=a_			,     mean=a_			,     std=a_      )

									a_     :      List[str] 						=     to_channel_dimension_format(a_			,     a_      )
									return image






							def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = ChannelDimension.FIRST			,     **a_			,     ):
									a_     :      List[str] 						=     do_resize if do_resize is not None else self.do_resize
									a_     :      Any 						=     resample if resample is not None else self.resample
									a_     :      List[str] 						=     do_center_crop if do_center_crop is not None else self.do_center_crop
									a_     :      Any 						=     do_rescale if do_rescale is not None else self.do_rescale
									a_     :      str 						=     rescale_factor if rescale_factor is not None else self.rescale_factor
									a_     :      Union[str, Any] 						=     offset if offset is not None else self.offset
									a_     :      Optional[int] 						=     do_normalize if do_normalize is not None else self.do_normalize
									a_     :      int 						=     image_mean if image_mean is not None else self.image_mean
									a_     :      int 						=     image_std if image_std is not None else self.image_std

									a_     :      Tuple 						=     size if size is not None else self.size
									a_     :      Union[str, Any] 						=     get_size_dict(a_			,     default_to_square=a_      )
									a_     :      Union[str, Any] 						=     crop_size if crop_size is not None else self.crop_size
									a_     :      Optional[Any] 						=     get_size_dict(a_			,     param_name="crop_size"      )

									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."      )

									a_     :      List[str] 						=     make_batched(a_      )

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

									a_     :      List[Any] 						=     {"pixel_values": videos}
									return BatchFeature(data=a_			,     tensor_type=a_      )




"""simple docstring"""
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






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

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

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






							def snake_case_  (   self      ):
									a_     :      int 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<unk>"      )
									self.assertEqual(vocab_keys[1]			,     "<s>"      )
									self.assertEqual(vocab_keys[-1]			,     "j"      )
									self.assertEqual(len(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

									a_     :      str 						=     tokenizer.tokenize("This is a test"      )
									self.assertListEqual(a_			,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

									a_     :      str 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]			,     )
									# fmt: on

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

									a_     :      Optional[int] 						=     tokenizer.convert_ids_to_tokens(a_      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
									# fmt: on






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(a_			,     a_      ):
											self.assertListEqual(tokenizer.encode_fast(a_      )			,     a_      )

									# Test that decode_fast returns the input text
									for text, token_ids in zip(a_			,     a_      ):
											self.assertEqual(tokenizer.decode_fast(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
									    "Hey there, how are you doing this fine day?",
									    "This is a text with a trailing spaces followed by a dot     .",
									    "Häj sväjs lillebrör! =)",
									    "Det är inget fel på Mr. Cool",
									]

									# fmt: off
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[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, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )