






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        _a  (     UpperCamelCase_					:				int   ) ->   List[List[ImageInput]]:
						"""simple docstring"""







						if isinstance(UpperCamelCase_    ,   (list, tuple)   ) and isinstance(videos[0]    ,   (list, tuple)   ) and is_valid_image(videos[0][0]   ):
												return videos

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

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

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





class      lowercase__     (			_UpperCAmelCase	):
	a_         =["""pixel_values"""]

	def __init__(							self		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = PILImageResampling.BILINEAR		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = True		,    __UpperCAmelCase = 1 / 255		,    __UpperCAmelCase = True		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					None:







							'''simple docstring'''





							super().__init__(**__UpperCAmelCase       )
							lowerCAmelCase__       =						size if size is not None else {"shortest_edge": 256}
							lowerCAmelCase__       =						get_size_dict(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase       )
							lowerCAmelCase__       =						crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowerCAmelCase__       =						get_size_dict(__UpperCAmelCase		,    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 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = PILImageResampling.BILINEAR		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





							lowerCAmelCase__       =						get_size_dict(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase       )
							if "shortest_edge" in size:
													lowerCAmelCase__       =						get_resize_output_image_size(__UpperCAmelCase		,    size["shortest_edge"]		,    default_to_square=__UpperCAmelCase       )
							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(__UpperCAmelCase		,    size=__UpperCAmelCase		,    resample=__UpperCAmelCase		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





							lowerCAmelCase__       =						get_size_dict(__UpperCAmelCase       )
							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(__UpperCAmelCase		,    size=(size["height"], size["width"])		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						image.astype(np.floataa       )
							if offset:
													lowerCAmelCase__       =						image - (scale / 2)
							return rescale(__UpperCAmelCase		,    scale=__UpperCAmelCase		,    data_format=__UpperCAmelCase		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = ChannelDimension.FIRST		,    )->					np.ndarray:







							'''simple docstring'''





							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(__UpperCAmelCase       )

							if do_resize:
													lowerCAmelCase__       =						self.resize(image=__UpperCAmelCase		,    size=__UpperCAmelCase		,    resample=__UpperCAmelCase       )

							if do_center_crop:
													lowerCAmelCase__       =						self.center_crop(__UpperCAmelCase		,    size=__UpperCAmelCase       )

							if do_rescale:
													lowerCAmelCase__       =						self.rescale(image=__UpperCAmelCase		,    scale=__UpperCAmelCase		,    offset=__UpperCAmelCase       )

							if do_normalize:
													lowerCAmelCase__       =						self.normalize(image=__UpperCAmelCase		,    mean=__UpperCAmelCase		,    std=__UpperCAmelCase       )

							lowerCAmelCase__       =						to_channel_dimension_format(__UpperCAmelCase		,    __UpperCAmelCase       )
							return image


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = ChannelDimension.FIRST		,    **__UpperCAmelCase		,    )->					PIL.Image.Image:







							'''simple docstring'''





							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(__UpperCAmelCase		,    default_to_square=__UpperCAmelCase       )
							lowerCAmelCase__       =						crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase__       =						get_size_dict(__UpperCAmelCase		,    param_name="crop_size"       )

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

							lowerCAmelCase__       =						make_batched(__UpperCAmelCase       )

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

							lowerCAmelCase__       =						{"pixel_values": videos}
							return BatchFeature(data=__UpperCAmelCase		,    tensor_type=__UpperCAmelCase       )














import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =None
	a_         =BloomTokenizerFast
	a_         =BloomTokenizerFast
	a_         =True
	a_         =False
	a_         ="""tokenizer_file"""
	a_         ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						BloomTokenizerFast.from_pretrained("bigscience/tokenizer"       )
							tokenizer.save_pretrained(self.tmpdirname       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							kwargs.update(self.special_tokens_map       )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase__       =						[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

							lowerCAmelCase__       =						tokenizer.batch_encode_plus(__UpperCAmelCase       )["input_ids"]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.batch_decode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=6       )->					str:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			# tokenizer_r.pad_token = None # Hotfixing padding = None
																			# Simple input
																			lowerCAmelCase__       =						"This is a simple input"
																			lowerCAmelCase__       =						["This is a simple input 1", "This is a simple input 2"]
																			lowerCAmelCase__       =						("This is a simple input", "This is a pair")
																			lowerCAmelCase__       =						[
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			try:
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )

																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																			except ValueError:
																									self.fail("Bloom Tokenizer should be able to deal with padding"       )

																			lowerCAmelCase__       =						None  # Hotfixing padding = None
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						load_dataset("xnli"		,    "all_languages"		,    split="test"		,    streaming=__UpperCAmelCase       )

							lowerCAmelCase__       =						next(iter(__UpperCAmelCase       )       )["premise"]  # pick up one data
							lowerCAmelCase__       =						list(sample_data.values()       )

							lowerCAmelCase__       =						list(map(tokenizer.encode		,    __UpperCAmelCase       )       )
							lowerCAmelCase__       =						[tokenizer.decode(__UpperCAmelCase		,    clean_up_tokenization_spaces=__UpperCAmelCase       ) for x in output_tokens]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )






