


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

lowerCamelCase						=		logging.get_logger(__name__)






def      lowerCamelCase_    (       _a       ):




		"""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 					_a							(				a_):
	_a  :  Optional[Any]          =							['''pixel_values''']




	def __init__(					self						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Dict = True		,					_SCREAMING_SNAKE_CASE						:		List[str] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[int] = PILImageResampling.BILINEAR		,					_SCREAMING_SNAKE_CASE						:		Any = True		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		,					_SCREAMING_SNAKE_CASE						:		int = True		,					_SCREAMING_SNAKE_CASE						:		Any = 1 / 255		,					_SCREAMING_SNAKE_CASE						:		str = True		,					_SCREAMING_SNAKE_CASE						:		Any = True		,					_SCREAMING_SNAKE_CASE						:		List[Any] = None		,					_SCREAMING_SNAKE_CASE						:		str = None		,					**_SCREAMING_SNAKE_CASE						:		Any		,					)->       int:
			super().__init__(**_A		)
			lowerCAmelCase__   :			str         =					size if size is not None else {'''shortest_edge''': 256}
			lowerCAmelCase__   :			List[Any]         =					get_size_dict(_A		,					default_to_square=_A		)
			lowerCAmelCase__   :			int         =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase__   :			Tuple         =					get_size_dict(_A		,					param_name='''crop_size'''		)

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




	def 							UpperCAmelCase__(					self						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = PILImageResampling.BILINEAR		,					_SCREAMING_SNAKE_CASE						:		Any = None		,					**_SCREAMING_SNAKE_CASE						:		str		,					)->       Dict:
			lowerCAmelCase__   :			int         =					get_size_dict(_A		,					default_to_square=_A		)
			if "shortest_edge" in size:
					lowerCAmelCase__   :			Tuple         =					get_resize_output_image_size(_A		,					size['''shortest_edge''']		,					default_to_square=_A		)
			elif "height" in size and "width" in size:
					lowerCAmelCase__   :			Tuple         =					(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 							UpperCAmelCase__(					self						:		int		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Tuple = None		,					**_SCREAMING_SNAKE_CASE						:		Dict		,					)->       List[Any]:
			lowerCAmelCase__   :			List[str]         =					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 							UpperCAmelCase__(					self						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[Any] = True		,					_SCREAMING_SNAKE_CASE						:		Tuple = None		,					**_SCREAMING_SNAKE_CASE						:		str		,					)->       Union[str, Any]:
			lowerCAmelCase__   :			List[Any]         =					image.astype(np.floataa		)
			if offset:
					lowerCAmelCase__   :			str         =					image - (scale / 2)
			return rescale(_A		,					scale=_A		,					data_format=_A		,					**_A		)




	def 							UpperCAmelCase__(					self						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Any = None		,					**_SCREAMING_SNAKE_CASE						:		Dict		,					)->       Union[str, Any]:
			return normalize(_A		,					mean=_A		,					std=_A		,					data_format=_A		,					**_A		)




	def 							UpperCAmelCase__(					self						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Optional[int] = None		,					_SCREAMING_SNAKE_CASE						:		Dict = None		,					_SCREAMING_SNAKE_CASE						:		Optional[Any] = None		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		,					_SCREAMING_SNAKE_CASE						:		Any = None		,					_SCREAMING_SNAKE_CASE						:		List[Any] = None		,					_SCREAMING_SNAKE_CASE						:		Optional[Any] = None		,					_SCREAMING_SNAKE_CASE						:		Dict = None		,					_SCREAMING_SNAKE_CASE						:		Dict = None		,					_SCREAMING_SNAKE_CASE						:		Tuple = ChannelDimension.FIRST		,					)->       List[Any]:







			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__   :			List[str]         =					to_numpy_array(_A		)

			if do_resize:
					lowerCAmelCase__   :			Optional[Any]         =					self.resize(image=_A		,					size=_A		,					resample=_A		)

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

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

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

			lowerCAmelCase__   :			Dict         =					to_channel_dimension_format(_A		,					_A		)
			return image




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

			lowerCAmelCase__   :			Dict         =					size if size is not None else self.size
			lowerCAmelCase__   :			Dict         =					get_size_dict(_A		,					default_to_square=_A		)
			lowerCAmelCase__   :			List[Any]         =					crop_size if crop_size is not None else self.crop_size
			lowerCAmelCase__   :			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.'''		)

			lowerCAmelCase__   :			Optional[Any]         =					make_batched(_A		)

			lowerCAmelCase__   :			Optional[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
			]

			lowerCAmelCase__   :			Optional[Any]         =					{'''pixel_values''': videos}
			return BatchFeature(data=_A		,					tensor_type=_A		)











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



