

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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__lowercase     : Optional[Any]       =						logging.get_logger(__name__)







def       lowercase					(	__A :					List[str]		)				->			List[List[ImageInput]]:





    '''simple docstring'''




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

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

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

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



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      List[str]					=   ['''pixel_values''']





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

      '''simple docstring'''
      super().__init__(**SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		size if size is not None else {"""shortest_edge""": 224}
      snake_case							:		str        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Union[str, Any]        =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      snake_case							:		Optional[int]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,param_name="""crop_size"""    )

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





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		int        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      if "shortest_edge" in size:
          snake_case							:		List[str]        =		get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,size["""shortest_edge"""]   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      elif "height" in size and "width" in size:
          snake_case							:		Optional[int]        =		(size["""height"""], size["""width"""])
      else:
          raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""    )
      return resize(SCREAMING_SNAKE_CASE_   ,size=SCREAMING_SNAKE_CASE_   ,resample=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		Dict        =		get_size_dict(SCREAMING_SNAKE_CASE_    )
      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(SCREAMING_SNAKE_CASE_   ,size=(size["""height"""], size["""width"""])   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      return rescale(SCREAMING_SNAKE_CASE_   ,scale=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      return normalize(SCREAMING_SNAKE_CASE_   ,mean=SCREAMING_SNAKE_CASE_   ,std=SCREAMING_SNAKE_CASE_   ,data_format=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,):

      '''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."""    )

      # All transformations expect numpy arrays.
      snake_case							:		int        =		to_numpy_array(SCREAMING_SNAKE_CASE_    )

      if do_resize:
          snake_case							:		Any        =		self.resize(image=SCREAMING_SNAKE_CASE_   ,size=SCREAMING_SNAKE_CASE_   ,resample=SCREAMING_SNAKE_CASE_    )

      if do_center_crop:
          snake_case							:		List[Any]        =		self.center_crop(SCREAMING_SNAKE_CASE_   ,size=SCREAMING_SNAKE_CASE_    )

      if do_rescale:
          snake_case							:		int        =		self.rescale(image=SCREAMING_SNAKE_CASE_   ,scale=SCREAMING_SNAKE_CASE_    )

      if do_normalize:
          snake_case							:		Tuple        =		self.normalize(image=SCREAMING_SNAKE_CASE_   ,mean=SCREAMING_SNAKE_CASE_   ,std=SCREAMING_SNAKE_CASE_    )

      snake_case							:		int        =		to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      return image





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		int        =		do_resize if do_resize is not None else self.do_resize
      snake_case							:		Union[str, Any]        =		resample if resample is not None else self.resample
      snake_case							:		Tuple        =		do_center_crop if do_center_crop is not None else self.do_center_crop
      snake_case							:		List[Any]        =		do_rescale if do_rescale is not None else self.do_rescale
      snake_case							:		Dict        =		rescale_factor if rescale_factor is not None else self.rescale_factor
      snake_case							:		List[Any]        =		do_normalize if do_normalize is not None else self.do_normalize
      snake_case							:		Any        =		image_mean if image_mean is not None else self.image_mean
      snake_case							:		List[Any]        =		image_std if image_std is not None else self.image_std

      snake_case							:		Dict        =		size if size is not None else self.size
      snake_case							:		List[Any]        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,default_to_square=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		crop_size if crop_size is not None else self.crop_size
      snake_case							:		Any        =		get_size_dict(SCREAMING_SNAKE_CASE_   ,param_name="""crop_size"""    )

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

      snake_case							:		List[str]        =		make_batched(SCREAMING_SNAKE_CASE_    )

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

      snake_case							:		Tuple        =		{"""pixel_values""": videos}
      return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,tensor_type=SCREAMING_SNAKE_CASE_    )









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    TransfoXLTokenizer
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								super().setUp()

								a									=				[
								    """<unk>""",
								    """[CLS]""",
								    """[SEP]""",
								    """want""",
								    """unwanted""",
								    """wa""",
								    """un""",
								    """running""",
								    """,""",
								    """low""",
								    """l""",
								]
								a									=				os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES["""vocab_file"""]							)
								with open(self.vocab_file						, """w"""						, encoding="""utf-8"""							) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)
							def  lowerCamelCase__				(		self		:List[Any]						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







								a									=				True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:int							):







								'''simple docstring'''







								a									=				"""<unk> UNwanted , running"""
								a									=				"""<unk> unwanted, running"""
								return input_text, output_text
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				TransfoXLTokenizer(vocab_file=self.vocab_file						, lower_case=__magic_name__							)

								a									=				tokenizer.tokenize("""<unk> UNwanted , running"""							)
								self.assertListEqual(__magic_name__						, ["""<unk>""", """unwanted""", """,""", """running"""]							)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__							)						, [0, 4, 8, 7]							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				TransfoXLTokenizer(lower_case=__magic_name__							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """							)						, ["""hello""", """!""", """how""", """are""", """you""", """?"""]							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				TransfoXLTokenizer(lower_case=__magic_name__							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """							)						, ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				TransfoXLTokenizer(lower_case=__magic_name__							)
								a									=				"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
								a									=				[
								    """Hello""",
								    """(""",
								    """bracket""",
								    """)""",
								    """and""",
								    """side""",
								    """@-@""",
								    """scrolled""",
								    """[""",
								    """and""",
								    """]""",
								    """Henry""",
								    """'s""",
								    """$""",
								    """5""",
								    """@,@""",
								    """000""",
								    """with""",
								    """3""",
								    """@.@""",
								    """34""",
								    """m""",
								    """.""",
								    """What""",
								    """'s""",
								    """up""",
								    """!""",
								    """?""",
								]

								self.assertListEqual(tokenizer.tokenize(__magic_name__							)						, __magic_name__							)

								self.assertEqual(tokenizer.convert_tokens_to_string(__magic_name__							)						, __magic_name__							)





							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								a									=				self.get_tokenizer()
								a									=				len(__magic_name__							)

								tokenizer.add_tokens(["""new1""", """new2"""]							)
								tokenizer.move_added_token("""new1"""						, 1							)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(__magic_name__							)						, original_len + 2							)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode("""new1"""							)						, [1]							)
								self.assertEqual(tokenizer.decode([1]							)						, """new1"""							)

