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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase__  	=      logging.get_logger(__name__)
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					['pixel_values']


	def __init__(   self       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = PILImageResampling.BICUBIC       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 1 / 255       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		super().__init__(**lowerCAmelCase_       )
		_lowercase   =crop_size if crop_size is not None else {"height": 256, "width": 256}
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )
		_lowercase   =size if size is not None else {"height": 224, "width": 224}
		_lowercase   =get_size_dict(lowerCAmelCase_       )

		_lowercase   =do_center_crop
		_lowercase   =crop_size
		_lowercase   =do_resize
		_lowercase   =size
		_lowercase   =resample
		_lowercase   =do_rescale
		_lowercase   =rescale_factor
		_lowercase   =do_normalize
		_lowercase   =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		_lowercase   =image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =self.size if size is None else size
		_lowercase   =get_size_dict(lowerCAmelCase_       )
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )

		_lowercase   ,  _lowercase   =get_image_size(lowerCAmelCase_       )
		_lowercase   =min(lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =(size["height"] / crop_size["height"]) * min_dim
		_lowercase   =(size["width"] / crop_size["width"]) * min_dim
		return center_crop(lowerCAmelCase_       ,  size=(cropped_height, cropped_width)       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = PIL.Image.BICUBIC       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =get_size_dict(lowerCAmelCase_       )
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
		return resize(
		    lowerCAmelCase_       ,  size=(size["height"], size["width"])       ,  resample=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		return rescale(lowerCAmelCase_       ,  scale=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  **lowerCAmelCase_       ,  ):
		return normalize(lowerCAmelCase_       ,  mean=lowerCAmelCase_       ,  std=lowerCAmelCase_       ,  data_format=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   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_ = ChannelDimension.FIRST       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =do_center_crop if do_center_crop is not None else self.do_center_crop
		_lowercase   =crop_size if crop_size is not None else self.crop_size
		_lowercase   =get_size_dict(lowerCAmelCase_       ,  param_name="crop_size"       )
		_lowercase   =do_resize if do_resize is not None else self.do_resize
		_lowercase   =size if size is not None else self.size
		_lowercase   =get_size_dict(lowerCAmelCase_       )
		_lowercase   =resample if resample is not None else self.resample
		_lowercase   =do_rescale if do_rescale is not None else self.do_rescale
		_lowercase   =rescale_factor if rescale_factor is not None else self.rescale_factor
		_lowercase   =do_normalize if do_normalize is not None else self.do_normalize
		_lowercase   =image_mean if image_mean is not None else self.image_mean
		_lowercase   =image_std if image_std is not None else self.image_std

		_lowercase   =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_center_crop and crop_size is None:
			raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."       )

		if do_resize and size is None:
			raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."       )

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

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

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

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

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

		_lowercase   =[to_channel_dimension_format(lowerCAmelCase_       ,  lowerCAmelCase_       ) for image in images]

		_lowercase   ={"pixel_values": images}
		return BatchFeature(data=lowerCAmelCase_       ,  tensor_type=lowerCAmelCase_       )









"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )





