



import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  CLIPTokenizer
			UpperCamelCase           =  CLIPTokenizerFast
			UpperCamelCase           =  True
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					List[str]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
						# fmt: on
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))
						_UpperCAmelCase	 =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
						_UpperCAmelCase	 =     {'unk_token': '<unk>'}

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')
						with open(self.merges_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write('\n'.join(A))



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						Any)     ->					Dict:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						str				,	**A     :						List[Any])     ->					List[str]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						int)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     'lower newer'
						_UpperCAmelCase	 =     'lower newer'
						return input_text, output_text



			def 					_lowerCamelCase (		self     :						int)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     CLIPTokenizer(self.vocab_file				,	self.merges_file				,	**self.special_tokens_map)
						_UpperCAmelCase	 =     'lower newer'
						_UpperCAmelCase	 =     ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
						_UpperCAmelCase	 =     tokenizer.tokenize(A)
						self.assertListEqual(A				,	A)

						_UpperCAmelCase	 =     tokens + [tokenizer.unk_token]
						_UpperCAmelCase	 =     [10, 2, 16, 9, 3, 2, 16, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)				,	A)



			@require_ftfy
			def 					_lowerCamelCase (		self     :						List[Any])     ->					str:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase	 =     self.tokenizer_class.from_pretrained(A				,	**A)
												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(A				,	**A)

												_UpperCAmelCase	 =     'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
												_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

												self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												_UpperCAmelCase	 =     'xa\u0303y' + ' ' + 'x\xe3y'
												_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
												_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

												self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on unicode of space type
												_UpperCAmelCase	 =     [
												    '\u0009',  # (horizontal tab, '\t')
												    '\u000B',  # (vertical tab)
												    '\u000C',  # (form feed)
												    '\u0020',  # (space, ' ')
												    '\u200E',  # (left-to-right mark):w
												    '\u200F',  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
															_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
															_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

															self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on unicode of line break type
												_UpperCAmelCase	 =     [
												    '\u000A',  # (line feed, '\n')
												    '\r\n',  # (carriage return and line feed, '\r\n')
												    '\u000D',  # (carriage return, '\r')
												    '\r',  # (carriage return, '\r')
												    '\u000D',  # (carriage return, '\r')
												    '\u2028',  # (line separator)
												    '\u2029',  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
															_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
															_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

															self.assertListEqual(A				,	A)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Union[str, Any]:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase	 =     'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
												_UpperCAmelCase	 =     F"{text_of_1_token} {text_of_1_token}"

												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(
												    A				,	use_fast=A				,	)
												_UpperCAmelCase	 =     tokenizer_r(A				,	return_offsets_mapping=A				,	add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]				,	(0, len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]				,	(len(A) + 1, len(A) + 1 + len(A))				,	)

												_UpperCAmelCase	 =     F" {text}"

												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(
												    A				,	use_fast=A				,	)
												_UpperCAmelCase	 =     tokenizer_r(A				,	return_offsets_mapping=A				,	add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]				,	(1, 1 + len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]				,	(1 + len(A) + 1, 1 + len(A) + 1 + len(A))				,	)



			def 					_lowerCamelCase (		self     :						Any)     ->					Optional[int]:



						"""simple docstring"""






						with self.assertRaises(A) as context:
									self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer')

						self.assertTrue(
						    context.exception.args[0].startswith(
						        'The `backend_tokenizer` provided does not match the expected format.'))



			@require_ftfy
			def 					_lowerCamelCase (		self     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						super().test_tokenization_python_rust_equals()



			def 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






						pass





from typing import Dict, List, Optional, Tuple, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Any				,	A     :						bool = True				,	A     :						Optional[Dict[str, int]] = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 2_56}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_center_crop
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						PILImageResampling = PILImageResampling.BICUBIC				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						List[str]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
						_UpperCAmelCase	 =     get_resize_output_image_size(A				,	size=size['shortest_edge']				,	default_to_square=A)
						return resize(A				,	size=A				,	resample=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Union[str, Any]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A)
						if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(A				,	size=(size['height'], size['width'])				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						float				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict)     ->					np.ndarray:



						"""simple docstring"""






						return rescale(A				,	scale=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						int				,	A     :						np.ndarray				,	A     :						Union[float, List[float]]				,	A     :						Union[float, List[float]]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict				,	)     ->					np.ndarray:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						ImageInput				,	A     :						Optional[bool] = None				,	A     :						Dict[str, int] = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[float] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST				,	**A     :						int				,	)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else self.image_mean
						_UpperCAmelCase	 =     image_std if image_std is not None else self.image_std

						_UpperCAmelCase	 =     make_list_of_images(A)

						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.')

						if do_resize and size is None:
									raise ValueError('Size 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.
						_UpperCAmelCase	 =     [to_numpy_array(A) for image in images]

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

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

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

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

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Any				,	A     :						List[Tuple] = None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(A) != len(A):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

									if is_torch_tensor(A):
												_UpperCAmelCase	 =     target_sizes.numpy()

									_UpperCAmelCase	 =     []

									for idx in range(len(A)):
												_UpperCAmelCase	 =     torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0)				,	size=target_sizes[idx]				,	mode='bilinear'				,	align_corners=A)
												_UpperCAmelCase	 =     resized_logits[0].argmax(dim=0)
												semantic_segmentation.append(A)
						else:
									_UpperCAmelCase	 =     logits.argmax(dim=1)
									_UpperCAmelCase	 =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation
