

"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  LongformerTokenizer
				SCREAMING_SNAKE_CASE_       :				Any       				=  True
				SCREAMING_SNAKE_CASE_       :				Any       				=  LongformerTokenizerFast
				SCREAMING_SNAKE_CASE_       :				Any       				=  True

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



						'''simple docstring'''


						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__lowerCAmelCase:	Optional[Any]   				=      [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						__lowerCAmelCase:	Tuple   				=      dict(zip(UpperCamelCase__					,  range(len(UpperCamelCase__))))
						__lowerCAmelCase:	str   				=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__lowerCAmelCase:	Optional[Any]   				=      {"unk_token": "<unk>"}

						__lowerCAmelCase:	Union[str, Any]   				=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase:	Any   				=      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(UpperCamelCase__) + "\n")
						with open(self.merges_file					,  "w"					,  encoding="utf-8") as fp:
								fp.write("\n".join(UpperCamelCase__))

				def 							lowercase_					(     self							:    List[str]					,  **UpperCamelCase__							:    Optional[Any])->       Tuple:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return self.tokenizer_class.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple					,  **UpperCamelCase__							:    Optional[int])->       Dict:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    Optional[int])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      "lower newer"
						__lowerCAmelCase:	Optional[Any]   				=      "lower newer"
						return input_text, output_text

				def 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map)
						__lowerCAmelCase:	Any   				=      "lower newer"
						__lowerCAmelCase:	Optional[int]   				=      ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
						__lowerCAmelCase:	List[str]   				=      tokenizer.tokenize(UpperCamelCase__)  # , add_prefix_space=True)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	str   				=      tokens + [tokenizer.unk_token]
						__lowerCAmelCase:	Tuple   				=      [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__)					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("Hello world!"					,  add_special_tokens=UpperCamelCase__)					,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2])
						self.assertListEqual(
						    tokenizer.encode("Hello world! cécé herlolip 418"					,  add_special_tokens=UpperCamelCase__)					,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]					,  )

				@slow
				def 							lowercase_					(     self							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      self.tokenizer_class.from_pretrained("allenai/longformer-base-4096")

						__lowerCAmelCase:	List[str]   				=      tokenizer.encode("sequence builders"					,  add_special_tokens=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.encode("multi-sequence build"					,  add_special_tokens=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      tokenizer.encode(
						    "sequence builders"					,  add_special_tokens=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      tokenizer.encode(
						    "sequence builders"					,  "multi-sequence build"					,  add_special_tokens=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      tokenizer.build_inputs_with_special_tokens(UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.build_inputs_with_special_tokens(UpperCamelCase__					,  UpperCamelCase__)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode

				def 							lowercase_					(     self							:    Union[str, Any])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.get_tokenizer()

						__lowerCAmelCase:	Optional[Any]   				=      "Encode this sequence."
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.byte_encoder[" ".encode("utf-8")[0]]

						# Testing encoder arguments
						__lowerCAmelCase:	Tuple   				=      tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
						self.assertNotEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      tokenizer.convert_ids_to_tokens(encoded[0])[0]
						self.assertEqual(UpperCamelCase__					,  UpperCamelCase__)

						tokenizer.add_special_tokens({"bos_token": "<s>"})
						__lowerCAmelCase:	str   				=      tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_ids_to_tokens(encoded[1])[0]
						self.assertNotEqual(UpperCamelCase__					,  UpperCamelCase__)

						# Testing spaces after special tokens
						__lowerCAmelCase:	Optional[Any]   				=      "<mask>"
						tokenizer.add_special_tokens(
						    {"mask_token": AddedToken(UpperCamelCase__					,  lstrip=UpperCamelCase__					,  rstrip=UpperCamelCase__)})  # mask token has a left space
						__lowerCAmelCase:	List[Any]   				=      tokenizer.convert_tokens_to_ids(UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      "Encode <mask> sequence"
						__lowerCAmelCase:	str   				=      "Encode <mask>sequence"

						__lowerCAmelCase:	Any   				=      tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      encoded.index(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
						self.assertEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      encoded.index(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
						self.assertNotEqual(UpperCamelCase__					,  UpperCamelCase__)

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



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:	Union[str, Any]   				=      self.rust_tokenizer_class.from_pretrained(UpperCamelCase__					,  **UpperCamelCase__)
										__lowerCAmelCase:	str   				=      self.tokenizer_class.from_pretrained(UpperCamelCase__					,  **UpperCamelCase__)
										__lowerCAmelCase:	int   				=      "A, <mask> AllenNLP sentence."
										__lowerCAmelCase:	Optional[Any]   				=      tokenizer_r.encode_plus(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  return_token_type_ids=UpperCamelCase__)
										__lowerCAmelCase:	Tuple   				=      tokenizer_p.encode_plus(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  return_token_type_ids=UpperCamelCase__)

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r["token_type_ids"])					,  sum(tokens_p["token_type_ids"]))

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])					,  sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])					,  )

										__lowerCAmelCase:	List[str]   				=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										__lowerCAmelCase:	str   				=      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p["input_ids"]					,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"]					,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

										self.assertSequenceEqual(
										    UpperCamelCase__					,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
										self.assertSequenceEqual(
										    UpperCamelCase__					,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])

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



						'''simple docstring'''


						for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2):
								__lowerCAmelCase:	str   				=      self.rust_tokenizer_class.from_pretrained(
								    self.tmpdirname					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)

								__lowerCAmelCase:	str   				=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
								__lowerCAmelCase:	int   				=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

								self.assertEqual(pre_tokenizer_state["add_prefix_space"]					,  UpperCamelCase__)

								self.assertEqual(post_processor_state["add_prefix_space"]					,  UpperCamelCase__)
								self.assertEqual(post_processor_state["trim_offsets"]					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    str)->       Dict:



						'''simple docstring'''


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

										__lowerCAmelCase:	Union[str, Any]   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	Optional[int]   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (len(UpperCamelCase__) + 1, len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	Union[str, Any]   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	Any   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (len(UpperCamelCase__) + 1, len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	int   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	str   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (len(UpperCamelCase__), len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	List[str]   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	int   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (len(UpperCamelCase__), len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	int   				=      f" {text}"

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										__lowerCAmelCase:	Tuple   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	str   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (1 + len(UpperCamelCase__) + 1, 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	List[str]   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	Optional[Any]   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (1 + len(UpperCamelCase__), 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )

										__lowerCAmelCase:	Any   				=      self.rust_tokenizer_class.from_pretrained(
										    UpperCamelCase__					,  use_fast=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__					,  trim_offsets=UpperCamelCase__)
										__lowerCAmelCase:	str   				=      tokenizer_r(UpperCamelCase__					,  return_offsets_mapping=UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
										self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(UpperCamelCase__)))
										self.assertEqual(
										    encoding.offset_mapping[1]					,  (1 + len(UpperCamelCase__), 1 + len(UpperCamelCase__) + 1 + len(UpperCamelCase__))					,  )





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[str]=3					,  UpperCamelCase__							:    List[Any]=1_8					,  UpperCamelCase__							:    List[Any]=3_0					,  UpperCamelCase__							:    List[str]=4_0_0					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    List[str]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    int=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__							:    str=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__							:    List[str]=True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:	Union[str, Any]   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	Optional[int]   				=      parent
						__lowerCAmelCase:	List[str]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	List[str]   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	List[Any]   				=      do_center_crop
						__lowerCAmelCase:	Optional[int]   				=      crop_size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	List[str]   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std
						__lowerCAmelCase:	str   				=      do_convert_rgb

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}




				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    Dict=False)->       List[str]:



						'''simple docstring'''


						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
								__lowerCAmelCase:	Optional[int]   				=      []
								for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        2_5_5					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
						else:
								__lowerCAmelCase:	List[str]   				=      []
								for i in range(self.batch_size):
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
										image_inputs.append(np.random.randint(2_5_5					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

						if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowerCAmelCase:	Union[str, Any]   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						if torchify:
								__lowerCAmelCase:	str   				=      [torch.from_numpy(UpperCamelCase__) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      ChineseCLIPImageProcessingTester(self					,  do_center_crop=UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_2_4, "width": 2_2_4})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

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



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	Optional[int]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	int   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

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



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Any   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      3

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       Any:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						pass




				def 							lowercase_					(     self							:    Tuple)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )


