
'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE     =       get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							XLMRobertaTokenizer
 lowercase_					:    Tuple				=							XLMRobertaTokenizerFast
 lowercase_					:    Dict				=							True
 lowercase_					:    Optional[int]				=							True







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().setUp()

   # We have a SentencePiece fixture for testing
   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizer(snake_case__			,				keep_accents=snake_case__ )
   tokenizer.save_pretrained(self.tmpdirname )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"<pad>"
   UpperCAmelCase__      :					Optional[Any]				       =							1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ )			,				snake_case__ )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ )			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							list(self.get_tokenizer().get_vocab().keys() )

   self.assertEqual(vocab_keys[0]			,				"<s>" )
   self.assertEqual(vocab_keys[1]			,				"<pad>" )
   self.assertEqual(vocab_keys[-1]			,				"<mask>" )
   self.assertEqual(len(snake_case__ )			,				10_02 )







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   self.assertEqual(self.get_tokenizer().vocab_size			,				10_02 )







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							XLMRobertaTokenizer(snake_case__			,				keep_accents=snake_case__ )

   UpperCAmelCase__      :					Dict				       =							tokenizer.tokenize("This is a test" )
   self.assertListEqual(snake_case__			,				["▁This", "▁is", "▁a", "▁t", "est"] )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(snake_case__ )			,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]			,				)

   UpperCAmelCase__      :					str				       =							tokenizer.tokenize("I was born in 92000, and this is falsé." )
   self.assertListEqual(
       snake_case__			,				[
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "9",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "é",
           ".",
       ]			,				)
   UpperCAmelCase__      :					List[Any]				       =							tokenizer.convert_tokens_to_ids(snake_case__ )
   self.assertListEqual(
       snake_case__			,				[
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
       ]			,				)

   UpperCAmelCase__      :					int				       =							tokenizer.convert_ids_to_tokens(snake_case__ )
   self.assertListEqual(
       snake_case__			,				[
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "<unk>",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "<unk>",
           ".",
       ]			,				)







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   if not self.test_slow_tokenizer:
     # as we don't have a slow version, we can't compare the outputs between slow and fast versions
     return

   UpperCAmelCase__      :					Dict				       =							(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
       UpperCAmelCase__      :					str				       =							self.rust_tokenizer_class.from_pretrained(snake_case__			,				**snake_case__ )
       UpperCAmelCase__      :					List[str]				       =							self.tokenizer_class.from_pretrained(snake_case__			,				**snake_case__ )

       UpperCAmelCase__      :					List[str]				       =							tempfile.mkdtemp()

       UpperCAmelCase__      :					int				       =							tokenizer_r.save_pretrained(snake_case__ )
       UpperCAmelCase__      :					Tuple				       =							tokenizer_p.save_pretrained(snake_case__ )

       # Checks it save with the same files + the tokenizer.json file for the fast one
       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
       UpperCAmelCase__      :					List[Any]				       =							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
       self.assertSequenceEqual(snake_case__			,				snake_case__ )

       # Checks everything loads correctly in the same way
       UpperCAmelCase__      :					Any				       =							tokenizer_r.from_pretrained(snake_case__ )
       UpperCAmelCase__      :					Any				       =							tokenizer_p.from_pretrained(snake_case__ )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(snake_case__			,				snake_case__ ) )
         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

       shutil.rmtree(snake_case__ )

       # Save tokenizer rust, legacy_format=True
       UpperCAmelCase__      :					Tuple				       =							tempfile.mkdtemp()

       UpperCAmelCase__      :					Optional[int]				       =							tokenizer_r.save_pretrained(snake_case__			,				legacy_format=snake_case__ )
       UpperCAmelCase__      :					Dict				       =							tokenizer_p.save_pretrained(snake_case__ )

       # Checks it save with the same files
       self.assertSequenceEqual(snake_case__			,				snake_case__ )

       # Checks everything loads correctly in the same way
       UpperCAmelCase__      :					Union[str, Any]				       =							tokenizer_r.from_pretrained(snake_case__ )
       UpperCAmelCase__      :					Any				       =							tokenizer_p.from_pretrained(snake_case__ )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(snake_case__			,				snake_case__ ) )

       shutil.rmtree(snake_case__ )

       # Save tokenizer rust, legacy_format=False
       UpperCAmelCase__      :					str				       =							tempfile.mkdtemp()

       UpperCAmelCase__      :					Optional[int]				       =							tokenizer_r.save_pretrained(snake_case__			,				legacy_format=snake_case__ )
       UpperCAmelCase__      :					Optional[int]				       =							tokenizer_p.save_pretrained(snake_case__ )

       # Checks it saved the tokenizer.json file
       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )

       # Checks everything loads correctly in the same way
       UpperCAmelCase__      :					List[Any]				       =							tokenizer_r.from_pretrained(snake_case__ )
       UpperCAmelCase__      :					Union[str, Any]				       =							tokenizer_p.from_pretrained(snake_case__ )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(snake_case__			,				snake_case__ ) )

       shutil.rmtree(snake_case__ )







 @cached_property
 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base" )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   with tempfile.NamedTemporaryFile() as f:
     shutil.copyfile(snake_case__			,				f.name )
     UpperCAmelCase__      :					Optional[Any]				       =							XLMRobertaTokenizer(f.name			,				keep_accents=snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							pickle.dumps(snake_case__ )
   pickle.loads(snake_case__ )







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   if not self.test_rust_tokenizer:
     return

   UpperCAmelCase__      :					int				       =							self.get_tokenizer()
   UpperCAmelCase__      :					Union[str, Any]				       =							self.get_rust_tokenizer()

   UpperCAmelCase__      :					int				       =							"I was born in 92000, and this is falsé."

   UpperCAmelCase__      :					Optional[Any]				       =							tokenizer.tokenize(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							rust_tokenizer.tokenize(snake_case__ )
   self.assertListEqual(snake_case__			,				snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							tokenizer.encode(snake_case__			,				add_special_tokens=snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							rust_tokenizer.encode(snake_case__			,				add_special_tokens=snake_case__ )
   self.assertListEqual(snake_case__			,				snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							self.get_rust_tokenizer()
   UpperCAmelCase__      :					Union[str, Any]				       =							tokenizer.encode(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							rust_tokenizer.encode(snake_case__ )
   self.assertListEqual(snake_case__			,				snake_case__ )







 @slow
 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							"Hello World!"
   UpperCAmelCase__      :					Dict				       =							[0, 3_53_78, 66_61, 38, 2]
   # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
   # xlmr.eval()
   # xlmr.encode(symbols)

   self.assertListEqual(snake_case__			,				self.big_tokenizer.encode(snake_case__ ) )







 @slow
 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							(
       "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
       " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
   )
   UpperCAmelCase__      :					Dict				       =							[
       0,
       32_93,
       83,
       10,
       45_52,
       49_89,
       79_86,
       6_78,
       10,
       59_15,
       1_11,
       17_94_59,
       12_48_50,
       4,
       60_44,
       2_37,
       12,
       6,
       5,
       6,
       4,
       67_80,
       7_05,
       15,
       13_88,
       44,
       3_78,
       1_01_14,
       7_11,
       1_52,
       20,
       6,
       5,
       2_23_76,
       6_42,
       12_21,
       1_51_90,
       3_41_53,
       4_50,
       56_08,
       9_59,
       11_19,
       5_77_02,
       1_36,
       1_86,
       47,
       10_98,
       2_93_67,
       47,
       # 4426, # What fairseq tokenizes from "<unk>": "_<"
       # 3678, # What fairseq tokenizes from "<unk>": "unk"
       # 2740, # What fairseq tokenizes from "<unk>": ">"
       3,  # What we tokenize from "<unk>": "<unk>"
       6,  # Residue from the tokenization: an extra sentencepiece underline
       4,
       60_44,
       2_37,
       62_84,
       5_09_01,
       5_28,
       31,
       90,
       34,
       9_27,
       2,
   ]
   # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
   # xlmr.eval()
   # xlmr.encode(symbols)

   self.assertListEqual(snake_case__			,				self.big_tokenizer.encode(snake_case__ ) )







 @slow
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							{"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=snake_case__			,				model_name="xlm-roberta-base"			,				revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"			,				)





'''simple docstring'''





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							size if size is not None else {"height": 18, "width": 18}
   UpperCAmelCase__      :					List[str]				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					List[str]				       =							num_channels
   UpperCAmelCase__      :					List[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							min_resolution
   UpperCAmelCase__      :					Optional[int]				       =							max_resolution
   UpperCAmelCase__      :					Any				       =							do_resize
   UpperCAmelCase__      :					Any				       =							size
   UpperCAmelCase__      :					int				       =							do_normalize







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return {
       # here we create 2 clusters for the sake of simplicity
       "clusters": np.asarray(
           [
               [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
               [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
           ] ),
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
   }






@require_torch
@require_vision
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(snake_case__			,				"clusters" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_resize" ) )
   self.assertTrue(hasattr(snake_case__			,				"size" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_normalize" ) )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							self.image_processing_class.from_dict(self.image_processor_dict )
   self.assertEqual(image_processor.size			,				{"height": 18, "width": 18} )

   UpperCAmelCase__      :					Union[str, Any]				       =							self.image_processing_class.from_dict(self.image_processor_dict			,				size=42 )
   self.assertEqual(image_processor.size			,				{"height": 42, "width": 42} )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					List[str]				       =							json.loads(image_processor.to_json_string() )
   for key, value in self.image_processor_dict.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				obj[key] ) )
     else:
       self.assertEqual(obj[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Union[str, Any]				       =							os.path.join(snake_case__			,				"image_processor.json" )
     image_processor_first.to_json_file(snake_case__ )
     UpperCAmelCase__      :					Any				       =							self.image_processing_class.from_json_file(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Union[str, Any]				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self.image_processing_class.from_pretrained(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Tuple				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 @unittest.skip("ImageGPT requires clusters at initialization" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

  UpperCAmelCase__      :					Tuple				       =							Image.open(dataset[4]["file"]		)
  UpperCAmelCase__      :					List[Any]				       =							Image.open(dataset[5]["file"]		)

  UpperCAmelCase__      :					Optional[Any]				       =							[imagea, imagea]

  return images






@require_vision
@require_torch
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

   UpperCAmelCase__      :					Union[str, Any]				       =							prepare_images()

   # test non-batched
   UpperCAmelCase__      :					Optional[Any]				       =							image_processing(images[0]			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(1, 10_24) )

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

   # test batched
   UpperCAmelCase__      :					List[Any]				       =							image_processing(snake_case__			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(2, 10_24) )

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



