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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Dict            =				LongformerTokenizer
  lowercase		:     Optional[int]            =				True
  lowercase		:     Union[str, Any]            =				LongformerTokenizerFast
  lowercase		:     Any            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Any:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         A						:     Tuple         =[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         A						:     Any         =dict(zip(SCREAMING_SNAKE_CASE__      ,	range(len(SCREAMING_SNAKE_CASE__     )     )     )     )
         A						:     List[Any]         =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         A						:     Dict         ={'unk_token': '<unk>'}

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Any:
         kwargs.update(self.special_tokens_map     )
         return self.tokenizer_class.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					int     )    ->				Dict:
         kwargs.update(self.special_tokens_map     )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				int:
         A						:     Union[str, Any]         ='lower newer'
         A						:     Optional[Any]         ='lower newer'
         return input_text, output_text







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				List[Any]:
         A						:     Tuple         =self.tokenizer_class(self.vocab_file      ,	self.merges_file      ,	**self.special_tokens_map     )
         A						:     Union[str, Any]         ='lower newer'
         A						:     Optional[int]         =['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         A						:     Optional[int]         =tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )  # , add_prefix_space=True)
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Optional[Any]         =tokens + [tokenizer.unk_token]
         A						:     Optional[int]         =[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[str]:
         A						:     Dict         =self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('Hello world!'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )      ,	[0, 3_14_14, 2_32, 3_28, 2]     )
         self.assertListEqual(
             tokenizer.encode('Hello world! cécé herlolip 418'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )      ,	[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,	)







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Union[str, Any]:
         A						:     Optional[Any]         =self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'     )

         A						:     List[str]         =tokenizer.encode('sequence builders'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         A						:     Any         =tokenizer.encode('multi-sequence build'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )

         A						:     int         =tokenizer.encode(
             'sequence builders'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =tokenizer.encode(
             'sequence builders'      ,	'multi-sequence build'      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__     )

         A						:     int         =tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Any:
         A						:     Any         =self.get_tokenizer()

         A						:     int         ='Encode this sequence.'
         A						:     str         =tokenizer.byte_encoder[' '.encode('utf-8'     )[0]]

         # Testing encoder arguments
         A						:     Optional[Any]         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
         self.assertNotEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     int         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
         self.assertEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         tokenizer.add_special_tokens({'bos_token': '<s>'}     )
         A						:     Union[str, Any]         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         A						:     List[Any]         =tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
         self.assertNotEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         # Testing spaces after special tokens
         A						:     Dict         ='<mask>'
         tokenizer.add_special_tokens(
             {'mask_token': AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     )}     )  # mask token has a left space
         A						:     List[str]         =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )

         A						:     Any         ='Encode <mask> sequence'
         A						:     int         ='Encode <mask>sequence'

         A						:     Tuple         =tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =encoded.index(SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
         self.assertEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Optional[Any]         =tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =encoded.index(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
         self.assertNotEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Dict:
         pass







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                       A						:     List[str]         =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       A						:     int         ='A, <mask> AllenNLP sentence.'
                       A						:     Optional[Any]         =tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	return_token_type_ids=SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__      ,	return_token_type_ids=SCREAMING_SNAKE_CASE__     )

                       # 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']     )      ,	)

                       A						:     Any         =tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
                       A						:     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_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
                       self.assertSequenceEqual(tokens_r['input_ids']      ,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[Any]:
         for trim_offsets, add_prefix_space in itertools.product([True, False]      ,	repeat=2     ):
                A						:     str         =self.rust_tokenizer_class.from_pretrained(
                    self.tmpdirname      ,	use_fast=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	trim_offsets=SCREAMING_SNAKE_CASE__     )

                A						:     Union[str, Any]         =json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
                A						:     Any         =json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

                self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,	SCREAMING_SNAKE_CASE__     )

                self.assertEqual(post_processor_state['add_prefix_space']      ,	SCREAMING_SNAKE_CASE__     )
                self.assertEqual(post_processor_state['trim_offsets']      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Any:
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                       A						:     Optional[int]         ='hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                       A						:     List[str]         =f'{text_of_1_token} {text_of_1_token}'

                       A						:     List[Any]         =self.rust_tokenizer_class.from_pretrained(
                           SCREAMING_SNAKE_CASE__      ,	use_fast=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	trim_offsets=SCREAMING_SNAKE_CASE__     )
                       A						:     Tuple         =tokenizer_r(SCREAMING_SNAKE_CASE__      ,	return_offsets_mapping=SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
                       self.assertEqual(encoding.offset_mapping[0]      ,	(0, len(SCREAMING_SNAKE_CASE__     ))     )
                       self.assertEqual(
                           encoding.offset_mapping[1]      ,	(len(SCREAMING_SNAKE_CASE__     ) + 1, len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))      ,	)

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

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

                       A						:     Dict         =self.rust_tokenizer_class.from_pretrained(
                           SCREAMING_SNAKE_CASE__      ,	use_fast=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	trim_offsets=SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_r(SCREAMING_SNAKE_CASE__      ,	return_offsets_mapping=SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
                       self.assertEqual(encoding.offset_mapping[0]      ,	(0, len(SCREAMING_SNAKE_CASE__     ))     )
                       self.assertEqual(
                           encoding.offset_mapping[1]      ,	(len(SCREAMING_SNAKE_CASE__     ), len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))      ,	)

                       A						:     Any         =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)),
                       # )

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

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

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







import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConditionalDetrImageProcessor



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def __init__(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Tuple=7      ,	SCREAMING_SNAKE_CASE__    :					Dict=3      ,	SCREAMING_SNAKE_CASE__    :					Tuple=30      ,	SCREAMING_SNAKE_CASE__    :					int=4_00      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=[0.5, 0.5, 0.5]      ,	SCREAMING_SNAKE_CASE__    :					str=[0.5, 0.5, 0.5]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Any=1 / 2_55      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	)    ->				Optional[int]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         A						:     Optional[Any]         =size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
         A						:     Union[str, Any]         =parent
         A						:     Union[str, Any]         =batch_size
         A						:     Union[str, Any]         =num_channels
         A						:     int         =min_resolution
         A						:     List[Any]         =max_resolution
         A						:     Dict         =do_resize
         A						:     Tuple         =size
         A						:     List[str]         =do_normalize
         A						:     List[Any]         =image_mean
         A						:     Dict         =image_std
         A						:     Any         =do_rescale
         A						:     List[str]         =rescale_factor
         A						:     Optional[Any]         =do_pad







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[Any]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Dict=False     )    ->				Dict:
         if not batched:
                A						:     Any         =image_inputs[0]
                if isinstance(SCREAMING_SNAKE_CASE__      ,	Image.Image     ):
                       A  ,  A						:     Union[str, Any]         =image.size
                else:
                       A  ,  A						:     Tuple         =image.shape[1], image.shape[2]
                if w < h:
                       A						:     Any         =int(self.size['shortest_edge'] * h / w     )
                       A						:     Any         =self.size['shortest_edge']
                elif w > h:
                       A						:     Dict         =self.size['shortest_edge']
                       A						:     Dict         =int(self.size['shortest_edge'] * w / h     )
                else:
                       A						:     List[str]         =self.size['shortest_edge']
                       A						:     Dict         =self.size['shortest_edge']

         else:
                A						:     List[Any]         =[]
                for image in image_inputs:
                       A  ,  A						:     int         =self.get_expected_values([image]     )
                       expected_values.append((expected_height, expected_width)     )
                A						:     str         =max(SCREAMING_SNAKE_CASE__      ,	key=lambda SCREAMING_SNAKE_CASE__     : item[0]     )[0]
                A						:     Tuple         =max(SCREAMING_SNAKE_CASE__      ,	key=lambda SCREAMING_SNAKE_CASE__     : item[1]     )[1]

         return expected_height, expected_width







@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				ConditionalDetrImageProcessor if is_vision_available() else None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Tuple:
         A						:     str         =ConditionalDetrImageProcessingTester(self     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         return self.image_processor_tester.prepare_image_processor_dict()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Tuple:
         A						:     Tuple         =self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_mean'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_std'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_normalize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_resize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'size'     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     int         =self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size      ,	{'shortest_edge': 18, 'longest_edge': 13_33}     )
         self.assertEqual(image_processor.do_pad      ,	SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.image_processing_class.from_dict(
             self.image_processor_dict      ,	size=42      ,	max_size=84      ,	pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__     )
         self.assertEqual(image_processor.size      ,	{'shortest_edge': 42, 'longest_edge': 84}     )
         self.assertEqual(image_processor.do_pad      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         pass







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         # Initialize image_processing
         A						:     Union[str, Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         A						:     Tuple         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	Image.Image     )

         # Test not batched input
         A						:     List[Any]         =image_processing(image_inputs[0]      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     List[str]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

         self.assertEqual(
             encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

         # Test batched
         A  ,  A						:     Union[str, Any]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values
         self.assertEqual(
             encoded_images.shape      ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         A						:     str         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	numpify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	np.ndarray     )

         # Test not batched input
         A						:     Tuple         =image_processing(image_inputs[0]      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     Any         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

         self.assertEqual(
             encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     Optional[int]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

         self.assertEqual(
             encoded_images.shape      ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[str]:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         A						:     Any         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	torchify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	torch.Tensor     )

         # Test not batched input
         A						:     Optional[int]         =image_processing(image_inputs[0]      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     Tuple         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

         self.assertEqual(
             encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     int         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

         self.assertEqual(
             encoded_images.shape      ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,	)







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Union[str, Any]:
         # prepare image and target
         A						:     Union[str, Any]         =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,	'r'     ) as f:
                A						:     List[Any]         =json.loads(f.read()     )

         A						:     Any         ={'image_id': 3_97_69, 'annotations': target}

         # encode them
         A						:     str         =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'     )
         A						:     Any         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         # verify pixel values
         A						:     Optional[Any]         =torch.Size([1, 3, 8_00, 10_66]     )
         self.assertEqual(encoding['pixel_values'].shape      ,	SCREAMING_SNAKE_CASE__     )

         A						:     List[str]         =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Dict         =torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     str         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     Dict         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     List[str]         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     Union[str, Any]         =torch.tensor([75, 75, 63, 65, 17, 17]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify orig_size
         A						:     List[Any]         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     int         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         # prepare image, target and masks_path
         A						:     List[str]         =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,	'r'     ) as f:
                A						:     Optional[int]         =json.loads(f.read()     )

         A						:     int         ={'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

         A						:     Optional[Any]         =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

         # encode them
         A						:     List[Any]         =ConditionalDetrImageProcessor(format='coco_panoptic'     )
         A						:     Union[str, Any]         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	masks_path=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         # verify pixel values
         A						:     Dict         =torch.Size([1, 3, 8_00, 10_66]     )
         self.assertEqual(encoding['pixel_values'].shape      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Optional[int]         =torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     List[Any]         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Any         =torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     List[Any]         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     Any         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     str         =torch.tensor([17, 17, 63, 75, 75, 93]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify masks
         A						:     int         =82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,	SCREAMING_SNAKE_CASE__     )
         # verify orig_size
         A						:     Any         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     str         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )






