"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=30		,    lowerCAmelCase=2		,    lowerCAmelCase=3		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=5		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=10		,    lowerCAmelCase=0.02		,    lowerCAmelCase=None		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			image_size
										snake_case								=			patch_size
										snake_case								=			num_channels
										snake_case								=			is_training
										snake_case								=			use_labels
										snake_case								=			hidden_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			intermediate_size
										snake_case								=			hidden_act
										snake_case								=			hidden_dropout_prob
										snake_case								=			attention_probs_dropout_prob
										snake_case								=			type_sequence_label_size
										snake_case								=			initializer_range
										snake_case								=			scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										snake_case								=			(image_size // patch_size) ** 2
										snake_case								=			num_patches + 1

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										snake_case								=			None
										if self.use_labels:
																snake_case								=			ids_tensor([self.batch_size]		,    self.type_sequence_label_size    )

										snake_case								=			self.get_config()

										return config, pixel_values, labels

				def 		snake_case (				self    ):






										"""simple docstring"""

										return ViTMSNConfig(
										    image_size=self.image_size		,    patch_size=self.patch_size		,    num_channels=self.num_channels		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			ViTMSNModel(config=lowerCAmelCase    )
										model.to(lowerCAmelCase    )
										model.eval()
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.type_sequence_label_size
										snake_case								=			ViTMSNForImageClassification(lowerCAmelCase    )
										model.to(lowerCAmelCase    )
										model.eval()
										snake_case								=			model(lowerCAmelCase		,    labels=lowerCAmelCase    )
										print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'    )
										print('Labels: {labels}'    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)    )

										# test greyscale images
										snake_case								=			1
										snake_case								=			ViTMSNForImageClassification(lowerCAmelCase    )
										model.to(lowerCAmelCase    )
										model.eval()

										snake_case								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.prepare_config_and_inputs()
										snake_case					,snake_case					,snake_case								=			config_and_inputs
										snake_case								=			{'pixel_values': pixel_values}
										return config, inputs_dict





@require_torch
class   lowerCAmelCase_    (			lowerCAmelCase     ,    lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[Any]			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				_lowerCAmelCase     :    Any			  =						(
				    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase     :    Union[str, Any]			  =						False
				_lowerCAmelCase     :    str			  =						False
				_lowerCAmelCase     :    Dict			  =						False
				_lowerCAmelCase     :    Optional[Any]			  =						False

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ViTMSNModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    has_text_modality=lowerCAmelCase		,    hidden_size=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@unittest.skip(reason='ViTMSN does not use inputs_embeds'    )
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case								=			model_class(lowerCAmelCase    )
																self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)    )
																snake_case								=			model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase		,    nn.Linear    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case								=			model_class(lowerCAmelCase    )
																snake_case								=			inspect.signature(model.forward    )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case								=			[*signature.parameters.keys()]

																snake_case								=			['pixel_values']
																self.assertListEqual(arg_names[:1]		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case								=			ViTMSNModel.from_pretrained(lowerCAmelCase    )
																self.assertIsNotNone(lowerCAmelCase    )





def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
						return image



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



				"""simple docstring"""

				@cached_property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'    ) if is_vision_available() else None

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										torch.manual_seed(2    )
										snake_case								=			ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'    ).to(lowerCAmelCase    )

										snake_case								=			self.default_image_processor
										snake_case								=			prepare_img()
										snake_case								=			image_processor(images=lowerCAmelCase		,    return_tensors='pt'    ).to(lowerCAmelCase    )

										# forward pass
										with torch.no_grad():
																snake_case								=			model(**lowerCAmelCase    )

										# verify the logits
										snake_case								=			torch.Size((1, 10_00)    )
										self.assertEqual(outputs.logits.shape		,    lowerCAmelCase    )

										snake_case								=			torch.tensor([-0.08_03, -0.44_54, -0.23_75]    ).to(lowerCAmelCase    )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    lowerCAmelCase		,    atol=1E-4    )    )



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      						=  {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			(
						    list(range(ord('!'					) ,			ord('~'					) + 1					)					) + list(range(ord('¡'					) ,			ord('¬'					) + 1					)					) + list(range(ord('®'					) ,			ord('ÿ'					) + 1					)					)
						)
						snake_case								=			bs[:]
						snake_case								=			0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(_UpperCamelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						snake_case								=			[chr(_UpperCamelCase					) for n in cs]
						return dict(zip(_UpperCamelCase ,			_UpperCamelCase					)					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				List[str]:





						"""simple docstring"""






						snake_case								=			set()
						snake_case								=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												snake_case								=			char
						return pairs



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    str			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    int			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    str			  =						["""input_ids""", """attention_mask"""]

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase="replace"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="<unk>"		,    lowerCAmelCase="<pad>"		,    lowerCAmelCase="<mask>"		,    lowerCAmelCase=False		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else bos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else eos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else sep_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else cls_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else unk_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else mask_token

										super().__init__(
										    errors=lowerCAmelCase		,    bos_token=lowerCAmelCase		,    eos_token=lowerCAmelCase		,    unk_token=lowerCAmelCase		,    sep_token=lowerCAmelCase		,    cls_token=lowerCAmelCase		,    pad_token=lowerCAmelCase		,    mask_token=lowerCAmelCase		,    add_prefix_space=lowerCAmelCase		,    **lowerCAmelCase		,    )

										with open(lowerCAmelCase		,    encoding='utf-8'    ) as vocab_handle:
																snake_case								=			json.load(lowerCAmelCase    )
										snake_case								=			{v: k for k, v in self.encoder.items()}
										snake_case								=			errors  # how to handle errors in decoding
										snake_case								=			bytes_to_unicode()
										snake_case								=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase		,    encoding='utf-8'    ) as merges_handle:
																snake_case								=			merges_handle.read().split('\n'    )[1:-1]
										snake_case								=			[tuple(merge.split()    ) for merge in bpe_merges]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			{}
										snake_case								=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case								=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return len(self.encoder    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										return dict(self.encoder		,    **self.added_tokens_encoder    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if token in self.cache:
																return self.cache[token]
										snake_case								=			tuple(lowerCAmelCase    )
										snake_case								=			get_pairs(lowerCAmelCase    )

										if not pairs:
																return token

										while True:
																snake_case								=			min(lowerCAmelCase		,    key=lambda lowerCAmelCase    : self.bpe_ranks.get(lowerCAmelCase		,    float('inf'    )    )    )
																if bigram not in self.bpe_ranks:
																						break
																snake_case					,snake_case								=			bigram
																snake_case								=			[]
																snake_case								=			0
																while i < len(lowerCAmelCase    ):
																						try:
																												snake_case								=			word.index(lowerCAmelCase		,    lowerCAmelCase    )
																						except ValueError:
																												new_word.extend(word[i:]    )
																												break
																						else:
																												new_word.extend(word[i:j]    )
																												snake_case								=			j

																						if word[i] == first and i < len(lowerCAmelCase    ) - 1 and word[i + 1] == second:
																												new_word.append(first + second    )
																												i += 2
																						else:
																												new_word.append(word[i]    )
																												i += 1
																snake_case								=			tuple(lowerCAmelCase    )
																snake_case								=			new_word
																if len(lowerCAmelCase    ) == 1:
																						break
																else:
																						snake_case								=			get_pairs(lowerCAmelCase    )
										snake_case								=			' '.join(lowerCAmelCase    )
										snake_case								=			word
										return word

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[]
										for token in re.findall(self.pat		,    lowerCAmelCase    ):
																snake_case								=			''.join(
																    self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase    ).split(' '    )    )
										return bpe_tokens

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.encoder.get(lowerCAmelCase		,    self.encoder.get(self.unk_token    )    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			''.join(lowerCAmelCase    )
										snake_case								=			bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'		,    errors=self.errors    )
										return text

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										if not os.path.isdir(lowerCAmelCase    ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
																return
										snake_case								=			os.path.join(
										    lowerCAmelCase		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
										snake_case								=			os.path.join(
										    lowerCAmelCase		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as f:
																f.write(json.dumps(self.encoder		,    indent=2		,    sort_keys=lowerCAmelCase		,    ensure_ascii=lowerCAmelCase    ) + '\n'    )

										snake_case								=			0
										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as writer:
																writer.write('#version: 0.2\n'    )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,    key=lambda lowerCAmelCase    : kv[1]    ):
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ' Please check that the tokenizer is not corrupted!'    )
																												snake_case								=			token_index
																						writer.write(' '.join(lowerCAmelCase    ) + '\n'    )
																						index += 1

										return vocab_file, merge_file

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case								=			[self.cls_token_id]
										snake_case								=			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase		,    token_ids_a=lowerCAmelCase		,    already_has_special_tokens=lowerCAmelCase    )

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase    )) + [1]
										return [1] + ([0] * len(lowerCAmelCase    )) + [1, 1] + ([0] * len(lowerCAmelCase    )) + [1]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep    ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=False		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.pop('add_prefix_space'		,    self.add_prefix_space    )
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase    ) > 0 and not text[0].isspace()):
																snake_case								=			' ' + text
										return (text, kwargs)


