




"""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 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=1_3       ,					lowerCamelCase_=3_0       ,					lowerCamelCase_=2       ,					lowerCamelCase_=3       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=5       ,					lowerCamelCase_=4       ,					lowerCamelCase_=3_7       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=1_0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=None       ,					)				->   Any:
       UpperCamelCase              =	parent
       UpperCamelCase              =	batch_size
       UpperCamelCase              =	image_size
       UpperCamelCase              =	patch_size
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	is_training
       UpperCamelCase              =	use_labels
       UpperCamelCase              =	hidden_size
       UpperCamelCase              =	num_hidden_layers
       UpperCamelCase              =	num_attention_heads
       UpperCamelCase              =	intermediate_size
       UpperCamelCase              =	hidden_act
       UpperCamelCase              =	hidden_dropout_prob
       UpperCamelCase              =	attention_probs_dropout_prob
       UpperCamelCase              =	type_sequence_label_size
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	scope

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







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

       UpperCamelCase              =	self.get_config()

       return config, pixel_values, labels







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[Any]:
       UpperCamelCase              =	ViTMSNModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Any:
       UpperCamelCase              =	self.type_sequence_label_size
       UpperCamelCase              =	ViTMSNForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	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
       UpperCamelCase              =	1
       UpperCamelCase              =	ViTMSNForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

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







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase              =	config_and_inputs
       UpperCamelCase              =	{'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_torch
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   A_				      =       (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   A_				      =       False
   A_				      =       False
   A_				      =       False
   A_				      =       False







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	ViTMSNModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					has_text_modality=lowerCamelCase_       ,					hidden_size=3_7)







   def   UpperCAmelCase__			(		self)				->   str:
       self.config_tester.run_common_tests()







   @unittest.skip(reason='''ViTMSN does not use inputs_embeds''')
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       pass







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase       ,	UpperCamelCase              =	self.model_tester.prepare_config_and_inputs_for_common()

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







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase       ,	UpperCamelCase              =	self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase              =	['''pixel_values''']
           self.assertListEqual(arg_names[:1]       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)







   @slow
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	ViTMSNModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)





def      __snake_case (				):


    """simple docstring"""







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


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







   """simple docstring"""




   @cached_property
   def   UpperCAmelCase__			(		self)				->   List[str]:
       return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''') if is_vision_available() else None







   @slow
   def   UpperCAmelCase__			(		self)				->   str:
       torch.manual_seed(2)
       UpperCamelCase              =	ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''').to(lowerCamelCase_)

       UpperCamelCase              =	self.default_image_processor
       UpperCamelCase              =	prepare_img()
       UpperCamelCase              =	image_processor(images=lowerCamelCase_       ,					return_tensors='''pt''').to(lowerCamelCase_)

       # forward pass
       with torch.no_grad():
           UpperCamelCase              =	model(**lowerCamelCase_)

       # verify the logits
       UpperCamelCase              =	torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape       ,					lowerCamelCase_)

       UpperCamelCase              =	torch.tensor([-0.0803, -0.4454, -0.2375]).to(lowerCamelCase_)

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







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:     Any   					=					logging.get_logger(__name__)


__a:     Dict   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     int   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     str   					=					{'''facebook/blenderbot_small-90M''': 512}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    set()
       _UpperCAmelCase							=    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCAmelCase							=    char

       _UpperCAmelCase							=    set(__snake_case )
       return pairs





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  ['''input_ids''', '''attention_mask''']


  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]="__start__"       ,				lowerCamelCase		:			List[Any]="__end__"       ,				lowerCamelCase		:			Any="__unk__"       ,				lowerCamelCase		:			Optional[Any]="__null__"       ,				**lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(unk_token=lowerCamelCase       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				pad_token=lowerCamelCase       ,				**lowerCamelCase							)

         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as vocab_handle:
                _UpperCAmelCase							=    json.load(lowerCamelCase							)
         _UpperCAmelCase							=    {v: k for k, v in self.encoder.items()}
         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as merges_handle:
                _UpperCAmelCase							=    merges_handle.read().split("""\n"""							)[1:-1]
         _UpperCAmelCase							=    [tuple(merge.split()							) for merge in merges]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    {}


  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return len(self.encoder							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


         """simple docstring"""
         return dict(self.encoder       ,				**self.added_tokens_encoder							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""
         if token in self.cache:
                return self.cache[token]
         _UpperCAmelCase							=    re.sub("""([.,!?()])"""       ,				r""" \1"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub("""(')"""       ,				r""" \1 """       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub(r"""\s{2,}"""       ,				""" """       ,				lowerCamelCase							)
         if "\n" in token:
                _UpperCAmelCase							=    token.replace("""\n"""       ,				""" __newln__"""							)

         _UpperCAmelCase							=    token.split(""" """							)
         _UpperCAmelCase							=    []
         for token in tokens:
                if not len(lowerCamelCase							):
                       continue

                _UpperCAmelCase							=    token.lower()
                _UpperCAmelCase							=    tuple(lowerCamelCase							)
                _UpperCAmelCase							=    tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
                _UpperCAmelCase							=    get_pairs(lowerCamelCase							)

                if not pairs:
                       words.append(lowerCamelCase							)
                       continue

                while True:
                       _UpperCAmelCase							=    min(lowerCamelCase       ,				key=lambda lowerCamelCase							: self.bpe_ranks.get(lowerCamelCase       ,				float("""inf"""							)							)							)
                       if bigram not in self.bpe_ranks:
                              break
                       _UpperCAmelCase	,			_UpperCAmelCase							=    bigram
                       _UpperCAmelCase							=    []
                       _UpperCAmelCase							=    0

                       while i < len(lowerCamelCase							):
                              try:
                                     _UpperCAmelCase							=    word.index(lowerCamelCase       ,				lowerCamelCase							)
                                     new_word.extend(word[i:j]							)
                                     _UpperCAmelCase							=    j
                              except ValueError:
                                     new_word.extend(word[i:]							)
                                     break

                              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
                       _UpperCAmelCase							=    tuple(lowerCamelCase							)
                       _UpperCAmelCase							=    new_word
                       if len(lowerCamelCase							) == 1:
                              break
                       else:
                              _UpperCAmelCase							=    get_pairs(lowerCamelCase							)
                _UpperCAmelCase							=    """@@ """.join(lowerCamelCase							)
                _UpperCAmelCase							=    word[:-4]

                _UpperCAmelCase							=    word
                words.append(lowerCamelCase							)
         return " ".join(lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    []

         _UpperCAmelCase							=    re.findall(r"""\S+\n?"""       ,				lowerCamelCase							)

         for token in words:
                split_tokens.extend(list(self.bpe(lowerCamelCase							).split(""" """							)							)							)
         return split_tokens


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    token.lower()
         return self.encoder.get(lowerCamelCase       ,				self.encoder.get(self.unk_token							)							)


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


         """simple docstring"""
         return self.decoder.get(lowerCamelCase       ,				self.unk_token							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    """ """.join(lowerCamelCase							).replace("""@@ """       ,				""""""							).strip()
         return out_string





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


         """simple docstring"""
         if not os.path.isdir(lowerCamelCase							):
                logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                return
         _UpperCAmelCase							=    os.path.join(
             lowerCamelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    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"""							)

         _UpperCAmelCase							=    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!"""							)
                              _UpperCAmelCase							=    token_index
                       writer.write(""" """.join(lowerCamelCase							) + """\n"""							)
                       index += 1

         return vocab_file, merge_file