




"""simple docstring"""


import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor


class _lowerCamelCase    :






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		str=13   ,     UpperCamelCase       :		Union[str, Any]=30   ,     UpperCamelCase       :		Optional[Any]=2   ,     UpperCamelCase       :		Union[str, Any]=3   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		Optional[Any]=True   ,     UpperCamelCase       :		Optional[Any]=32   ,     UpperCamelCase       :		int=5   ,     UpperCamelCase       :		Optional[Any]=4   ,     UpperCamelCase       :		int=37   ,     UpperCamelCase       :		Optional[Any]="gelu"   ,     UpperCamelCase       :		Tuple=0.1   ,     UpperCamelCase       :		Any=0.1   ,     UpperCamelCase       :		str=10   ,     UpperCamelCase       :		str=0.02   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Optional[int]=2   ,     )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	parent
         lowerCAmelCase__      :			Optional[int]   =	batch_size
         lowerCAmelCase__      :			int   =	image_size
         lowerCAmelCase__      :			Any   =	patch_size
         lowerCAmelCase__      :			Union[str, Any]   =	num_channels
         lowerCAmelCase__      :			str   =	is_training
         lowerCAmelCase__      :			Any   =	use_labels
         lowerCAmelCase__      :			Any   =	hidden_size
         lowerCAmelCase__      :			Union[str, Any]   =	num_hidden_layers
         lowerCAmelCase__      :			List[Any]   =	num_attention_heads
         lowerCAmelCase__      :			Optional[int]   =	intermediate_size
         lowerCAmelCase__      :			Optional[Any]   =	hidden_act
         lowerCAmelCase__      :			Any   =	hidden_dropout_prob
         lowerCAmelCase__      :			Union[str, Any]   =	attention_probs_dropout_prob
         lowerCAmelCase__      :			Optional[int]   =	type_sequence_label_size
         lowerCAmelCase__      :			Union[str, Any]   =	initializer_range
         lowerCAmelCase__      :			Optional[int]   =	scope
         lowerCAmelCase__      :			str   =	encoder_stride

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






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

         lowerCAmelCase__      :			Optional[int]   =	None
         if self.use_labels:
               lowerCAmelCase__      :			str   =	ids_tensor([self.batch_size]   ,     self.type_sequence_label_size   )

         lowerCAmelCase__      :			Dict   =	self.get_config()

         return config, pixel_values, labels






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         return ViTConfig(
             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   ,     is_decoder=UpperCamelCase   ,     initializer_range=self.initializer_range   ,     encoder_stride=self.encoder_stride   ,     )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Any   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	ViTModel(config=UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()
         lowerCAmelCase__      :			Optional[Any]   =	model(UpperCamelCase   )
         self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	ViTForMaskedImageModeling(config=UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()
         lowerCAmelCase__      :			Optional[Any]   =	model(UpperCamelCase   )
         self.parent.assertEqual(
             result.reconstruction.shape   ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

         # test greyscale images
         lowerCAmelCase__      :			Tuple   =	1
         lowerCAmelCase__      :			Optional[Any]   =	ViTForMaskedImageModeling(UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()

         lowerCAmelCase__      :			int   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
         lowerCAmelCase__      :			List[Any]   =	model(UpperCamelCase   )
         self.parent.assertEqual(result.reconstruction.shape   ,     (self.batch_size, 1, self.image_size, self.image_size)   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.type_sequence_label_size
         lowerCAmelCase__      :			Tuple   =	ViTForImageClassification(UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()
         lowerCAmelCase__      :			Any   =	model(UpperCamelCase   ,     labels=UpperCamelCase   )
         self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)   )

         # test greyscale images
         lowerCAmelCase__      :			Any   =	1
         lowerCAmelCase__      :			Union[str, Any]   =	ViTForImageClassification(UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.eval()

         lowerCAmelCase__      :			Union[str, Any]   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
         lowerCAmelCase__      :			Dict   =	model(UpperCamelCase   )
         self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			str   =	config_and_inputs
         lowerCAmelCase__      :			Dict   =	{"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_torch
class _lowerCamelCase    (					a_       ,	a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Optional[int]     				=     (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _lowerCamelCase    :Any     				=     (
       {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   _lowerCamelCase    :Tuple     				=     True

   _lowerCamelCase    :Union[str, Any]     				=     False
   _lowerCamelCase    :Optional[int]     				=     False
   _lowerCamelCase    :Union[str, Any]     				=     False






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

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	ViTModelTester(self   )
         lowerCAmelCase__      :			Dict   =	ConfigTester(self   ,     config_class=UpperCamelCase   ,     has_text_modality=UpperCamelCase   ,     hidden_size=37   )






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

         """simple docstring"""
         self.config_tester.run_common_tests()






   @unittest.skip(reason="""ViT does not use inputs_embeds"""   )
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Dict:

         """simple docstring"""
         pass






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

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowerCAmelCase__      :			Optional[Any]   =	model_class(UpperCamelCase   )
               self.assertIsInstance(model.get_input_embeddings()   ,     (nn.Module)   )
               lowerCAmelCase__      :			Dict   =	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(UpperCamelCase   ,     nn.Linear   )   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.model_tester.prepare_config_and_inputs_for_common()

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

               lowerCAmelCase__      :			List[Any]   =	["""pixel_values"""]
               self.assertListEqual(arg_names[:1]   ,     UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase   )






   @slow
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Dict:

         """simple docstring"""
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__      :			str   =	ViTModel.from_pretrained(UpperCamelCase   )
               self.assertIsNotNone(UpperCamelCase   )






def 	lowercase_				(			)						->    Optional[Any]:
      lowerCAmelCase__      :			str   =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
      return image


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






   @cached_property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict:

         """simple docstring"""
         return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""   ) if is_vision_available() else None






   @slow
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""   ).to(UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	self.default_image_processor
         lowerCAmelCase__      :			str   =	prepare_img()
         lowerCAmelCase__      :			Tuple   =	image_processor(images=UpperCamelCase   ,     return_tensors="""pt"""   ).to(UpperCamelCase   )

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

         # verify the logits
         lowerCAmelCase__      :			Union[str, Any]   =	torch.Size((1, 10_00)   )
         self.assertEqual(outputs.logits.shape   ,     UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	torch.tensor([-0.2744, 0.8215, -0.0836]   ).to(UpperCamelCase   )

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






   @slow
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[str]:

         """simple docstring"""
         # ViT models have an `interpolate_pos_encoding` argument in their forward method,
         # allowing to interpolate the pre-trained position embeddings in order to use
         # the model on higher resolutions. The DINO model by Facebook AI leverages this
         # to visualize self-attention on higher resolution images.
         lowerCAmelCase__      :			Dict   =	ViTModel.from_pretrained("""facebook/dino-vits8"""   ).to(UpperCamelCase   )

         lowerCAmelCase__      :			Optional[Any]   =	ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""   ,     size=4_80   )
         lowerCAmelCase__      :			Union[str, Any]   =	prepare_img()
         lowerCAmelCase__      :			Tuple   =	image_processor(images=UpperCamelCase   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			Any   =	inputs.pixel_values.to(UpperCamelCase   )

         # forward pass
         with torch.no_grad():
               lowerCAmelCase__      :			List[Any]   =	model(UpperCamelCase   ,     interpolate_pos_encoding=UpperCamelCase   )

         # verify the logits
         lowerCAmelCase__      :			Tuple   =	torch.Size((1, 36_01, 3_84)   )
         self.assertEqual(outputs.last_hidden_state.shape   ,     UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	torch.tensor(
             [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]   ).to(UpperCamelCase   )

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






   @slow
   @require_accelerate
   @require_torch_gpu
   def 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	ViTModel.from_pretrained("""facebook/dino-vits8"""   ,     torch_dtype=torch.floataa   ,     device_map="""auto"""   )
         lowerCAmelCase__      :			Dict   =	self.default_image_processor

         lowerCAmelCase__      :			List[str]   =	prepare_img()
         lowerCAmelCase__      :			Dict   =	image_processor(images=UpperCamelCase   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			Optional[Any]   =	inputs.pixel_values.to(UpperCamelCase   )

         # forward pass to make sure inference works in fp16
         with torch.no_grad():
               lowerCAmelCase__      :			str   =	model(UpperCamelCase   )











"""simple docstring"""


import json
import os
from typing import Optional, Tuple

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


_A													=      logging.get_logger(__name__)

_A													=      {"""vocab_file""": """vocab.json"""}

_A													=      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_A													=      {"""mgp-str""": 2_7}


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Optional[int]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Union[str, Any]="[GO]"   ,     UpperCamelCase       :		Any="[GO]"   ,     UpperCamelCase       :		Tuple="[s]"   ,     UpperCamelCase       :		List[Any]="[GO]"   ,     **UpperCamelCase       :		Dict   )			->						Union[str, Any]:

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

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			Optional[Any]   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{v: k for k, v in self.vocab.items()}






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

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






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Any:

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






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	[]
         for s in text:
               char_tokens.extend(UpperCamelCase   )
         return char_tokens






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		str   )			->						Tuple:

         """simple docstring"""
         return self.vocab.get(UpperCamelCase   ,     self.vocab.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		int   )			->						Any:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase   )   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

         return (vocab_file,)





