


'''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   snake_case					:





  """simple docstring"""




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







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						patch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						is_training
   lowerCamelCase_  				=						use_labels
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						type_sequence_label_size
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						scope
   lowerCamelCase_  				=						encoder_stride

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

   lowerCamelCase_  				=						self.get_config()

   return config, pixel_values, labels






  def 						snake_case	(					self  ):







   """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=lowerCAmelCase__    ,						initializer_range=self.initializer_range    ,						encoder_stride=self.encoder_stride    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						ViTModel(config=lowerCAmelCase__  )
   model.to(lowerCAmelCase__  )
   model.eval()
   lowerCamelCase_  				=						model(lowerCAmelCase__  )
   self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						ViTForMaskedImageModeling(config=lowerCAmelCase__  )
   model.to(lowerCAmelCase__  )
   model.eval()
   lowerCamelCase_  				=						model(lowerCAmelCase__  )
   self.parent.assertEqual(
       result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						ViTForMaskedImageModeling(lowerCAmelCase__  )
   model.to(lowerCAmelCase__  )
   model.eval()

   lowerCamelCase_  				=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
   lowerCamelCase_  				=						model(lowerCAmelCase__  )
   self.parent.assertEqual(result.reconstruction.shape    ,						(self.batch_size, 1, self.image_size, self.image_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.type_sequence_label_size
   lowerCamelCase_  				=						ViTForImageClassification(lowerCAmelCase__  )
   model.to(lowerCAmelCase__  )
   model.eval()
   lowerCamelCase_  				=						model(lowerCAmelCase__    ,						labels=lowerCAmelCase__  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)  )

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						ViTForImageClassification(lowerCAmelCase__  )
   model.to(lowerCAmelCase__  )
   model.eval()

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







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_config_and_inputs()
   (
       lowerCamelCase_ 
   )  				=						config_and_inputs
   lowerCamelCase_  				=						{"pixel_values": pixel_values}
   return config, inputs_dict





@require_torch
class   snake_case					(					a__    ,		a__    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase  					=						(
      (
          ViTModel,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase  					=						(
      {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
      if is_torch_available()
      else {}
  )
  _lowerCamelCase  					=						True

  _lowerCamelCase  					=						False
  _lowerCamelCase  					=						False
  _lowerCamelCase  					=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						ViTModelTester(self  )
   lowerCamelCase_  				=						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="ViT does not use inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_  				=						["pixel_values"]
    self.assertListEqual(arg_names[:1]    ,						lowerCAmelCase__  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCAmelCase__  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						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_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_  				=						ViTModel.from_pretrained(lowerCAmelCase__  )
    self.assertIsNotNone(lowerCAmelCase__  )





def __snake_case      (   ):
 lowerCamelCase_  				=						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 						snake_case	(					self  ):







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






  @slow
  def 						snake_case	(					self  ):







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

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=lowerCAmelCase__    ,						return_tensors="pt"  ).to(lowerCAmelCase__  )

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

   # verify the logits
   lowerCamelCase_  				=						torch.Size((1, 1000)  )
   self.assertEqual(outputs.logits.shape    ,						lowerCAmelCase__  )

   lowerCamelCase_  				=						torch.tensor([-0.2_744, 0.8_215, -0.0_836]  ).to(lowerCAmelCase__  )

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






  @slow
  def 						snake_case	(					self  ):







   """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_  				=						ViTModel.from_pretrained("facebook/dino-vits8"  ).to(lowerCAmelCase__  )

   lowerCamelCase_  				=						ViTImageProcessor.from_pretrained("facebook/dino-vits8"    ,						size=480  )
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=lowerCAmelCase__    ,						return_tensors="pt"  )
   lowerCamelCase_  				=						inputs.pixel_values.to(lowerCAmelCase__  )

   # forward pass
   with torch.no_grad():
    lowerCamelCase_  				=						model(lowerCAmelCase__    ,						interpolate_pos_encoding=lowerCAmelCase__  )

   # verify the logits
   lowerCamelCase_  				=						torch.Size((1, 3601, 384)  )
   self.assertEqual(outputs.last_hidden_state.shape    ,						lowerCAmelCase__  )

   lowerCamelCase_  				=						torch.tensor(
       [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]  ).to(lowerCAmelCase__  )

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







  @slow
  @require_accelerate
  @require_torch_gpu
  def 						snake_case	(					self  ):







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

   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=lowerCAmelCase__    ,						return_tensors="pt"  )
   lowerCamelCase_  				=						inputs.pixel_values.to(lowerCAmelCase__  )

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







'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
     from ...feature_extraction_utils import FeatureExtractionMixin
     from ...tokenization_utils_base import PreTrainedTokenizerBase
     from ...utils import TensorType

a_						:       Optional[int]      			=				logging.get_logger(__name__)

a_						:       Optional[int]      			=				{
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
a_						:       List[str]      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
a_						:       Tuple      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"whisper"
  _lowerCamelCase						=						["past_key_values"]
  _lowerCamelCase						=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






  def __init__(					self    ,						UpperCamelCase=5_1865    ,						UpperCamelCase=80    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=1536    ,						UpperCamelCase=1536    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=5_0257    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=256    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.02    ,						UpperCamelCase=False    ,						UpperCamelCase=1500    ,						UpperCamelCase=448    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=None    ,						UpperCamelCase=[220, 5_0256]    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=False    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=7    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						num_mel_bins
   lowerCamelCase_  				=						d_model
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						encoder_attention_heads
   lowerCamelCase_  				=						decoder_layers
   lowerCamelCase_  				=						decoder_attention_heads
   lowerCamelCase_  				=						decoder_ffn_dim
   lowerCamelCase_  				=						encoder_ffn_dim
   lowerCamelCase_  				=						dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						activation_function
   lowerCamelCase_  				=						init_std
   lowerCamelCase_  				=						encoder_layerdrop
   lowerCamelCase_  				=						decoder_layerdrop
   lowerCamelCase_  				=						use_cache
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						scale_embedding  # scale factor will be sqrt(d_model) if True
   lowerCamelCase_  				=						max_source_positions
   lowerCamelCase_  				=						max_target_positions

   # Audio Classification-specific parameters. Feel free to ignore for other classes.
   lowerCamelCase_  				=						classifier_proj_size
   lowerCamelCase_  				=						use_weighted_layer_sum

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   lowerCamelCase_  				=						median_filter_width

   super().__init__(
       pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						is_encoder_decoder=UpperCamelCase    ,						decoder_start_token_id=UpperCamelCase    ,						suppress_tokens=UpperCamelCase    ,						begin_suppress_tokens=UpperCamelCase    ,						**UpperCamelCase    ,						)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict(
       [
           ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
       ]  )
   if self.use_past:
    lowerCamelCase_  				=						{0: "batch"}
   else:
    lowerCamelCase_  				=						{0: "batch", 1: "decoder_sequence"}

   if self.use_past:
    self.fill_with_past_key_values_(UpperCamelCase    ,						direction="inputs"  )

   return common_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = -1    ,						UpperCamelCase = -1    ,						UpperCamelCase = False    ,						UpperCamelCase = None    ,						UpperCamelCase = 2_2050    ,						UpperCamelCase = 5.0    ,						UpperCamelCase = 220    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict()
   lowerCamelCase_  				=						OnnxConfig.generate_dummy_inputs(
       self    ,						preprocessor=preprocessor.feature_extractor    ,						batch_size=UpperCamelCase    ,						framework=UpperCamelCase    ,						sampling_rate=UpperCamelCase    ,						time_duration=UpperCamelCase    ,						frequency=UpperCamelCase    ,						)
   lowerCamelCase_  				=						encoder_inputs["input_features"].shape[2]
   lowerCamelCase_  				=						encoder_sequence_length // 2 if self.use_past else seq_length

   lowerCamelCase_  				=						super().generate_dummy_inputs(
       preprocessor.tokenizer    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						encoder_inputs.pop("input_features"  )
   lowerCamelCase_  				=						decoder_inputs.pop("decoder_input_ids"  )

   if "past_key_values" in decoder_inputs:
    lowerCamelCase_  				=						decoder_inputs.pop("past_key_values"  )

   return dummy_inputs







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 1e-3



