


"""simple docstring"""







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
     from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=13		,     snake_case__=64		,     snake_case__=2		,     snake_case__=3		,     snake_case__=True		,     snake_case__=True		,     snake_case__=32		,     snake_case__=5		,     snake_case__=4		,     snake_case__=37		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=10		,     snake_case__=0.02		,     snake_case__=[1, 16, 4, 4]		,     snake_case__=None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							parent
         lowerCAmelCase     :     Optional[int]								=							batch_size
         lowerCAmelCase     :     Any								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     int								=							is_training
         lowerCAmelCase     :     List[str]								=							use_labels
         lowerCAmelCase     :     List[str]								=							hidden_size
         lowerCAmelCase     :     Tuple								=							num_hidden_layers
         lowerCAmelCase     :     Optional[Any]								=							num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase     :     Any								=							attention_probs_dropout_prob
         lowerCAmelCase     :     Any								=							type_sequence_label_size
         lowerCAmelCase     :     Tuple								=							initializer_range
         lowerCAmelCase     :     Tuple								=							scope
         lowerCAmelCase     :     int								=							backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         lowerCAmelCase     :     Any								=							(self.image_size // 32) ** 2
         lowerCAmelCase     :     List[Any]								=							num_patches + 1

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

         lowerCAmelCase     :     List[str]								=							self.get_config()

         return config, pixel_values, labels

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage1', 'stage2', 'stage3'],
             'embedding_dynamic_padding': True,
             'hidden_sizes': [4, 8, 16, 32],
             'num_groups': 2,
         }

         return ViTHybridConfig(
             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=snake_case__		,     initializer_range=self.initializer_range		,     backbone_featmap_shape=self.backbone_featmap_shape		,     backbone_config=snake_case__		,     )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							ViTHybridModel(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     Union[str, Any]								=							model(snake_case__						)
         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.type_sequence_label_size
         lowerCAmelCase     :     Union[str, Any]								=							ViTHybridForImageClassification(snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     Tuple								=							model(snake_case__		,     labels=snake_case__						)
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.prepare_config_and_inputs()
         lowerCAmelCase     :     Union[str, Any]								=							config_and_inputs
         lowerCAmelCase     :     str								=							{'pixel_values': pixel_values}
         return config, inputs_dict


@require_torch
class       SCREAMING_SNAKE_CASE__				(		__UpperCAmelCase   ,    __UpperCAmelCase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
       a  :				List[str]   =(
           {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
           if is_torch_available()
           else {}
       )
       a  :				Any   =False
       a  :				Optional[int]   =False
       a  :				Optional[Any]   =False

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							ViTHybridModelTester(self						)
         lowerCAmelCase     :     Any								=							ConfigTester(self		,     config_class=snake_case__		,     has_text_modality=snake_case__		,     hidden_size=37						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.config_tester.run_common_tests()

       @unittest.skip(reason="ViT does not use inputs_embeds"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.model_tester.prepare_config_and_inputs_for_common()

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							self.model_tester.prepare_config_and_inputs_for_common()

         lowerCAmelCase     :     Tuple								=							_config_zero_init(snake_case__						)
         for model_class in self.all_model_classes:
           lowerCAmelCase     :     List[Any]								=							model_class(config=snake_case__						)
           # Skip the check for the backbone
           for name, module in model.named_modules():
             if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
               lowerCAmelCase     :     Dict								=							[f"""{name}.{key}""" for key in module.state_dict().keys()]
               break

           for name, param in model.named_parameters():
             if param.requires_grad:
               if name in backbone_params:
                 continue
               self.assertIn(
                   ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     :     Optional[Any]								=							ViTHybridModel.from_pretrained(snake_case__						)
           self.assertIsNotNone(snake_case__						)

def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
  return image





@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @cached_property
       def        lowercase__							(						self						):






         """simple docstring"""





         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
             if is_vision_available()
             else None
         )

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
             snake_case__						)

         lowerCAmelCase     :     List[str]								=							self.default_image_processor
         lowerCAmelCase     :     List[Any]								=							prepare_img()
         lowerCAmelCase     :     Any								=							image_processor(images=snake_case__		,     return_tensors="pt"						).to(snake_case__						)

         # forward pass
         with torch.no_grad():
           lowerCAmelCase     :     Optional[Any]								=							model(**snake_case__						)

         # verify the logits
         lowerCAmelCase     :     Optional[Any]								=							torch.Size((1, 1_000)						)
         self.assertEqual(outputs.logits.shape		,     snake_case__						)

         lowerCAmelCase     :     Any								=							torch.tensor([-1.9090, -0.4993, -0.2389]						).to(snake_case__						)

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







       @slow
       @require_accelerate
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"						)
         lowerCAmelCase     :     Union[str, Any]								=							ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"		,     device_map="auto"						)

         lowerCAmelCase     :     Any								=							prepare_img()

         lowerCAmelCase     :     str								=							image_processor(images=snake_case__		,     return_tensors="pt"						)
         lowerCAmelCase     :     List[Any]								=							model(**snake_case__						)
         lowerCAmelCase     :     int								=							outputs.logits
         # model predicts one of the 1000 ImageNet classes
         lowerCAmelCase     :     List[str]								=							logits.argmax(-1						).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]		,     "tabby, tabby cat"						)












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

lowercase      :       List[str]									=  logging.get_logger(__name__)

lowercase      :       Any									=  {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase      :       List[str]									=  [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
lowercase      :       List[Any]									=  [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				int         =  '''whisper'''
   __A   :				List[Any]         =  ['''past_key_values''']
   __A   :				Optional[int]         =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

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







          '''simple docstring'''

          a__						:	int =				vocab_size
          a__						:	int =				num_mel_bins
          a__						:	Optional[int] =				d_model
          a__						:	List[str] =				encoder_layers
          a__						:	Dict =				encoder_attention_heads
          a__						:	List[str] =				decoder_layers
          a__						:	Tuple =				decoder_attention_heads
          a__						:	List[str] =				decoder_ffn_dim
          a__						:	Optional[Any] =				encoder_ffn_dim
          a__						:	Tuple =				dropout
          a__						:	Optional[int] =				attention_dropout
          a__						:	Any =				activation_dropout
          a__						:	Any =				activation_function
          a__						:	List[Any] =				init_std
          a__						:	Optional[int] =				encoder_layerdrop
          a__						:	Union[str, Any] =				decoder_layerdrop
          a__						:	Tuple =				use_cache
          a__						:	List[str] =				encoder_layers
          a__						:	Dict =				scale_embedding  # scale factor will be sqrt(d_model) if True
          a__						:	Dict =				max_source_positions
          a__						:	Dict =				max_target_positions

          # Audio Classification-specific parameters. Feel free to ignore for other classes.
          a__						:	Optional[int] =				classifier_proj_size
          a__						:	List[Any] =				use_weighted_layer_sum

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          a__						:	List[Any] =				apply_spec_augment
          a__						:	int =				mask_time_prob
          a__						:	int =				mask_time_length
          a__						:	List[Any] =				mask_time_min_masks
          a__						:	str =				mask_feature_prob
          a__						:	Optional[int] =				mask_feature_length
          a__						:	Union[str, Any] =				mask_feature_min_masks

          a__						:	Tuple =				median_filter_width

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





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   @property
   def 							__lowercase		(					self)			->						Mapping[str, Mapping[int, str]]:







          '''simple docstring'''

          a__						:	List[str] =				OrderedDict(
              [
                  ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
              ])
          if self.use_past:
                 a__						:	Optional[Any] =				{0: 'batch'}
          else:
                 a__						:	Optional[Any] =				{0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
                 self.fill_with_past_key_values_(lowercase					,  direction='inputs')

          return common_inputs

   def 							__lowercase		(					self					,  lowercase					,  lowercase = -1					,  lowercase = -1					,  lowercase = False					,  lowercase = None					,  lowercase = 2_2050					,  lowercase = 5.0					,  lowercase = 220					,  )			->						Mapping[str, Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				OrderedDict()
          a__						:	int =				OnnxConfig.generate_dummy_inputs(
              self					,  preprocessor=preprocessor.feature_extractor					,  batch_size=lowercase					,  framework=lowercase					,  sampling_rate=lowercase					,  time_duration=lowercase					,  frequency=lowercase					,  )
          a__						:	List[Any] =				encoder_inputs['input_features'].shape[2]
          a__						:	Optional[int] =				encoder_sequence_length // 2 if self.use_past else seq_length

          a__						:	Any =				super().generate_dummy_inputs(
              preprocessor.tokenizer					,  lowercase					,  lowercase					,  lowercase					,  lowercase)

          a__						:	List[str] =				encoder_inputs.pop('input_features')
          a__						:	Optional[int] =				decoder_inputs.pop('decoder_input_ids')

          if "past_key_values" in decoder_inputs:
                 a__						:	List[str] =				decoder_inputs.pop('past_key_values')

          return dummy_inputs

   @property
   def 							__lowercase		(					self)			->						float:







          '''simple docstring'''

          return 1e-3


