





import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
    from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=3					,  lowercase=32					,  lowercase=3					,  lowercase=10					,  lowercase=[8, 16, 32, 64]					,  lowercase=[1, 1, 2, 1]					,  lowercase=True					,  lowercase=True					,  lowercase="relu"					,  lowercase=3					,  lowercase=None					,  lowercase=["stage2", "stage3", "stage4"]					,  lowercase=[2, 3, 4]					,  lowercase=1					,  )			->						Optional[int]:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	Any =				batch_size
          a__						:	Any =				image_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	Optional[int] =				embeddings_size
          a__						:	str =				hidden_sizes
          a__						:	Optional[Any] =				depths
          a__						:	Optional[Any] =				is_training
          a__						:	int =				use_labels
          a__						:	Union[str, Any] =				hidden_act
          a__						:	Optional[int] =				num_labels
          a__						:	Optional[int] =				scope
          a__						:	Tuple =				len(lowercase)
          a__						:	Union[str, Any] =				out_features
          a__						:	List[str] =				out_indices
          a__						:	Any =				num_groups

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	Optional[Any] =				None
          if self.use_labels:
                 a__						:	Tuple =				ids_tensor([self.batch_size]					,  self.num_labels)

          a__						:	Optional[int] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          return BitConfig(
              num_channels=self.num_channels					,  embeddings_size=self.embeddings_size					,  hidden_sizes=self.hidden_sizes					,  depths=self.depths					,  hidden_act=self.hidden_act					,  num_labels=self.num_labels					,  out_features=self.out_features					,  out_indices=self.out_indices					,  num_groups=self.num_groups					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	List[Any] =				BitModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase)
          self.parent.assertEqual(
              result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.num_labels
          a__						:	Union[str, Any] =				BitForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	List[Any] =				BitBackbone(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)					,  len(config.out_features))
          self.parent.assertListEqual(list(result.feature_maps[0].shape)					,  [self.batch_size, self.hidden_sizes[1], 4, 4])

          # verify channels
          self.parent.assertEqual(len(model.channels)					,  len(config.out_features))
          self.parent.assertListEqual(model.channels					,  config.hidden_sizes[1:])

          # verify backbone works with out_features=None
          a__						:	Union[str, Any] =				None
          a__						:	List[Any] =				BitBackbone(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[int] =				model(lowercase)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)					,  1)
          self.parent.assertListEqual(list(result.feature_maps[0].shape)					,  [self.batch_size, self.hidden_sizes[-1], 1, 1])

          # verify channels
          self.parent.assertEqual(len(model.channels)					,  1)
          self.parent.assertListEqual(model.channels					,  [config.hidden_sizes[-1]])

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	int =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Optional[int] =				config_and_inputs
          a__						:	str =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   __A   :				List[Any]         =  (
       {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				Optional[Any]         =  False
   __A   :				int         =  False
   __A   :				Optional[int]         =  False
   __A   :				str         =  False
   __A   :				Optional[Any]         =  False

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				BitModelTester(self)
          a__						:	int =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase)

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







          '''simple docstring'''

          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()

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







          '''simple docstring'''

          return

   @unittest.skip(reason='Bit does not output attentions')
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          pass

   @unittest.skip(reason='Bit does not use inputs_embeds')
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

   @unittest.skip(reason='Bit does not support input and output embeddings')
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          pass

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







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 a__						:	Any =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

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







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

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

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(config=lowercase)
                 for name, module in model.named_modules():
                        if isinstance(lowercase					,  (nn.BatchNormad, nn.GroupNorm)):
                               self.assertTrue(
                                   torch.all(module.weight == 1)					,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,  )
                               self.assertTrue(
                                   torch.all(module.bias == 0)					,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,  )

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







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	Union[str, Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	List[str] =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 a__						:	List[str] =				self.model_tester.num_stages
                 self.assertEqual(len(lowercase)					,  expected_num_stages + 1)

                 # Bit's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:])					,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,  )

          a__ ,							a__						:	Any =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	int =				['preactivation', 'bottleneck']
          for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                        a__						:	int =				layer_type
                        a__						:	List[str] =				True
                        check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                        # check that output_hidden_states also work using config
                        del inputs_dict["output_hidden_states"]
                        a__						:	int =				True

                        check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   @unittest.skip(reason='Bit does not use feedforward chunking')
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Dict =				BitModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
          )

   @slow
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase)

          a__						:	Tuple =				self.default_image_processor
          a__						:	str =				prepare_img()
          a__						:	Tuple =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Any =				model(**lowercase)

          # verify the logits
          a__						:	Any =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]).to(lowercase)

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





@require_torch
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  (BitBackbone,) if is_torch_available() else ()
   __A   :				Optional[Any]         =  BitConfig

   __A   :				int         =  False

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				BitModelTester(self)









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


