



"""simple docstring"""


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 :Tuple   ,  lowercase_ :str   ,  lowercase_ :Optional[Any]=3   ,  lowercase_ :List[str]=32   ,  lowercase_ :str=3   ,  lowercase_ :Optional[int]=10   ,  lowercase_ :Union[str, Any]=[8, 16, 32, 64]   ,  lowercase_ :Any=[1, 1, 2, 1]   ,  lowercase_ :Dict=True   ,  lowercase_ :Optional[int]=True   ,  lowercase_ :List[Any]="relu"   ,  lowercase_ :List[str]=3   ,  lowercase_ :Optional[Any]=None   ,  lowercase_ :Dict=["stage2", "stage3", "stage4"]   ,  lowercase_ :str=[2, 3, 4]   ,  lowercase_ :Union[str, Any]=1   ,  )				->			Union[str, Any]:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = image_size
												UpperCAmelCase             = num_channels
												UpperCAmelCase             = embeddings_size
												UpperCAmelCase             = hidden_sizes
												UpperCAmelCase             = depths
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = num_labels
												UpperCAmelCase             = scope
												UpperCAmelCase             = len(lowercase_					)
												UpperCAmelCase             = out_features
												UpperCAmelCase             = out_indices
												UpperCAmelCase             = num_groups






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			List[str]:
												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.num_labels					)

												UpperCAmelCase             = self.get_config()

												return config, pixel_values, labels






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			List[str]:
												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 	UpperCAmelCase__     (					self :List[str]   ,  lowercase_ :str   ,  lowercase_ :List[str]   ,  lowercase_ :Dict					)				->			List[Any]:
												UpperCAmelCase             = BitModel(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :Tuple   ,  lowercase_ :List[str]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Tuple					)				->			List[str]:
												UpperCAmelCase             = self.num_labels
												UpperCAmelCase             = BitForImageClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Tuple   ,  lowercase_ :Optional[Any]					)				->			List[str]:
												UpperCAmelCase             = BitBackbone(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = 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
												UpperCAmelCase             = None
												UpperCAmelCase             = BitBackbone(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :int					)				->			int:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = config_and_inputs
												UpperCAmelCase             = {'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						__UpperCamelCase						=							(
						    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						__UpperCamelCase						=							False
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Union[str, Any]:
												UpperCAmelCase             = BitModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=lowercase_   ,  has_text_modality=lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			str:
												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 	UpperCAmelCase__     (					self :int					)				->			Tuple:
												return






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






						@unittest.skip(reason='Bit does not use inputs_embeds'					)
						def 	UpperCAmelCase__     (					self :str					)				->			Dict:
												pass






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






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Union[str, Any]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase             = model_class(lowercase_					)
																		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]   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			List[Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			List[Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :List[Any]					)				->			str:
												def check_hidden_states_output(lowercase_ :int   ,  lowercase_ :Optional[Any]   ,  lowercase_ :List[str]					):
																		UpperCAmelCase             = model_class(lowercase_					)
																		model.to(lowercase_					)
																		model.eval()

																		with torch.no_grad():
																								UpperCAmelCase             = model(**self._prepare_for_class(lowercase_   ,  lowercase_					)					)

																		UpperCAmelCase             = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		UpperCAmelCase             = 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]   ,  )

												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = ['preactivation', 'bottleneck']
												for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																								UpperCAmelCase             = layer_type
																								UpperCAmelCase             = True
																								check_hidden_states_output(lowercase_   ,  lowercase_   ,  lowercase_					)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								UpperCAmelCase             = True

																								check_hidden_states_output(lowercase_   ,  lowercase_   ,  lowercase_					)






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






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowercase_					)






						@slow
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Union[str, Any]:
												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase             = BitModel.from_pretrained(lowercase_					)
																		self.assertIsNotNone(lowercase_					)


def  _lowerCAmelCase      (   ):
						UpperCAmelCase             = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image







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



						"""simple docstring"""






						@cached_property
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Tuple:
												return (
												    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					) if is_vision_available() else None
												)






						@slow
						def 	UpperCAmelCase__     (					self :List[str]					)				->			List[Any]:
												UpperCAmelCase             = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(lowercase_					)

												UpperCAmelCase             = self.default_image_processor
												UpperCAmelCase             = prepare_img()
												UpperCAmelCase             = image_processor(images=lowercase_   ,  return_tensors='pt'					).to(lowercase_					)

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase             = model(**lowercase_					)

												# verify the logits
												UpperCAmelCase             = torch.Size((1, 10_00)					)
												self.assertEqual(outputs.logits.shape   ,  lowercase_					)

												UpperCAmelCase             = torch.tensor([[-0.6526, -0.5263, -1.4398]]					).to(lowercase_					)

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







@require_torch
class       A_			(     SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(BitBackbone,) if is_torch_available() else ()
						__UpperCamelCase						=							BitConfig

						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			List[str]:
												UpperCAmelCase             = BitModelTester(self					)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCamelCase__ :   List[str]					=							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "speech_to_text"
 lowercase_				    =    ["past_key_values"]
 lowercase_				    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		List[Any]=10_000				,				_lowerCAmelCase  :		List[Any]=12				,				_lowerCAmelCase  :		Union[str, Any]=2_048				,				_lowerCAmelCase  :		Optional[int]=4				,				_lowerCAmelCase  :		Union[str, Any]=6				,				_lowerCAmelCase  :		Optional[int]=2_048				,				_lowerCAmelCase  :		Optional[Any]=4				,				_lowerCAmelCase  :		Any=0.0				,				_lowerCAmelCase  :		Optional[Any]=0.0				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		List[Any]=True				,				_lowerCAmelCase  :		int="relu"				,				_lowerCAmelCase  :		Union[str, Any]=256				,				_lowerCAmelCase  :		List[str]=0.1				,				_lowerCAmelCase  :		str=0.0				,				_lowerCAmelCase  :		Tuple=0.0				,				_lowerCAmelCase  :		List[str]=0.02				,				_lowerCAmelCase  :		List[str]=2				,				_lowerCAmelCase  :		Tuple=True				,				_lowerCAmelCase  :		List[str]=1				,				_lowerCAmelCase  :		str=0				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Union[str, Any]=6_000				,				_lowerCAmelCase  :		List[str]=1_024				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Optional[Any]=(5, 5)				,				_lowerCAmelCase  :		str=1_024				,				_lowerCAmelCase  :		str=80				,				_lowerCAmelCase  :		Tuple=1				,				**_lowerCAmelCase  :		Any				,				):
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			d_model
       SCREAMING_SNAKE_CASE_							=			encoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			encoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			decoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			decoder_layers
       SCREAMING_SNAKE_CASE_							=			decoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			dropout
       SCREAMING_SNAKE_CASE_							=			attention_dropout
       SCREAMING_SNAKE_CASE_							=			activation_dropout
       SCREAMING_SNAKE_CASE_							=			activation_function
       SCREAMING_SNAKE_CASE_							=			init_std
       SCREAMING_SNAKE_CASE_							=			encoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			decoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			use_cache
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_							=			max_source_positions
       SCREAMING_SNAKE_CASE_							=			max_target_positions
       SCREAMING_SNAKE_CASE_							=			num_conv_layers
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			conv_channels
       SCREAMING_SNAKE_CASE_							=			input_feat_per_channel
       SCREAMING_SNAKE_CASE_							=			input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, "
                 F"`config.num_conv_layers = {self.num_conv_layers}`."						)

       super().__init__(
           pad_token_id=_lowerCAmelCase				,				bos_token_id=_lowerCAmelCase				,				eos_token_id=_lowerCAmelCase				,				is_encoder_decoder=_lowerCAmelCase				,				decoder_start_token_id=_lowerCAmelCase				,				**_lowerCAmelCase				,				)