'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor






def 				snake_case__							(			_A:					Optional[int] )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowerCAmelCase   					=  4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				lowerCAmelCase   					=  4
				lowerCAmelCase   					=  48
				lowerCAmelCase   					=  "pixelshuffle_aux"
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowerCAmelCase   					=  [6, 6, 6, 6]
				lowerCAmelCase   					=  60
				lowerCAmelCase   					=  [6, 6, 6, 6]
				lowerCAmelCase   					=  "pixelshuffledirect"
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowerCAmelCase   					=  4
				lowerCAmelCase   					=  "nearest+conv"
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				lowerCAmelCase   					=  1
				lowerCAmelCase   					=  1
				lowerCAmelCase   					=  126
				lowerCAmelCase   					=  7
				lowerCAmelCase   					=  255.0
				lowerCAmelCase   					=  ""

		return config






def 				snake_case__							(			_A:					Union[str, Any]      ,   _A:					List[Any] )    ->	Tuple:

		'''simple docstring'''


		if "patch_embed.proj" in name and "layers" not in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.proj"""      ,   """embeddings.patch_embeddings.projection""" )
		if "patch_embed.norm" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.norm"""      ,   """embeddings.patch_embeddings.layernorm""" )
		if "layers" in name:
				lowerCAmelCase   					=  name.replace("""layers"""      ,   """encoder.stages""" )
		if "residual_group.blocks" in name:
				lowerCAmelCase   					=  name.replace("""residual_group.blocks"""      ,   """layers""" )
		if "attn.proj" in name:
				lowerCAmelCase   					=  name.replace("""attn.proj"""      ,   """attention.output.dense""" )
		if "attn" in name:
				lowerCAmelCase   					=  name.replace("""attn"""      ,   """attention.self""" )
		if "norm1" in name:
				lowerCAmelCase   					=  name.replace("""norm1"""      ,   """layernorm_before""" )
		if "norm2" in name:
				lowerCAmelCase   					=  name.replace("""norm2"""      ,   """layernorm_after""" )
		if "mlp.fc1" in name:
				lowerCAmelCase   					=  name.replace("""mlp.fc1"""      ,   """intermediate.dense""" )
		if "mlp.fc2" in name:
				lowerCAmelCase   					=  name.replace("""mlp.fc2"""      ,   """output.dense""" )
		if "q_bias" in name:
				lowerCAmelCase   					=  name.replace("""q_bias"""      ,   """query.bias""" )
		if "k_bias" in name:
				lowerCAmelCase   					=  name.replace("""k_bias"""      ,   """key.bias""" )
		if "v_bias" in name:
				lowerCAmelCase   					=  name.replace("""v_bias"""      ,   """value.bias""" )
		if "cpb_mlp" in name:
				lowerCAmelCase   					=  name.replace("""cpb_mlp"""      ,   """continuous_position_bias_mlp""" )
		if "patch_embed.proj" in name:
				lowerCAmelCase   					=  name.replace("""patch_embed.proj"""      ,   """patch_embed.projection""" )

		if name == "norm.weight":
				lowerCAmelCase   					=  "layernorm.weight"
		if name == "norm.bias":
				lowerCAmelCase   					=  "layernorm.bias"

		if "conv_first" in name:
				lowerCAmelCase   					=  name.replace("""conv_first"""      ,   """first_convolution""" )

		if (
		    "upsample" in name
		    or "conv_before_upsample" in name
		    or "conv_bicubic" in name
		    or "conv_up" in name
		    or "conv_hr" in name
		    or "conv_last" in name
		    or "aux" in name
		):
				# heads
				if "conv_last" in name:
						lowerCAmelCase   					=  name.replace("""conv_last"""      ,   """final_convolution""" )
				if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
						if "conv_before_upsample.0" in name:
								lowerCAmelCase   					=  name.replace("""conv_before_upsample.0"""      ,   """conv_before_upsample""" )
						if "upsample.0" in name:
								lowerCAmelCase   					=  name.replace("""upsample.0"""      ,   """upsample.convolution_0""" )
						if "upsample.2" in name:
								lowerCAmelCase   					=  name.replace("""upsample.2"""      ,   """upsample.convolution_1""" )
						lowerCAmelCase   					=  "upsample." + name
				elif config.upsampler == "pixelshuffledirect":
						lowerCAmelCase   					=  name.replace("""upsample.0.weight"""      ,   """upsample.conv.weight""" )
						lowerCAmelCase   					=  name.replace("""upsample.0.bias"""      ,   """upsample.conv.bias""" )
				else:
						pass
		else:
				lowerCAmelCase   					=  "swin2sr." + name

		return name






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					str )    ->	Optional[Any]:

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCAmelCase   					=  orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

				if "qkv" in key:
						lowerCAmelCase   					=  key.split(""".""" )
						lowerCAmelCase   					=  int(key_split[1] )
						lowerCAmelCase   					=  int(key_split[4] )
						lowerCAmelCase   					=  config.embed_dim

						if "weight" in key:
								lowerCAmelCase   					=  val[:dim, :]
								lowerCAmelCase   					=  val[dim : dim * 2, :]
								lowerCAmelCase   					=  val[-dim:, :]
						else:
								lowerCAmelCase   					=  val[:dim]
								lowerCAmelCase   					=  val[dim : dim * 2]
								lowerCAmelCase   					=  val[-dim:]
						pass
				else:
						lowerCAmelCase   					=  val

		return orig_state_dict






def 				snake_case__							(			_A:					int      ,   _A:					Any      ,   _A:					Tuple )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  get_config(__SCREAMING_SNAKE_CASE )
		lowerCAmelCase   					=  SwinaSRForImageSuperResolution(__SCREAMING_SNAKE_CASE )
		model.eval()

		lowerCAmelCase   					=  torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE      ,   map_location="""cpu""" )
		lowerCAmelCase   					=  convert_state_dict(__SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE )
		lowerCAmelCase   					=  model.load_state_dict(__SCREAMING_SNAKE_CASE      ,   strict=__SCREAMING_SNAKE_CASE )

		if len(__SCREAMING_SNAKE_CASE ) > 0:
				raise ValueError("""Missing keys when converting: {}""".format(__SCREAMING_SNAKE_CASE ) )
		for key in unexpected_keys:
				if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
						raise ValueError(f"Unexpected key {key} in state_dict" )

    # verify values
		lowerCAmelCase   					=  "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
		lowerCAmelCase   					=  Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,   stream=__SCREAMING_SNAKE_CASE ).raw ).convert("""RGB""" )
		lowerCAmelCase   					=  SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		lowerCAmelCase   					=  126 if "Jpeg" in checkpoint_url else 256
		lowerCAmelCase   					=  Compose(
		    [
		        Resize((image_size, image_size) ),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]      ,   std=[0.229, 0.224, 0.225] ),
		    ] )
		lowerCAmelCase   					=  transforms(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )

		if config.num_channels == 1:
				lowerCAmelCase   					=  pixel_values[:, 0, :, :].unsqueeze(1 )

		lowerCAmelCase   					=  model(__SCREAMING_SNAKE_CASE )

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				lowerCAmelCase   					=  torch.Size([1, 3, 512, 512] )
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]] )
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowerCAmelCase   					=  torch.Size([1, 3, 1024, 1024] )
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]] )
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				lowerCAmelCase   					=  torch.Size([1, 3, 1024, 1024] )
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]] )
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowerCAmelCase   					=  torch.Size([1, 3, 512, 512] )
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]] )
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowerCAmelCase   					=  torch.Size([1, 3, 1024, 1024] )
				lowerCAmelCase   					=  torch.tensor(
				    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]] )

		assert (
		    outputs.reconstruction.shape == expected_shape
		), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
		assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]      ,   __SCREAMING_SNAKE_CASE      ,   atol=1e-3 )
		print("""Looks ok!""" )

		lowerCAmelCase   					=  {
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
		        "swin2SR-classical-sr-x2-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
		        "swin2SR-classical-sr-x4-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
		        "swin2SR-compressed-sr-x4-48"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
		        "swin2SR-lightweight-x2-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
		        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
		    ),
		}
		lowerCAmelCase   					=  url_to_name[checkpoint_url]

		if pytorch_dump_folder_path is not None:
				print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
				model.save_pretrained(__SCREAMING_SNAKE_CASE )
				print(f"Saving image processor to {pytorch_dump_folder_path}" )
				processor.save_pretrained(__SCREAMING_SNAKE_CASE )

		if push_to_hub:
				model.push_to_hub(f"caidas/{model_name}" )
				processor.push_to_hub(f"caidas/{model_name}" )


if __name__ == "__main__":
						__lowercase          =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
						    type=str,
						    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

						__lowercase          =  parser.parse_args()
						convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       int          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						self.assertTrue(all(len(UpperCamelCase__) == len(UpperCamelCase__) for x, y in zip(UpperCamelCase__     ,  processed_features[input_name])))

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_torch
				def 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="pt")

						__lowerCAmelCase:       str          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Any          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				Tuple):
								__lowerCAmelCase:       Optional[int]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				str     ,  UpperCamelCase__							:				str):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[int]          =			self.feat_extract_tester.seq_length_diff
						__lowerCAmelCase:       str          =			self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.min_seq_length
						__lowerCAmelCase:       Union[str, Any]          =			self.feat_extract_tester.batch_size
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						# max_length parameter has to be provided when setting `padding="max_length"`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length")[input_name]

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						self.assertTrue(len(input_a[0]) == pad_min_length)
						self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
						self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
						self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

						# test padding for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == expected_mult_pad_length for x in input_a))
						self.assertEqual(input_a.shape[:2]     ,  (batch_size, expected_mult_pad_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == feature_size)

						# Check padding value is correct
						__lowerCAmelCase:       Any          =			(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
						    < 1e-3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				List[Any]):
								__lowerCAmelCase:       List[str]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						# truncate to smallest
						__lowerCAmelCase:       int          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to smallest with np
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np"     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

						self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  truncation=UpperCamelCase__)[input_name]

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase:       Optional[Any]          =			len(speech_inputs[0])
						if expected_length % pad_to_multiple_of != 0:
								__lowerCAmelCase:       Optional[Any]          =			((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

						self.assertTrue(len(input_a[0]) == expected_length)
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

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



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

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



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

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



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       str          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="pt")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)

				@require_tf
				def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="tf")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)

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



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       Any          =			True
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Tuple          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Optional[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(list(processed.attention_mask.shape)     ,  list(processed[input_name].shape[:2]))
						self.assertListEqual(processed.attention_mask.sum(-1).tolist()     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape)     ,  [processed_pad[input_name].shape[0], max_length])
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1).tolist()     ,  [max_length for x in speech_inputs])


