
'''simple docstring'''



import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)





def        _A						(							lowercase__		):
						lowercase__							       =				VideoMAEConfig()

						set_architecture_configs(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE		)

						if "finetuned" not in model_name:
												lowercase__							       =				False

						if "finetuned" in model_name:
												lowercase__							       =				'huggingface/label-files'
												if "kinetics" in model_name:
																		lowercase__							       =				400
																		lowercase__							       =				'kinetics400-id2label.json'
												elif "ssv2" in model_name:
																		lowercase__							       =				174
																		lowercase__							       =				'something-something-v2-id2label.json'
												else:
																		raise ValueError("""Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned."""		)
												lowercase__							       =				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						repo_type="""dataset"""		)						,						"""r"""		)		)
												lowercase__							       =				{int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
												lowercase__							       =				idalabel
												lowercase__							       =				{v: k for k, v in idalabel.items()}

						return config





def        _A						(							lowercase__						,						lowercase__		):
						if "small" in model_name:
												lowercase__							       =				384
												lowercase__							       =				1536
												lowercase__							       =				12
												lowercase__							       =				16
												lowercase__							       =				12
												lowercase__							       =				3
												lowercase__							       =				192
												lowercase__							       =				768
						elif "large" in model_name:
												lowercase__							       =				1024
												lowercase__							       =				4096
												lowercase__							       =				24
												lowercase__							       =				16
												lowercase__							       =				12
												lowercase__							       =				8
												lowercase__							       =				512
												lowercase__							       =				2048
						elif "huge" in model_name:
												lowercase__							       =				1280
												lowercase__							       =				5120
												lowercase__							       =				32
												lowercase__							       =				16
												lowercase__							       =				12
												lowercase__							       =				8
												lowercase__							       =				640
												lowercase__							       =				2560
						elif "base" not in model_name:
												raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""		)





def        _A						(							lowercase__		):
						if "encoder." in name:
												lowercase__							       =				name.replace("""encoder."""						,						""""""		)
						if "cls_token" in name:
												lowercase__							       =				name.replace("""cls_token"""						,						"""videomae.embeddings.cls_token"""		)
						if "decoder_pos_embed" in name:
												lowercase__							       =				name.replace("""decoder_pos_embed"""						,						"""decoder.decoder_pos_embed"""		)
						if "pos_embed" in name and "decoder" not in name:
												lowercase__							       =				name.replace("""pos_embed"""						,						"""videomae.embeddings.position_embeddings"""		)
						if "patch_embed.proj" in name:
												lowercase__							       =				name.replace("""patch_embed.proj"""						,						"""videomae.embeddings.patch_embeddings.projection"""		)
						if "patch_embed.norm" in name:
												lowercase__							       =				name.replace("""patch_embed.norm"""						,						"""videomae.embeddings.norm"""		)
						if "decoder.blocks" in name:
												lowercase__							       =				name.replace("""decoder.blocks"""						,						"""decoder.decoder_layers"""		)
						if "blocks" in name:
												lowercase__							       =				name.replace("""blocks"""						,						"""videomae.encoder.layer"""		)
						if "attn.proj" in name:
												lowercase__							       =				name.replace("""attn.proj"""						,						"""attention.output.dense"""		)
						if "attn" in name and "bias" not in name:
												lowercase__							       =				name.replace("""attn"""						,						"""attention.self"""		)
						if "attn" in name:
												lowercase__							       =				name.replace("""attn"""						,						"""attention.attention"""		)
						if "norm1" in name:
												lowercase__							       =				name.replace("""norm1"""						,						"""layernorm_before"""		)
						if "norm2" in name:
												lowercase__							       =				name.replace("""norm2"""						,						"""layernorm_after"""		)
						if "mlp.fc1" in name:
												lowercase__							       =				name.replace("""mlp.fc1"""						,						"""intermediate.dense"""		)
						if "mlp.fc2" in name:
												lowercase__							       =				name.replace("""mlp.fc2"""						,						"""output.dense"""		)
						if "decoder_embed" in name:
												lowercase__							       =				name.replace("""decoder_embed"""						,						"""decoder.decoder_embed"""		)
						if "decoder_norm" in name:
												lowercase__							       =				name.replace("""decoder_norm"""						,						"""decoder.decoder_norm"""		)
						if "decoder_pred" in name:
												lowercase__							       =				name.replace("""decoder_pred"""						,						"""decoder.decoder_pred"""		)
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												lowercase__							       =				name.replace("""norm.weight"""						,						"""videomae.layernorm.weight"""		)
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												lowercase__							       =				name.replace("""norm.bias"""						,						"""videomae.layernorm.bias"""		)
						if "head" in name and "decoder" not in name:
												lowercase__							       =				name.replace("""head"""						,						"""classifier"""		)

						return name





def        _A						(							lowercase__						,						lowercase__		):
						for key in orig_state_dict.copy().keys():
												lowercase__							       =				orig_state_dict.pop(_SCREAMING_SNAKE_CASE		)

												if key.startswith("""encoder."""		):
																		lowercase__							       =				key.replace("""encoder."""						,						""""""		)

												if "qkv" in key:
																		lowercase__							       =				key.split("""."""		)
																		if key.startswith("""decoder.blocks"""		):
																								lowercase__							       =				config.decoder_hidden_size
																								lowercase__							       =				int(key_split[2]		)
																								lowercase__							       =				'decoder.decoder_layers.'
																								if "weight" in key:
																														lowercase__							       =				val[:dim, :]
																														lowercase__							       =				val[dim : dim * 2, :]
																														lowercase__							       =				val[-dim:, :]
																		else:
																								lowercase__							       =				config.hidden_size
																								lowercase__							       =				int(key_split[1]		)
																								lowercase__							       =				'videomae.encoder.layer.'
																								if "weight" in key:
																														lowercase__							       =				val[:dim, :]
																														lowercase__							       =				val[dim : dim * 2, :]
																														lowercase__							       =				val[-dim:, :]
												else:
																		lowercase__							       =				val

						return orig_state_dict





def        _A						(							):
						lowercase__							       =				hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""						,						filename="""eating_spaghetti.npy"""						,						repo_type="""dataset"""		)
						lowercase__							       =				np.load(_SCREAMING_SNAKE_CASE		)
						return list(_SCREAMING_SNAKE_CASE		)





def        _A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				get_videomae_config(_SCREAMING_SNAKE_CASE		)

						if "finetuned" in model_name:
												lowercase__							       =				VideoMAEForVideoClassification(_SCREAMING_SNAKE_CASE		)
						else:
												lowercase__							       =				VideoMAEForPreTraining(_SCREAMING_SNAKE_CASE		)

						# download original checkpoint, hosted on Google Drive
						lowercase__							       =				'pytorch_model.bin'
						gdown.cached_download(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						quiet=_SCREAMING_SNAKE_CASE		)
						lowercase__							       =				torch.load(_SCREAMING_SNAKE_CASE						,						map_location="""cpu"""		)
						if "model" in files:
												lowercase__							       =				files['model']
						else:
												lowercase__							       =				files['module']
						lowercase__							       =				convert_state_dict(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE		)

						model.load_state_dict(_SCREAMING_SNAKE_CASE		)
						model.eval()

						# verify model on basic input
						lowercase__							       =				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,						image_std=[0.5, 0.5, 0.5]		)
						lowercase__							       =				prepare_video()
						lowercase__							       =				image_processor(_SCREAMING_SNAKE_CASE						,						return_tensors="""pt"""		)

						if "finetuned" not in model_name:
												lowercase__							       =				hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""						,						filename="""bool_masked_pos.pt"""		)
												lowercase__							       =				torch.load(_SCREAMING_SNAKE_CASE		)

						lowercase__							       =				model(**_SCREAMING_SNAKE_CASE		)
						lowercase__							       =				outputs.logits

						lowercase__							       =				[
						    'videomae-small-finetuned-kinetics',
						    'videomae-small-finetuned-ssv2',
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    'videomae-base-short',
						    'videomae-base-short-finetuned-kinetics',
						    'videomae-base',
						    'videomae-base-finetuned-kinetics',
						    'videomae-large',
						    'videomae-large-finetuned-kinetics',
						    'videomae-huge-finetuned-kinetics',
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    'videomae-base-short-ssv2',
						    'videomae-base-short-finetuned-ssv2',
						    'videomae-base-ssv2',
						    'videomae-base-finetuned-ssv2',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												lowercase__							       =				torch.Size([1, 400]		)
												lowercase__							       =				torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]		)
						elif model_name == "videomae-small-finetuned-ssv2":
												lowercase__							       =				torch.Size([1, 174]		)
												lowercase__							       =				torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]		)
						elif model_name == "videomae-base":
												lowercase__							       =				torch.Size([1, 1408, 1536]		)
												lowercase__							       =				torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]		)
						elif model_name == "videomae-base-short":
												lowercase__							       =				torch.Size([1, 1408, 1536]		)
												lowercase__							       =				torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]		)
												# we verified the loss both for normalized and unnormalized targets for this one
												lowercase__							       =				torch.tensor([0.5_1_4_2]		) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]		)
						elif model_name == "videomae-large":
												lowercase__							       =				torch.Size([1, 1408, 1536]		)
												lowercase__							       =				torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]		)
						elif model_name == "videomae-large-finetuned-kinetics":
												lowercase__							       =				torch.Size([1, 400]		)
												lowercase__							       =				torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]		)
						elif model_name == "videomae-huge-finetuned-kinetics":
												lowercase__							       =				torch.Size([1, 400]		)
												lowercase__							       =				torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]		)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												lowercase__							       =				torch.Size([1, 400]		)
												lowercase__							       =				torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]		)
						elif model_name == "videomae-base-finetuned-kinetics":
												lowercase__							       =				torch.Size([1, 400]		)
												lowercase__							       =				torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]		)
						elif model_name == "videomae-base-short-ssv2":
												lowercase__							       =				torch.Size([1, 1408, 1536]		)
												lowercase__							       =				torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]		)
						elif model_name == "videomae-base-short-finetuned-ssv2":
												lowercase__							       =				torch.Size([1, 174]		)
												lowercase__							       =				torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]		)
						elif model_name == "videomae-base-ssv2":
												lowercase__							       =				torch.Size([1, 1408, 1536]		)
												lowercase__							       =				torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]		)
						elif model_name == "videomae-base-finetuned-ssv2":
												lowercase__							       =				torch.Size([1, 174]		)
												lowercase__							       =				torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]		)
						else:
												raise ValueError(f'''Model name not supported. Should be one of {model_names}'''		)

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3]						,						_SCREAMING_SNAKE_CASE						,						atol=1e-4		)
						else:
												print("""Logits:"""						,						logits[0, :3, :3]		)
												assert torch.allclose(logits[0, :3, :3]						,						_SCREAMING_SNAKE_CASE						,						atol=1e-4		)
						print("""Logits ok!"""		)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												lowercase__							       =				outputs.loss
												assert torch.allclose(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						atol=1e-4		)
												print("""Loss ok!"""		)

						if pytorch_dump_folder_path is not None:
												print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''		)
												image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)
												model.save_pretrained(_SCREAMING_SNAKE_CASE		)

						if push_to_hub:
												print("""Pushing to the hub..."""		)
												model.push_to_hub(_SCREAMING_SNAKE_CASE						,						organization="""nielsr"""		)


if __name__ == "__main__":
				__A								=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
				    type=str,
				    help=(
				        "URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"
				        " download link."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="/Users/nielsrogge/Documents/VideoMAE/Test",
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				__A								=			parser.parse_args()
				convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__					       =	random.Random()







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=1.0		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  if rng is None:
    a__:     Any										=	global_rng

  a__:     int										=	[]
  for batch_idx in range(shape[0]   ):
    values.append([]   )
    for _ in range(shape[1]   ):
      values[-1].append(rng.random() * scale   )

  return values




class    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=7    ,			lowercase=4_00    ,			lowercase=20_00    ,			lowercase=1    ,			lowercase=0.0    ,			lowercase=1_60_00    ,			lowercase=True    ,			lowercase=True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	parent
        a__:     Optional[int]										=	batch_size
        a__:     Optional[Any]										=	min_seq_length
        a__:     Optional[int]										=	max_seq_length
        a__:     Tuple										=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:     Dict										=	feature_size
        a__:     Any										=	padding_value
        a__:     Optional[Any]										=	sampling_rate
        a__:     Optional[Any]										=	return_attention_mask
        a__:     str										=	do_normalize



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



      def   lowerCamelCase_   (						self    ,			lowercase=False    ,			lowercase=False)	->			Tuple:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*lowercase))

        if equal_length:
          a__:     Dict										=	floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:     List[Any]										=	[
              _flatten(floats_list((x, self.feature_size)))
              for x in range(self.min_seq_length    ,			self.max_seq_length    ,			self.seq_length_diff)
          ]

        if numpify:
          a__:     str										=	[np.asarray(lowercase) for x in speech_inputs]

        return speech_inputs







class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		WavaVecaFeatureExtractor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	WavaVecaFeatureExtractionTester(self)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        self.assertTrue(np.all(np.mean(lowercase    ,			axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(lowercase    ,			axis=0) - 1) < 1e-3))



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





        '''simple docstring'''





        a__:     List[Any]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:     Optional[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     List[str]										=	[np.asarray(lowercase) for speech_input in speech_inputs]

        # Test not batched input
        a__:     Optional[Any]										=	feat_extract(speech_inputs[0]    ,			return_tensors='np').input_values
        a__:     Dict										=	feat_extract(np_speech_inputs[0]    ,			return_tensors='np').input_values
        self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test batched
        a__:     Dict										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase    ,			lowercase):
          self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test 2-D numpy arrays are batched.
        a__:     int										=	[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
        a__:     Union[str, Any]										=	np.asarray(lowercase)
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     Any										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase    ,			lowercase):
          self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     List[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]

        a__:     Optional[int]										=	['longest', 'max_length', 'do_not_pad']
        a__:     List[Any]										=	[None, 16_00, None]
        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     Dict										=	feat_extract(lowercase    ,			padding=lowercase    ,			max_length=lowercase    ,			return_tensors='np')
          a__:     Union[str, Any]										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



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





        '''simple docstring'''





        a__:     str										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Optional[int]										=	range(8_00    ,			14_00    ,			2_00)
        a__:     List[str]										=	[floats_list((1, x))[0] for x in lengths]

        a__:     Tuple										=	['longest', 'max_length', 'do_not_pad']
        a__:     Dict										=	[None, 16_00, None]

        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     int										=	feat_extract(lowercase    ,			max_length=lowercase    ,			padding=lowercase)
          a__:     Any										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



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





        '''simple docstring'''





        a__:     Any										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Any										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Dict										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='max_length'    ,			return_tensors='np')
        a__:     int										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     int										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     str										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     Any										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 10_00))

        a__:     Dict										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Tuple										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=20_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     str										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 12_00))



      @require_torch
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        import torch

        a__:     Optional[int]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Tuple										=	np.random.rand(1_00).astype(np.floataa)
        a__:     Tuple										=	np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          a__:     Any										=	feature_extractor.pad([{'input_values': inputs}]    ,			return_tensors='np')
          self.assertTrue(np_processed.input_values.dtype == np.floataa)
          a__:     Optional[Any]										=	feature_extractor.pad([{'input_values': inputs}]    ,			return_tensors='pt')
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



      @slow
      @require_torch
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          a__:     str										=	WavaVecaConfig.from_pretrained(lowercase)
          a__:     str										=	WavaVecaFeatureExtractor.from_pretrained(lowercase)

          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(feat_extract.return_attention_mask    ,			config.feat_extract_norm == 'layer')


