



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_				= logging.get_logger()




@dataclass
class 			snake_case_						:
							__lowerCAmelCase    					=					4_2
							__lowerCAmelCase    					=					field(default_factory=a_				)
							__lowerCAmelCase    					=					field(default_factory=a_				)






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      str 						=     len(list(m.modules()      )      ) == 1 or isinstance(a_			,     nn.Convad      ) or isinstance(a_			,     nn.BatchNormad      )
									if has_not_submodules:
											self.traced.append(a_      )






							def __call__(   self			,     a_      ):
									for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(a_      )
									[x.remove() for x in self.handles]
									return self






							@property
							def snake_case_  (   self      ):
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda a_      : len(list(x.state_dict().keys()      )      ) > 0			,     self.traced      )      )




@dataclass
class 			snake_case_						:
							__lowerCAmelCase    					=					4_2
							__lowerCAmelCase    					=					4_2
							__lowerCAmelCase    					=					1
							__lowerCAmelCase    					=					field(default_factory=a_				)
							__lowerCAmelCase    					=					field(default_factory=a_				)
							__lowerCAmelCase    					=					True






							def __call__(   self			,     a_      ):
									a_     :      List[str] 						=     Tracker(self.dest      )(a_      ).parametrized
									a_     :      Union[str, Any] 						=     Tracker(self.src      )(a_      ).parametrized

									a_     :      Tuple 						=     list(filter(lambda a_      : type(a_      ) not in self.src_skip			,     a_      )      )
									a_     :      Dict 						=     list(filter(lambda a_      : type(a_      ) not in self.dest_skip			,     a_      )      )

									if len(a_      ) != len(a_      ) and self.raise_if_mismatch:
											raise Exception(
											    F"""Numbers of operations are different. Source module has {len(a_      )} operations while"""
											    F""" destination module has {len(a_      )}."""      )

									for dest_m, src_m in zip(a_			,     a_      ):
											dest_m.load_state_dict(src_m.state_dict()      )
											if self.verbose == 1:
													print(F"""Transfered from={src_m} to={dest_m}"""      )




class 			snake_case_						(						nn.Module				):






							def __init__(   self			,     a_      ):
									super().__init__()

									a_     :      List[Tuple[str, nn.Module]] 						=     []
									# - get the stem
									feature_blocks.append(("conv1", model.stem)      )
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
											assert k.startswith("block"      ), F"""Unexpected layer name {k}"""
											a_     :      Tuple 						=     len(a_      ) + 1
											feature_blocks.append((F"""res{block_index}""", v)      )

									a_     :      List[Any] 						=     nn.ModuleDict(a_      )






							def snake_case_  (   self			,     a_      ):
									return get_trunk_forward_outputs(
									    a_			,     out_feat_keys=a_			,     feature_blocks=self._feature_blocks			,     )




class 			snake_case_						(						a_				):






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     x.split("-"      )
									return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )






							def __getitem__(   self			,     a_      ):
									# default to timm!
									if x not in self:
											a_     :      Tuple 						=     self.convert_name_to_timm(a_      )
											a_     :      Dict 						=     partial(lambda: (timm.create_model(a_			,     pretrained=a_      ).eval(), None)      )

									else:
											a_     :      Optional[int] 						=     super().__getitem__(a_      )

									return val




class 			snake_case_						(						a_				):






							def __getitem__(   self			,     a_      ):
									if "seer" in x and "in1k" not in x:
											a_     :      Tuple 						=     RegNetModel
									else:
											a_     :      List[Any] 						=     RegNetForImageClassification
									return val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:
		for from_key, to_key in keys:
				a_     :      Optional[int] 						=     from_state_dict[from_key].clone()
				print(F"""Copied key={from_key} to={to_key}"""	)
		return to_state_dict



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = True, )			->      Optional[int]:
		print(F"""Converting {name}..."""	)
		with torch.no_grad():
				a_     :      List[str] 						=     from_model_func()
				a_     :      Tuple 						=     our_model_func(SCREAMING_SNAKE_CASE__	).eval()
				a_     :      Tuple 						=     ModuleTransfer(src=SCREAMING_SNAKE_CASE__, dest=SCREAMING_SNAKE_CASE__, raise_if_mismatch=SCREAMING_SNAKE_CASE__	)
				a_     :      int 						=     torch.randn((1, 3, 224, 224)	)
				module_transfer(SCREAMING_SNAKE_CASE__	)

		if from_state_dict is not None:
				a_     :      Optional[Any] 						=     []
				# for seer - in1k finetuned we have to manually copy the head
				if "seer" in name and "in1k" in name:
						a_     :      str 						=     [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
				a_     :      Tuple 						=     manually_copy_vissl_head(SCREAMING_SNAKE_CASE__, our_model.state_dict(), SCREAMING_SNAKE_CASE__	)
				our_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

		a_     :      List[str] 						=     our_model(SCREAMING_SNAKE_CASE__, output_hidden_states=SCREAMING_SNAKE_CASE__	)
		a_     :      Any 						=     (
		    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) else our_outputs.last_hidden_state
		)

		a_     :      int 						=     from_model(SCREAMING_SNAKE_CASE__	)
		a_     :      Optional[Any] 						=     from_output[-1] if type(SCREAMING_SNAKE_CASE__	) is list else from_output

		# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
		if "seer" in name and "in1k" in name:
				a_     :      Optional[int] 						=     our_outputs.hidden_states[-1]

		assert torch.allclose(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	), "The model logits don't match the original one."

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / name, commit_message="Add model", use_temp_dir=SCREAMING_SNAKE_CASE__, )

				a_     :      Dict 						=     224 if "seer" not in name else 384
				# we can use the convnext one
				a_     :      List[str] 						=     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=SCREAMING_SNAKE_CASE__	)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name, commit_message="Add image processor", use_temp_dir=SCREAMING_SNAKE_CASE__, )

				print(F"""Pushed {name}"""	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = None, SCREAMING_SNAKE_CASE__ = True	)			->      Dict:
		a_     :      Optional[Any] 						=     "imagenet-1k-id2label.json"
		a_     :      List[str] 						=     1_000
		a_     :      int 						=     (1, num_labels)

		a_     :      List[Any] 						=     "huggingface/label-files"
		a_     :      Optional[int] 						=     num_labels
		a_     :      Dict 						=     json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, repo_type="dataset"	)	), "r"	)	)
		a_     :      Optional[Any] 						=     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

		a_     :      Optional[int] 						=     idalabel
		a_     :      int 						=     {v: k for k, v in idalabel.items()}

		a_     :      List[str] 						=     partial(SCREAMING_SNAKE_CASE__, num_labels=SCREAMING_SNAKE_CASE__, idalabel=SCREAMING_SNAKE_CASE__, labelaid=SCREAMING_SNAKE_CASE__	)

		a_     :      Optional[int] 						=     {
		    "regnet-x-002": ImageNetPreTrainedConfig(
		        depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type="x"	),
		    "regnet-x-004": ImageNetPreTrainedConfig(
		        depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type="x"	),
		    "regnet-x-006": ImageNetPreTrainedConfig(
		        depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type="x"	),
		    "regnet-x-008": ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type="x"	),
		    "regnet-x-016": ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type="x"	),
		    "regnet-x-032": ImageNetPreTrainedConfig(
		        depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1_008], groups_width=48, layer_type="x"	),
		    "regnet-x-040": ImageNetPreTrainedConfig(
		        depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1_360], groups_width=40, layer_type="x"	),
		    "regnet-x-064": ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1_624], groups_width=56, layer_type="x"	),
		    "regnet-x-080": ImageNetPreTrainedConfig(
		        depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1_920], groups_width=120, layer_type="x"	),
		    "regnet-x-120": ImageNetPreTrainedConfig(
		        depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2_240], groups_width=112, layer_type="x"	),
		    "regnet-x-160": ImageNetPreTrainedConfig(
		        depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2_048], groups_width=128, layer_type="x"	),
		    "regnet-x-320": ImageNetPreTrainedConfig(
		        depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1_344, 2_520], groups_width=168, layer_type="x"	),
		    # y variant
		    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8	),
		    "regnet-y-004": ImageNetPreTrainedConfig(
		        depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8	),
		    "regnet-y-006": ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16	),
		    "regnet-y-008": ImageNetPreTrainedConfig(
		        depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16	),
		    "regnet-y-016": ImageNetPreTrainedConfig(
		        depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24	),
		    "regnet-y-032": ImageNetPreTrainedConfig(
		        depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1_512], groups_width=24	),
		    "regnet-y-040": ImageNetPreTrainedConfig(
		        depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1_088], groups_width=64	),
		    "regnet-y-064": ImageNetPreTrainedConfig(
		        depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1_296], groups_width=72	),
		    "regnet-y-080": ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2_016], groups_width=56	),
		    "regnet-y-120": ImageNetPreTrainedConfig(
		        depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2_240], groups_width=112	),
		    "regnet-y-160": ImageNetPreTrainedConfig(
		        depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1_232, 3_024], groups_width=112	),
		    "regnet-y-320": ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232	),
		    # models created by SEER -> https://arxiv.org/abs/2202.08360
		    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232	),
		    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1_968, 4_920], groups_width=328	),
		    "regnet-y-1280-seer": RegNetConfig(
		        depths=[2, 7, 17, 1], hidden_sizes=[528, 1_056, 2_904, 7_392], groups_width=264	),
		    "regnet-y-2560-seer": RegNetConfig(
		        depths=[3, 7, 16, 1], hidden_sizes=[640, 1_696, 2_544, 5_088], groups_width=640	),
		    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1], hidden_sizes=[2_020, 4_040, 11_110, 28_280], groups_width=1_010	),
		    # finetuned on imagenet
		    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1_392, 3_712], groups_width=232	),
		    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1_968, 4_920], groups_width=328	),
		    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1], hidden_sizes=[528, 1_056, 2_904, 7_392], groups_width=264	),
		    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[3, 7, 16, 1], hidden_sizes=[640, 1_696, 2_544, 5_088], groups_width=640	),
		    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1], hidden_sizes=[2_020, 4_040, 11_110, 28_280], groups_width=1_010	),
		}

		a_     :      List[Any] 						=     NameToOurModelFuncMap()
		a_     :      Tuple 						=     NameToFromModelFuncMap()
		# add seer weights logic

		def load_using_classy_vision(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) -> Tuple[nn.Module, Dict]:
				a_     :      Tuple 						=     torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__, model_dir=str(SCREAMING_SNAKE_CASE__	), map_location="cpu"	)
				a_     :      str 						=     model_func()
				# check if we have a head, if yes add it
				a_     :      Union[str, Any] 						=     files["classy_state_dict"]["base_model"]["model"]
				a_     :      Union[str, Any] 						=     model_state_dict["trunk"]
				model.load_state_dict(SCREAMING_SNAKE_CASE__	)
				return model.eval(), model_state_dict["heads"]

		# pretrained
		a_     :      List[Any] 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf()	), )

		a_     :      int 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf()	), )

		a_     :      List[str] 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	), )

		a_     :      List[Any] 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch", lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=27, group_width=1_010, w_a=1_744, w_a=620.83, w_m=2.52	)	)	), )

		# IN1K finetuned
		a_     :      Any 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf()	), )

		a_     :      Optional[int] 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaagf()	), )

		a_     :      Optional[Any] 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	), )

		a_     :      str 						=     partial(
		    SCREAMING_SNAKE_CASE__, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch", lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=27, group_width=1_010, w_a=1_744, w_a=620.83, w_m=2.52	)	)	), )

		if model_name:
				convert_weight_and_push(
				    SCREAMING_SNAKE_CASE__, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    SCREAMING_SNAKE_CASE__, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, )
		return config, expected_shape


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
					        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					SCREAMING_SNAKE_CASE_				= parser.parse_args()

					SCREAMING_SNAKE_CASE_				= args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

SCREAMING_SNAKE_CASE_				= random.Random()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=1.0, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		if rng is None:
				a_     :      Any 						=     global_rng

		a_     :      Dict 						=     []
		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			,     a_			,     a_=7			,     a_=4_0_0			,     a_=2_0_0_0			,     a_=2_0_4_8			,     a_=1_2_8			,     a_=1			,     a_=5_1_2			,     a_=3_0			,     a_=4_4_1_0_0			,     ):
									a_     :      Optional[Any] 						=     parent
									a_     :      Tuple 						=     batch_size
									a_     :      Union[str, Any] 						=     min_seq_length
									a_     :      Dict 						=     max_seq_length
									a_     :      Union[str, Any] 						=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_     :      int 						=     spectrogram_length
									a_     :      Optional[Any] 						=     feature_size
									a_     :      Optional[int] 						=     num_audio_channels
									a_     :      List[str] 						=     hop_length
									a_     :      Optional[int] 						=     chunk_length
									a_     :      List[str] 						=     sampling_rate






							def snake_case_  (   self      ):
									return {
									    "spectrogram_length": self.spectrogram_length,
									    "feature_size": self.feature_size,
									    "num_audio_channels": self.num_audio_channels,
									    "hop_length": self.hop_length,
									    "chunk_length": self.chunk_length,
									    "sampling_rate": self.sampling_rate,
									}






							def snake_case_  (   self			,     a_=False			,     a_=False      ):
									def _flatten(a_      ):
											return list(itertools.chain(*a_      )      )

									if equal_length:
											a_     :      Tuple 						=     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
									else:
											# make sure that inputs increase in size
											a_     :      Optional[Any] 						=     [
											    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_     :      Tuple 						=     [np.asarray(a_      ) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					TvltFeatureExtractor






							def snake_case_  (   self      ):
									a_     :      Dict 						=     TvltFeatureExtractionTester(self      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )
									self.assertTrue(hasattr(a_			,     "spectrogram_length"      )      )
									self.assertTrue(hasattr(a_			,     "feature_size"      )      )
									self.assertTrue(hasattr(a_			,     "num_audio_channels"      )      )
									self.assertTrue(hasattr(a_			,     "hop_length"      )      )
									self.assertTrue(hasattr(a_			,     "chunk_length"      )      )
									self.assertTrue(hasattr(a_			,     "sampling_rate"      )      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      List[Any] 						=     feat_extract_first.save_pretrained(a_      )[0]
											check_json_file_has_correct_format(a_      )
											a_     :      Tuple 						=     self.feature_extraction_class.from_pretrained(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      List[str] 						=     feat_extract_second.to_dict()
									a_     :      List[Any] 						=     dict_first.pop("mel_filters"      )
									a_     :      str 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Dict 						=     os.path.join(a_			,     "feat_extract.json"      )
											feat_extract_first.to_json_file(a_      )
											a_     :      str 						=     self.feature_extraction_class.from_json_file(a_      )

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      str 						=     feat_extract_second.to_dict()
									a_     :      int 						=     dict_first.pop("mel_filters"      )
									a_     :      List[Any] 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									# Initialize feature_extractor
									a_     :      str 						=     self.feature_extraction_class(**self.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_0_0			,     1_4_0_0			,     2_0_0      )]
									a_     :      Union[str, Any] 						=     [np.asarray(a_      ) for speech_input in speech_inputs]

									# Test not batched input
									a_     :      Dict 						=     feature_extractor(np_speech_inputs[0]			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test batched
									a_     :      int 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test audio masking
									a_     :      Optional[int] 						=     feature_extractor(
									    a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0			,     mask_audio=a_      ).audio_values

									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

									# Test 2-D numpy arrays are batched.
									a_     :      List[str] 						=     [floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a_     :      Union[str, Any] 						=     np.asarray(a_      )
									a_     :      Dict 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).audio_values
									self.assertTrue(encoded_audios.ndim == 4      )
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation"      )
									# automatic decoding with librispeech
									a_     :      List[str] 						=     ds.sort("id"      ).select(range(a_      )      )[:num_samples]["audio"]

									return [x["array"] for x in speech_samples]






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self._load_datasamples(1      )
									a_     :      Any 						=     TvltFeatureExtractor()
									a_     :      Union[str, Any] 						=     feature_extractor(a_			,     return_tensors="pt"      ).audio_values

									self.assertEquals(audio_values.shape			,     (1, 1, 1_9_2, 1_2_8)      )

									a_     :      Dict 						=     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     a_			,     atol=1e-4      )      )