"""simple docstring"""

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()
UpperCAmelCase       =  logging.get_logger()







@dataclass
class      UpperCAmelCase_       :
							snake_case__        =      42
							snake_case__        =      field(default_factory=__snake_case)
							snake_case__        =      field(default_factory=__snake_case)






							def       _UpperCamelCase			(		self :      Optional[Any]     ,						__UpperCamelCase :      Tuple     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      List[Any]  )       ->       str:
												_UpperCamelCase      						=	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 :      int     ,						__UpperCamelCase :      Optional[int]  )       ->       List[str]:




												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       _UpperCamelCase			(		self :      List[str]  )       ->       Union[str, Any]:
												return list(filter(lambda __UpperCamelCase  : len(list(x.state_dict().keys()  )  ) > 0     ,						self.traced  )  )







@dataclass
class      UpperCAmelCase_       :
							snake_case__        =      42
							snake_case__        =      42
							snake_case__        =      1
							snake_case__        =      field(default_factory=__snake_case)
							snake_case__        =      field(default_factory=__snake_case)
							snake_case__        =      True






							def __call__(		self :      Optional[Any]     ,						__UpperCamelCase :      Dict  )       ->       str:
												_UpperCamelCase      						=	Tracker(self.dest  )(A_  ).parametrized
												_UpperCamelCase      						=	Tracker(self.src  )(A_  ).parametrized

												_UpperCamelCase      						=	list(filter(lambda __UpperCamelCase  : type(A_  ) not in self.src_skip     ,						A_  )  )
												_UpperCamelCase      						=	list(filter(lambda __UpperCamelCase  : 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      UpperCAmelCase_       (						nn.Module):






							def __init__(		self :      Union[str, Any]     ,						__UpperCamelCase :      Any  )       ->       str:
												super().__init__()

												_UpperCamelCase      						=	[]
												# - 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}'''
																	_UpperCamelCase      						=	len(A_  ) + 1
																	feature_blocks.append((F'''res{block_index}''', v)  )

												_UpperCamelCase      						=	nn.ModuleDict(A_  )






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      Union[str, Any]  )       ->       List[str]:
												return get_trunk_forward_outputs(
												    A_     ,						out_feat_keys=A_     ,						feature_blocks=self._feature_blocks     ,						)







class      UpperCAmelCase_       (						__snake_case):






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      str  )       ->       Dict:
												_UpperCamelCase      						=	x.split('''-'''  )
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]  )






							def __getitem__(		self :      List[Any]     ,						__UpperCamelCase :      Union[str, Any]  )       ->       Optional[Any]:




												if x not in self:
																	_UpperCamelCase      						=	self.convert_name_to_timm(A_  )
																	_UpperCamelCase      						=	partial(lambda: (timm.create_model(A_     ,						pretrained=A_  ).eval(), None)  )

												else:
																	_UpperCamelCase      						=	super().__getitem__(A_  )

												return val







class      UpperCAmelCase_       (						__snake_case):






							def __getitem__(		self :      List[str]     ,						__UpperCamelCase :      Dict  )       ->       Dict:




												if "seer" in x and "in1k" not in x:
																	_UpperCamelCase      						=	RegNetModel
												else:
																	_UpperCamelCase      						=	RegNetForImageClassification
												return val






def      lowercase		(						a__	:		Tuple ,  a__	:		Any ,  a__	:		List[Any]     )						->   List[str]:
					for from_key, to_key in keys:
										_UpperCamelCase      						=	from_state_dict[from_key].clone()
										print(F'''Copied key={from_key} to={to_key}'''     )
					return to_state_dict






def      lowercase		(						a__	:		Dict ,  a__	:		Optional[Any] ,  a__	:		List[str] ,  a__	:		int ,  a__	:		str ,  a__	:		str = True ,  )						->   int:
					print(F'''Converting {name}...'''     )
					with torch.no_grad():
										_UpperCamelCase      						=	from_model_func()
										_UpperCamelCase      						=	our_model_func(_lowerCAmelCase     ).eval()
										_UpperCamelCase      						=	ModuleTransfer(src=_lowerCAmelCase ,  dest=_lowerCAmelCase ,  raise_if_mismatch=_lowerCAmelCase     )
										_UpperCamelCase      						=	torch.randn((1, 3, 224, 224)     )
										module_transfer(_lowerCAmelCase     )

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

					_UpperCamelCase      						=	our_model(_lowerCAmelCase ,  output_hidden_states=_lowerCAmelCase     )
					_UpperCamelCase      						=	(
					    our_outputs.logits if isinstance(_lowerCAmelCase ,  _lowerCAmelCase     ) else our_outputs.last_hidden_state
					)

					_UpperCamelCase      						=	from_model(_lowerCAmelCase     )
					_UpperCamelCase      						=	from_output[-1] if type(_lowerCAmelCase     ) 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:
										_UpperCamelCase      						=	our_outputs.hidden_states[-1]

					assert torch.allclose(_lowerCAmelCase ,  _lowerCAmelCase     ), "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=_lowerCAmelCase ,  )

										_UpperCamelCase      						=	224 if "seer" not in name else 384
										# we can use the convnext one
										_UpperCamelCase      						=	AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ,  size=_lowerCAmelCase     )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / name ,  commit_message='''Add image processor''' ,  use_temp_dir=_lowerCAmelCase ,  )

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






def      lowercase		(						a__	:		Union[str, Any] ,  a__	:		Optional[Any] = None ,  a__	:		int = True     )						->   List[str]:
					_UpperCamelCase      						=	"imagenet-1k-id2label.json"
					_UpperCamelCase      						=	1000
					_UpperCamelCase      						=	(1, num_labels)

					_UpperCamelCase      						=	"huggingface/label-files"
					_UpperCamelCase      						=	num_labels
					_UpperCamelCase      						=	json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,  _lowerCAmelCase ,  repo_type='''dataset'''     )     ) ,  '''r'''     )     )
					_UpperCamelCase      						=	{int(_lowerCAmelCase     ): v for k, v in idalabel.items()}

					_UpperCamelCase      						=	idalabel
					_UpperCamelCase      						=	{v: k for k, v in idalabel.items()}

					_UpperCamelCase      						=	partial(_lowerCAmelCase ,  num_labels=_lowerCAmelCase ,  idalabel=_lowerCAmelCase ,  labelaid=_lowerCAmelCase     )

					_UpperCamelCase      						=	{
					    "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, 1008] ,  groups_width=48 ,  layer_type='''x'''     ),
					    "regnet-x-040": ImageNetPreTrainedConfig(
					        depths=[2, 5, 14, 2] ,  hidden_sizes=[80, 240, 560, 1360] ,  groups_width=40 ,  layer_type='''x'''     ),
					    "regnet-x-064": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1] ,  hidden_sizes=[168, 392, 784, 1624] ,  groups_width=56 ,  layer_type='''x'''     ),
					    "regnet-x-080": ImageNetPreTrainedConfig(
					        depths=[2, 5, 15, 1] ,  hidden_sizes=[80, 240, 720, 1920] ,  groups_width=120 ,  layer_type='''x'''     ),
					    "regnet-x-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1] ,  hidden_sizes=[224, 448, 896, 2240] ,  groups_width=112 ,  layer_type='''x'''     ),
					    "regnet-x-160": ImageNetPreTrainedConfig(
					        depths=[2, 6, 13, 1] ,  hidden_sizes=[256, 512, 896, 2048] ,  groups_width=128 ,  layer_type='''x'''     ),
					    "regnet-x-320": ImageNetPreTrainedConfig(
					        depths=[2, 7, 13, 1] ,  hidden_sizes=[336, 672, 1344, 2520] ,  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, 1512] ,  groups_width=24     ),
					    "regnet-y-040": ImageNetPreTrainedConfig(
					        depths=[2, 6, 12, 2] ,  hidden_sizes=[128, 192, 512, 1088] ,  groups_width=64     ),
					    "regnet-y-064": ImageNetPreTrainedConfig(
					        depths=[2, 7, 14, 2] ,  hidden_sizes=[144, 288, 576, 1296] ,  groups_width=72     ),
					    "regnet-y-080": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1] ,  hidden_sizes=[168, 448, 896, 2016] ,  groups_width=56     ),
					    "regnet-y-120": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1] ,  hidden_sizes=[224, 448, 896, 2240] ,  groups_width=112     ),
					    "regnet-y-160": ImageNetPreTrainedConfig(
					        depths=[2, 4, 11, 1] ,  hidden_sizes=[224, 448, 1232, 3024] ,  groups_width=112     ),
					    "regnet-y-320": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1] ,  hidden_sizes=[232, 696, 1392, 3712] ,  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, 1392, 3712] ,  groups_width=232     ),
					    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1] ,  hidden_sizes=[328, 984, 1968, 4920] ,  groups_width=328     ),
					    "regnet-y-1280-seer": RegNetConfig(
					        depths=[2, 7, 17, 1] ,  hidden_sizes=[528, 1056, 2904, 7392] ,  groups_width=264     ),
					    "regnet-y-2560-seer": RegNetConfig(
					        depths=[3, 7, 16, 1] ,  hidden_sizes=[640, 1696, 2544, 5088] ,  groups_width=640     ),
					    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,  hidden_sizes=[2020, 4040, 11110, 28280] ,  groups_width=1010     ),
					    # finetuned on imagenet
					    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1] ,  hidden_sizes=[232, 696, 1392, 3712] ,  groups_width=232     ),
					    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1] ,  hidden_sizes=[328, 984, 1968, 4920] ,  groups_width=328     ),
					    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,  hidden_sizes=[528, 1056, 2904, 7392] ,  groups_width=264     ),
					    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[3, 7, 16, 1] ,  hidden_sizes=[640, 1696, 2544, 5088] ,  groups_width=640     ),
					    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,  hidden_sizes=[2020, 4040, 11110, 28280] ,  groups_width=1010     ),
					}

					_UpperCamelCase      						=	NameToOurModelFuncMap()
					_UpperCamelCase      						=	NameToFromModelFuncMap()
					# add seer weights logic

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

					# pretrained
					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''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()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' ,  lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27 ,  group_width=1010 ,  w_a=1744 ,  w_a=620.83 ,  w_m=2.52     )     )     ) ,  )

					# IN1K finetuned
					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch''' ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' ,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     ) ,  )

					_UpperCamelCase      						=	partial(
					    _lowerCAmelCase ,  '''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=1010 ,  w_a=1744 ,  w_a=620.83 ,  w_m=2.52     )     )     ) ,  )

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


if __name__ == "__main__":
		UpperCAmelCase       =  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.""",
		)

		UpperCAmelCase       =  parser.parse_args()

		UpperCAmelCase       =  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)







import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCamelCase       :     str   =     random.Random()

if is_torch_available():
    import torch



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase=1.0							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None     )						-> Optional[Any]:
 if rng is None:
  UpperCamelCase	:					Optional[int]       	=   global_rng

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

 return values



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=400       ,							A_=2000       ,							A_=1       ,							A_=0.0       ,							A_=1_6000       ,							A_=True       ,							A_=True       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''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        __UpperCamelCase( self       ,							A_=False       ,							A_=False   ):
       '''simple docstring'''




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

       if equal_length:
        UpperCamelCase	:					List[str]       	=   floats_list((self.batch_size, self.max_seq_length)   )
       else:
        # make sure that inputs increase in size
        UpperCamelCase	:					Dict       	=   [
            _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:
        UpperCamelCase	:					Union[str, Any]       	=   [np.asarray(A_   ) for x in speech_inputs]

       return speech_inputs



@require_torch
@require_torchaudio
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   ASTFeatureExtractor





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractionTester(self   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase	:					Tuple       	=   [floats_list((1, x)   )[0] for x in range(800       ,							1400       ,							200   )]
       UpperCamelCase	:					Dict       	=   [np.asarray(A_   ) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase	:					Dict       	=   feat_extract(speech_inputs[0]       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract(np_speech_inputs[0]       ,							return_tensors="np"   ).input_values
       self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test batched
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test 2-D numpy arrays are batched.
       UpperCamelCase	:					Dict       	=   [floats_list((1, x)   )[0] for x in (800, 800, 800)]
       UpperCamelCase	:					int       	=   np.asarray(A_   )
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					List[str]       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import torch

       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       UpperCamelCase	:					int       	=   np.random.rand(100   ).astype(np.floataa   )
       UpperCamelCase	:					str       	=   np_speech_inputs.tolist()

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       from datasets import load_dataset

       UpperCamelCase	:					Dict       	=   load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,							"clean"       ,							split="validation"   )
       # automatic decoding with librispeech
       UpperCamelCase	:					Any       	=   ds.sort("id"   ).select(range(A_   )   )[:num_samples]["audio"]

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





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   torch.tensor(
           [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
            -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
            -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
            -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]   )
       # fmt: on

       UpperCamelCase	:					List[Any]       	=   self._load_datasamples(1   )
       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractor()
       UpperCamelCase	:					str       	=   feature_extractor(A_       ,							return_tensors="pt"   ).input_values
       self.assertEquals(input_values.shape       ,							(1, 1024, 128)   )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,							A_       ,							atol=1e-4   )   )
