
"""simple docstring"""





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_					:						List[str]				= logging.get_logger(__name__)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SwinConfig.from_pretrained(
					    "microsoft/swin-tiny-patch4-window7-224"				,   out_features=["stage1", "stage2", "stage3", "stage4"]				)
					A_				 =						MaskFormerConfig(backbone_config=__a				)

					A_				 =						"huggingface/label-files"
					if "ade20k-full" in model_name:
										# this should be ok
										A_				 =						847
										A_				 =						"maskformer-ade20k-full-id2label.json"
					elif "ade" in model_name:
										# this should be ok
										A_				 =						150
										A_				 =						"ade20k-id2label.json"
					elif "coco-stuff" in model_name:
										# this should be ok
										A_				 =						171
										A_				 =						"maskformer-coco-stuff-id2label.json"
					elif "coco" in model_name:
										# TODO
										A_				 =						133
										A_				 =						"coco-panoptic-id2label.json"
					elif "cityscapes" in model_name:
										# this should be ok
										A_				 =						19
										A_				 =						"cityscapes-id2label.json"
					elif "vistas" in model_name:
										# this should be ok
										A_				 =						65
										A_				 =						"mapillary-vistas-id2label.json"

					A_				 =						json.load(open(hf_hub_download(__a				,   __a				,   repo_type="dataset"				)				,   "r"				)				)
					A_				 =						{int(__a				): v for k, v in idalabel.items()}

					return config
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						[]
					# stem
					# fmt: off
					rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")				)
					rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")				)
					rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")				)
					rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")				)
					# stages
					for i in range(len(config.backbone_config.depths				)				):
										for j in range(config.backbone_config.depths[i]				):
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')				)
															rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')				)

										if i < 3:
															rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')				)
															rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')				)
															rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')				)
										rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')				)
										rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')				)

					# FPN
					rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")				)
					rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")				)
					rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")				)
					for source_index, target_index in zip(range(3				,   0				,   -1				)				,   range(0				,   3				)				):
										rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')				)
										rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')				)
										rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')				)
										rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')				)
										rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')				)
										rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')				)
					rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")				)
					rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")				)

					# Transformer decoder
					for idx in range(config.decoder_config.decoder_layers				):
										# self-attention out projection
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')				)
										# cross-attention out projection
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')				)
										# MLP 1
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias')				)
										# MLP 2
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias')				)
										# layernorm 1 (self-attention layernorm)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')				)
										# layernorm 2 (cross-attention layernorm)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')				)
										# layernorm 3 (final layernorm)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')				)

					rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")				)
					rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")				)

					# heads on top
					rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")				)

					rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")				)
					rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")				)

					rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")				)
					rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")				)

					for i in range(3				):
										rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight')				)
										rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias')				)
					# fmt: on

					return rename_keys
def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						dct.pop(__a				)
					A_				 =						val
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
					for i in range(len(backbone_config.depths				)				):
										A_				 =						num_features[i]
										for j in range(backbone_config.depths[i]				):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															A_				 =						state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'				)
															A_				 =						state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'				)
															# next, add query, keys and values (in that order) to the state dict
															A_				 =						in_proj_weight[:dim, :]
															A_				 =						in_proj_bias[: dim]
															A_				 =						in_proj_weight[
															    dim : dim * 2, :
															]
															A_				 =						in_proj_bias[
															    dim : dim * 2
															]
															A_				 =						in_proj_weight[
															    -dim :, :
															]
															A_				 =						in_proj_bias[-dim :]
															# fmt: on
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						config.decoder_config.hidden_size
					for idx in range(config.decoder_config.decoder_layers				):
										# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
										A_				 =						state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'				)
										A_				 =						state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'				)
										# next, add query, keys and values (in that order) to the state dict
										A_				 =						in_proj_weight[: hidden_size, :]
										A_				 =						in_proj_bias[:config.hidden_size]
										A_				 =						in_proj_weight[hidden_size : hidden_size * 2, :]
										A_				 =						in_proj_bias[hidden_size : hidden_size * 2]
										A_				 =						in_proj_weight[-hidden_size :, :]
										A_				 =						in_proj_bias[-hidden_size :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										A_				 =						state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'				)
										A_				 =						state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'				)
										# next, add query, keys and values (in that order) to the state dict
										A_				 =						in_proj_weight[: hidden_size, :]
										A_				 =						in_proj_bias[:config.hidden_size]
										A_				 =						in_proj_weight[hidden_size : hidden_size * 2, :]
										A_				 =						in_proj_bias[hidden_size : hidden_size * 2]
										A_				 =						in_proj_weight[-hidden_size :, :]
										A_				 =						in_proj_bias[-hidden_size :]
					# fmt: on
def 							A_     ():

					'''simple docstring'''





					A_				 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
					A_				 =						Image.open(requests.get(__a				,   stream=__a				).raw				)
					return im
@torch.no_grad()
def 							A_     (__a				,   __a				,   __a				,   __a = False				):

					'''simple docstring'''





					A_				 =						get_maskformer_config(__a				)

					# load original state_dict
					with open(__a				,   "rb"				) as f:
										A_				 =						pickle.load(__a				)
					A_				 =						data["model"]

					# for name, param in state_dict.items():
					#     print(name, param.shape)

					# rename keys
					A_				 =						create_rename_keys(__a				)
					for src, dest in rename_keys:
										rename_key(__a				,   __a				,   __a				)
					read_in_swin_q_k_v(__a				,   config.backbone_config				)
					read_in_decoder_q_k_v(__a				,   __a				)

					# update to torch tensors
					for key, value in state_dict.items():
										A_				 =						torch.from_numpy(__a				)

					# load 🤗 model
					A_				 =						MaskFormerForInstanceSegmentation(__a				)
					model.eval()

					for name, param in model.named_parameters():
										print(__a				,   param.shape				)

					A_ ,    A_				 =						model.load_state_dict(__a				,   strict=__a				)
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(__a				) == 0, f'Unexpected keys: {unexpected_keys}'

					# verify results
					A_				 =						prepare_img()
					if "vistas" in model_name:
										A_				 =						65
					elif "cityscapes" in model_name:
										A_				 =						6_5535
					else:
										A_				 =						255
					A_				 =						True if "ade" in model_name else False
					A_				 =						MaskFormerImageProcessor(ignore_index=__a				,   reduce_labels=__a				)

					A_				 =						image_processor(__a				,   return_tensors="pt"				)

					A_				 =						model(**__a				)

					print("Logits:"				,   outputs.class_queries_logits[0, :3, :3]				)

					if model_name == "maskformer-swin-tiny-ade":
										A_				 =						torch.tensor(
										    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]				)
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,   __a				,   atol=1e-4				)
					print("Looks ok!"				)

					if pytorch_dump_folder_path is not None:
										print(f'Saving model and image processor to {pytorch_dump_folder_path}'				)
										Path(__a				).mkdir(exist_ok=__a				)
										model.save_pretrained(__a				)
										image_processor.save_pretrained(__a				)

					if push_to_hub:
										print("Pushing model and image processor to the hub..."				)
										model.push_to_hub(f'nielsr/{model_name}'				)
										image_processor.push_to_hub(f'nielsr/{model_name}'				)


if __name__ == "__main__":
		UpperCamelCase_					:						List[Any]				= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''maskformer-swin-tiny-ade''',
		    type=str,
		    help=('''Name of the MaskFormer model you\'d like to convert''',),
		)
		parser.add_argument(
		    '''--checkpoint_path''',
		    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
		    type=str,
		    help='''Path to the original state dict (.pth file).''',
		)
		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 or not to push the converted model to the 🤗 hub.'''
		)

		UpperCamelCase_					:						List[Any]				= parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, 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

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

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

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




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







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

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





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						feat_extract_first.save_pretrained(_snake_case	)[0]
													check_json_file_has_correct_format(_snake_case	)
													A_				 =						self.feature_extraction_class.from_pretrained(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

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

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




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







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).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_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).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_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







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

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






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

								self.assertEquals(audio_values.shape       ,   (1, 1, 192, 128)	)

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)




