




'''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()
_lowerCAmelCase      	=					logging.get_logger(__name__)

def 			__lowerCAmelCase					(    snake_case__						):
			__UpperCamelCase      :			str									=   SwinConfig.from_pretrained(
			    "microsoft/swin-tiny-patch4-window7-224"					,      out_features=["stage1", "stage2", "stage3", "stage4"]						)
			__UpperCamelCase      :			Union[str, Any]									=   MaskFormerConfig(backbone_config=UpperCamelCase__						)

			__UpperCamelCase      :			Optional[Any]									=   "huggingface/label-files"
			if "ade20k-full" in model_name:
						# this should be ok
						__UpperCamelCase      :			Optional[Any]									=   847
						__UpperCamelCase      :			Optional[Any]									=   "maskformer-ade20k-full-id2label.json"
			elif "ade" in model_name:
						# this should be ok
						__UpperCamelCase      :			Optional[int]									=   150
						__UpperCamelCase      :			Dict									=   "ade20k-id2label.json"
			elif "coco-stuff" in model_name:
						# this should be ok
						__UpperCamelCase      :			List[Any]									=   171
						__UpperCamelCase      :			int									=   "maskformer-coco-stuff-id2label.json"
			elif "coco" in model_name:
						# TODO
						__UpperCamelCase      :			Any									=   133
						__UpperCamelCase      :			str									=   "coco-panoptic-id2label.json"
			elif "cityscapes" in model_name:
						# this should be ok
						__UpperCamelCase      :			int									=   19
						__UpperCamelCase      :			int									=   "cityscapes-id2label.json"
			elif "vistas" in model_name:
						# this should be ok
						__UpperCamelCase      :			Tuple									=   65
						__UpperCamelCase      :			Tuple									=   "mapillary-vistas-id2label.json"

			__UpperCamelCase      :			Optional[Any]									=   json.load(open(hf_hub_download(UpperCamelCase__					,      UpperCamelCase__					,      repo_type="dataset"						)					,      "r"						)						)
			__UpperCamelCase      :			Union[str, Any]									=   {int(UpperCamelCase__						): v for k, v in idalabel.items()}

			return config

def 			__lowerCAmelCase					(    snake_case__						):
			__UpperCamelCase      :			List[str]									=   []
			# 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 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__						):
			__UpperCamelCase      :			List[Any]									=   dct.pop(UpperCamelCase__						)
			__UpperCamelCase      :			Any									=   val

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__						):
			__UpperCamelCase      :			List[Any]									=   [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
			for i in range(len(backbone_config.depths						)						):
						__UpperCamelCase      :			Union[str, Any]									=   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)
									__UpperCamelCase      :			Optional[Any]									=   state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"						)
									__UpperCamelCase      :			int									=   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
									__UpperCamelCase      :			Tuple									=   in_proj_weight[:dim, :]
									__UpperCamelCase      :			List[str]									=   in_proj_bias[: dim]
									__UpperCamelCase      :			Any									=   in_proj_weight[
									    dim : dim * 2, :
									]
									__UpperCamelCase      :			List[str]									=   in_proj_bias[
									    dim : dim * 2
									]
									__UpperCamelCase      :			int									=   in_proj_weight[
									    -dim :, :
									]
									__UpperCamelCase      :			int									=   in_proj_bias[-dim :]
									# fmt: on

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__						):
			__UpperCamelCase      :			Union[str, Any]									=   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)
						__UpperCamelCase      :			Tuple									=   state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"						)
						__UpperCamelCase      :			List[Any]									=   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
						__UpperCamelCase      :			List[Any]									=   in_proj_weight[: hidden_size, :]
						__UpperCamelCase      :			Dict									=   in_proj_bias[:config.hidden_size]
						__UpperCamelCase      :			List[Any]									=   in_proj_weight[hidden_size : hidden_size * 2, :]
						__UpperCamelCase      :			Any									=   in_proj_bias[hidden_size : hidden_size * 2]
						__UpperCamelCase      :			Tuple									=   in_proj_weight[-hidden_size :, :]
						__UpperCamelCase      :			Optional[int]									=   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)
						__UpperCamelCase      :			str									=   state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"						)
						__UpperCamelCase      :			Dict									=   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
						__UpperCamelCase      :			Dict									=   in_proj_weight[: hidden_size, :]
						__UpperCamelCase      :			List[str]									=   in_proj_bias[:config.hidden_size]
						__UpperCamelCase      :			Optional[int]									=   in_proj_weight[hidden_size : hidden_size * 2, :]
						__UpperCamelCase      :			str									=   in_proj_bias[hidden_size : hidden_size * 2]
						__UpperCamelCase      :			List[str]									=   in_proj_weight[-hidden_size :, :]
						__UpperCamelCase      :			Union[str, Any]									=   in_proj_bias[-hidden_size :]
			# fmt: on

def 			__lowerCAmelCase					(    ):
			__UpperCamelCase      :			Optional[int]									=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			__UpperCamelCase      :			Union[str, Any]									=   Image.open(requests.get(UpperCamelCase__					,      stream=UpperCamelCase__						).raw						)
			return im

@torch.no_grad()
def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__					,      snake_case__ = False						):
			__UpperCamelCase      :			List[Any]									=   get_maskformer_config(UpperCamelCase__						)

			# load original state_dict
			with open(UpperCamelCase__					,      "rb"						) as f:
						__UpperCamelCase      :			Any									=   pickle.load(UpperCamelCase__						)
			__UpperCamelCase      :			List[str]									=   data["model"]

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

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

			# update to torch tensors
			for key, value in state_dict.items():
						__UpperCamelCase      :			List[str]									=   torch.from_numpy(UpperCamelCase__						)

			# load 🤗 model
			__UpperCamelCase      :			Dict									=   MaskFormerForInstanceSegmentation(UpperCamelCase__						)
			model.eval()

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

			__UpperCamelCase		,					__UpperCamelCase      :			Union[str, Any]									=   model.load_state_dict(UpperCamelCase__					,      strict=UpperCamelCase__						)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(UpperCamelCase__						) == 0, F"Unexpected keys: {unexpected_keys}"

			# verify results
			__UpperCamelCase      :			List[Any]									=   prepare_img()
			if "vistas" in model_name:
						__UpperCamelCase      :			Optional[int]									=   65
			elif "cityscapes" in model_name:
						__UpperCamelCase      :			List[str]									=   65_535
			else:
						__UpperCamelCase      :			List[str]									=   255
			__UpperCamelCase      :			List[str]									=   True if "ade" in model_name else False
			__UpperCamelCase      :			List[Any]									=   MaskFormerImageProcessor(ignore_index=UpperCamelCase__					,      reduce_labels=UpperCamelCase__						)

			__UpperCamelCase      :			Optional[Any]									=   image_processor(UpperCamelCase__					,      return_tensors="pt"						)

			__UpperCamelCase      :			Optional[Any]									=   model(**UpperCamelCase__						)

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

			if model_name == "maskformer-swin-tiny-ade":
						__UpperCamelCase      :			Union[str, Any]									=   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]					,      UpperCamelCase__					,      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(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
						model.save_pretrained(UpperCamelCase__						)
						image_processor.save_pretrained(UpperCamelCase__						)

			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__":
				_lowerCAmelCase      	=					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.'''
				)

				_lowerCAmelCase      	=					parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)










import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


__A				:    str       =   random.Random()





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=1.0			,			UpperCamelCase__=None			,			UpperCamelCase__=None )  ->				Tuple:







			'''simple docstring'''

			if rng is None:
						UpperCAmelCase   		=				global_rng

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

			return values

@require_torch
class 	A_       (unittest.TestCase			):
	def __init__(    self							,					_A							,					_A=7							,					_A=4_0_0							,					_A=2_0_0_0							,					_A=1							,					_A=0.0							,					_A=1_6_0_0_0							,					_A=True							,					_A=8_0							,					_A=1_6							,					_A=6_4							,					_A="hann_window"							,					_A=8_0							,					_A=7_6_0_0							,					_A=1E-10							,					_A=True							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				min_seq_length
				UpperCAmelCase   		=				max_seq_length
				UpperCAmelCase   		=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCAmelCase   		=				feature_size
				UpperCAmelCase   		=				padding_value
				UpperCAmelCase   		=				sampling_rate
				UpperCAmelCase   		=				do_normalize
				UpperCAmelCase   		=				num_mel_bins
				UpperCAmelCase   		=				hop_length
				UpperCAmelCase   		=				win_length
				UpperCAmelCase   		=				win_function
				UpperCAmelCase   		=				fmin
				UpperCAmelCase   		=				fmax
				UpperCAmelCase   		=				mel_floor
				UpperCAmelCase   		=				return_attention_mask
	def      _lowercase				(    self		):






				'''simple docstring'''







				return {
				    "feature_size": self.feature_size,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "do_normalize": self.do_normalize,
				    "num_mel_bins": self.num_mel_bins,
				    "hop_length": self.hop_length,
				    "win_length": self.win_length,
				    "win_function": self.win_function,
				    "fmin": self.fmin,
				    "fmax": self.fmax,
				    "mel_floor": self.mel_floor,
				    "return_attention_mask": self.return_attention_mask,
				}
	def      _lowercase				(    self							,					_A=False							,					_A=False		):






				'''simple docstring'''







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

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

				return speech_inputs







	def      _lowercase				(    self							,					_A=False							,					_A=False		):






				'''simple docstring'''







				if equal_length:
							UpperCAmelCase   		=				[floats_list((self.max_seq_length, self.num_mel_bins)		) for _ in range(self.batch_size		)]
				else:
							# make sure that inputs increase in size
							UpperCAmelCase   		=				[
							    floats_list((x, self.num_mel_bins)		)
							    for x in range(self.min_seq_length							,					self.max_seq_length							,					self.seq_length_diff		)
							]

				if numpify:
							UpperCAmelCase   		=				[np.asarray(_A		) for x in speech_inputs]

				return speech_inputs

@require_torch
class 	A_       (a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			SpeechTaFeatureExtractor
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				SpeechTaFeatureExtractionTester(self		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				self.assertTrue(np.all(np.mean(_A							,					axis=0		) < 1E-3		)		)
				self.assertTrue(np.all(np.abs(np.var(_A							,					axis=0		) - 1		) < 1E-3		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				# create three inputs of length 800, 1000, and 1200
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]
				UpperCAmelCase   		=				[np.asarray(_A		) for speech_input in speech_inputs]

				# Test not batched input
				UpperCAmelCase   		=				feat_extract(speech_inputs[0]							,					return_tensors='''np'''		).input_values
				UpperCAmelCase   		=				feat_extract(np_speech_inputs[0]							,					return_tensors='''np'''		).input_values
				self.assertTrue(np.allclose(_A							,					_A							,					atol=1E-3		)		)

				# Test batched
				UpperCAmelCase   		=				feat_extract(_A							,					return_tensors='''np'''		).input_values
				UpperCAmelCase   		=				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		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]

				UpperCAmelCase   		=				['''longest''', '''max_length''', '''do_not_pad''']
				UpperCAmelCase   		=				[None, 1_6_0_0, None]
				for max_length, padding in zip(_A							,					_A		):
							UpperCAmelCase   		=				feat_extract(_A							,					padding=_A							,					max_length=_A							,					return_tensors='''np'''		)
							UpperCAmelCase   		=				processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:8_0_0]		)
							self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6		)
							self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]		)
							self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6		)
							self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase   		=				range(8_0_0							,					1_4_0_0							,					2_0_0		)
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in lengths]

				UpperCAmelCase   		=				['''longest''', '''max_length''', '''do_not_pad''']
				UpperCAmelCase   		=				[None, 1_6_0_0, None]

				for max_length, padding in zip(_A							,					_A		):
							UpperCAmelCase   		=				feat_extract(_A							,					max_length=_A							,					padding=_A		)
							UpperCAmelCase   		=				processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:8_0_0]		)
							self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]		)
							self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]
				UpperCAmelCase   		=				feat_extract(
				    _A							,					truncation=_A							,					max_length=1_0_0_0							,					padding='''max_length'''							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_0_0]		)
				self._check_zero_mean_unit_variance(input_values[1]		)
				self._check_zero_mean_unit_variance(input_values[2]		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]
				UpperCAmelCase   		=				feat_extract(
				    _A							,					truncation=_A							,					max_length=1_0_0_0							,					padding='''longest'''							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_0_0]		)
				self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]		)
				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, 1_0_0_0)		)

				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]
				UpperCAmelCase   		=				feat_extract(
				    _A							,					truncation=_A							,					max_length=2_0_0_0							,					padding='''longest'''							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_0_0]		)
				self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]		)
				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, 1_2_0_0)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase   		=				np.random.rand(1_0_0		).astype(np.floataa		)
				UpperCAmelCase   		=				np_speech_inputs.tolist()

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






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				# create three inputs of length 800, 1000, and 1200
				UpperCAmelCase   		=				[floats_list((1, x)		)[0] for x in range(8_0_0							,					1_4_0_0							,					2_0_0		)]
				UpperCAmelCase   		=				[np.asarray(_A		) for speech_input in speech_inputs]

				# Test feature size
				UpperCAmelCase   		=				feature_extractor(audio_target=_A							,					padding=_A							,					return_tensors='''np'''		).input_values
				self.assertTrue(input_values.ndim == 3		)
				self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins		)

				# Test not batched input
				UpperCAmelCase   		=				feature_extractor(speech_inputs[0]							,					return_tensors='''np'''		).input_values
				UpperCAmelCase   		=				feature_extractor(np_speech_inputs[0]							,					return_tensors='''np'''		).input_values
				self.assertTrue(np.allclose(_A							,					_A							,					atol=1E-3		)		)

				# Test batched
				UpperCAmelCase   		=				feature_extractor(_A							,					return_tensors='''np'''		).input_values
				UpperCAmelCase   		=				feature_extractor(_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   		=				[floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
				UpperCAmelCase   		=				np.asarray(_A		)
				UpperCAmelCase   		=				feature_extractor(_A							,					return_tensors='''np'''		).input_values
				UpperCAmelCase   		=				feature_extractor(_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		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target()
				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase   		=				feat_extract.model_input_names[0]

				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}		)

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

				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A		)
				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}							,					tensor_type='''np'''		)

				UpperCAmelCase   		=				processed_features[input_name]

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

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A		)
				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase   		=				feat_extract.model_input_names[0]

				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}							,					tensor_type='''pt'''		)

				UpperCAmelCase   		=				processed_features[input_name]

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

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)
	@require_torch
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target()
				UpperCAmelCase   		=				feat_extract.model_input_names[0]

				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}		)

				UpperCAmelCase   		=				feat_extract.num_mel_bins  # hack!

				UpperCAmelCase   		=				feat_extract.pad(_A							,					padding='''longest'''							,					return_tensors='''np'''		)[input_name]
				UpperCAmelCase   		=				feat_extract.pad(_A							,					padding='''longest'''							,					return_tensors='''pt'''		)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feat_extract_dict
				UpperCAmelCase   		=				True
				UpperCAmelCase   		=				self.feature_extraction_class(**_A		)
				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target()
				UpperCAmelCase   		=				[len(_A		) for x in speech_inputs]
				UpperCAmelCase   		=				feat_extract.model_input_names[0]

				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}		)

				UpperCAmelCase   		=				feat_extract.num_mel_bins  # hack!

				UpperCAmelCase   		=				feat_extract.pad(_A							,					padding='''longest'''							,					return_tensors='''np'''		)
				self.assertIn('''attention_mask'''							,					_A		)
				self.assertListEqual(list(processed.attention_mask.shape		)							,					list(processed[input_name].shape[:2]		)		)
				self.assertListEqual(processed.attention_mask.sum(-1		).tolist()							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.feat_extract_dict
				UpperCAmelCase   		=				True
				UpperCAmelCase   		=				self.feature_extraction_class(**_A		)
				UpperCAmelCase   		=				self.feat_extract_tester.prepare_inputs_for_target()
				UpperCAmelCase   		=				[len(_A		) for x in speech_inputs]
				UpperCAmelCase   		=				feat_extract.model_input_names[0]

				UpperCAmelCase   		=				BatchFeature({input_name: speech_inputs}		)
				UpperCAmelCase   		=				min(_A		)

				UpperCAmelCase   		=				feat_extract.num_mel_bins  # hack!

				UpperCAmelCase   		=				feat_extract.pad(
				    _A							,					padding='''max_length'''							,					max_length=_A							,					truncation=_A							,					return_tensors='''np'''		)
				self.assertIn('''attention_mask'''							,					_A		)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape		)							,					[processed_pad[input_name].shape[0], max_length]		)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()							,					[max_length for x in speech_inputs]		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				from datasets import load_dataset

				UpperCAmelCase   		=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,					'''clean'''							,					split='''validation'''		)
				# automatic decoding with librispeech
				UpperCAmelCase   		=				ds.sort('''id'''		).select(range(_A		)		)[:num_samples]['''audio''']

				return [x["array"] for x in speech_samples]
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				torch.tensor(
				    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
				     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
				     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
				     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
				     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
				     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]		)
				# fmt: on

				UpperCAmelCase   		=				self._load_datasamples(1		)
				UpperCAmelCase   		=				SpeechTaFeatureExtractor()
				UpperCAmelCase   		=				feature_extractor(_A							,					return_tensors='''pt'''		).input_values
				self.assertEquals(input_values.shape							,					(1, 9_3_6_8_0)		)
				self.assertTrue(torch.allclose(input_values[0, :3_0]							,					_A							,					atol=1E-6		)		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				torch.tensor(
				    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
				     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
				     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
				     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]		)
				# fmt: on

				UpperCAmelCase   		=				self._load_datasamples(1		)
				UpperCAmelCase   		=				SpeechTaFeatureExtractor()
				UpperCAmelCase   		=				feature_extractor(audio_target=_A							,					return_tensors='''pt'''		).input_values
				self.assertEquals(input_values.shape							,					(1, 3_6_6, 8_0)		)
				self.assertTrue(torch.allclose(input_values[0, 0, :3_0]							,					_A							,					atol=1E-4		)		)



