import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	384
    __UpperCamelCase  :List[Any]    						=	7
    if "tiny" in model_name:
        __UpperCamelCase  :Any    						=	96
        __UpperCamelCase  :int    						=	(2, 2, 6, 2)
        __UpperCamelCase  :List[Any]    						=	(3, 6, 12, 24)
    elif "small" in model_name:
        __UpperCamelCase  :Optional[int]    						=	96
        __UpperCamelCase  :Optional[Any]    						=	(2, 2, 18, 2)
        __UpperCamelCase  :Union[str, Any]    						=	(3, 6, 12, 24)
    elif "base" in model_name:
        __UpperCamelCase  :Optional[int]    						=	128
        __UpperCamelCase  :Dict    						=	(2, 2, 18, 2)
        __UpperCamelCase  :Union[str, Any]    						=	(4, 8, 16, 32)
        __UpperCamelCase  :str    						=	12
        __UpperCamelCase  :Optional[int]    						=	512
    elif "large" in model_name:
        __UpperCamelCase  :str    						=	192
        __UpperCamelCase  :List[str]    						=	(2, 2, 18, 2)
        __UpperCamelCase  :Optional[int]    						=	(6, 12, 24, 48)
        __UpperCamelCase  :Any    						=	12
        __UpperCamelCase  :List[Any]    						=	768

    # set label information
    __UpperCamelCase  :Optional[Any]    						=	150
    __UpperCamelCase  :int    						=	'''huggingface/label-files'''
    __UpperCamelCase  :int    						=	'''ade20k-id2label.json'''
    __UpperCamelCase  :Any    						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    repo_type='''dataset'''			)						,    '''r'''			)			)
    __UpperCamelCase  :Union[str, Any]    						=	{int(SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
    __UpperCamelCase  :List[Any]    						=	{v: k for k, v in idalabel.items()}

    __UpperCamelCase  :Union[str, Any]    						=	SwinConfig(
        embed_dim=SCREAMING_SNAKE_CASE						,    depths=SCREAMING_SNAKE_CASE						,    num_heads=SCREAMING_SNAKE_CASE						,    window_size=SCREAMING_SNAKE_CASE						,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,    )
    __UpperCamelCase  :int    						=	UperNetConfig(
        backbone_config=SCREAMING_SNAKE_CASE						,    auxiliary_in_channels=SCREAMING_SNAKE_CASE						,    num_labels=SCREAMING_SNAKE_CASE						,    idalabel=SCREAMING_SNAKE_CASE						,    labelaid=SCREAMING_SNAKE_CASE						,    )

    return config



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	[]

    # fmt: off
    # stem
    rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')			)
    rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')			)
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')			)
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.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.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

        if i < 3:
            rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")			)
        rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")			)
        rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")			)

    # decode head
    rename_keys.extend(
        [
            ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
            ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
            ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
            ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
        ]			)
    # fmt: on

    return rename_keys



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	dct.pop(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	val



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __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  :str    						=	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  :str    						=	state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""			)
            __UpperCamelCase  :Optional[Any]    						=	state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""			)
            # next, add query, keys and values (in that order) to the state dict
            __UpperCamelCase  :Dict    						=	in_proj_weight[:dim, :]
            __UpperCamelCase  :Union[str, Any]    						=	in_proj_bias[: dim]
            __UpperCamelCase  :Union[str, Any]    						=	in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCamelCase  :Any    						=	in_proj_bias[
                dim : dim * 2
            ]
            __UpperCamelCase  :Any    						=	in_proj_weight[
                -dim :, :
            ]
            __UpperCamelCase  :Any    						=	in_proj_bias[-dim :]
            # fmt: on



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	x.shape
    __UpperCamelCase  :str    						=	x.reshape(SCREAMING_SNAKE_CASE						,    4						,    in_channel // 4			)
    __UpperCamelCase  :Union[str, Any]    						=	x[:, [0, 2, 1, 3], :].transpose(1						,    2			).reshape(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    return x



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	x.shape
    __UpperCamelCase  :Tuple    						=	x.reshape(SCREAMING_SNAKE_CASE						,    in_channel // 4						,    4			)
    __UpperCamelCase  :Optional[int]    						=	x[:, :, [0, 2, 1, 3]].transpose(1						,    2			).reshape(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    return x



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	x.shape[0]
    __UpperCamelCase  :int    						=	x.reshape(4						,    in_channel // 4			)
    __UpperCamelCase  :Optional[Any]    						=	x[[0, 2, 1, 3], :].transpose(0						,    1			).reshape(SCREAMING_SNAKE_CASE			)
    return x



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	x.shape[0]
    __UpperCamelCase  :str    						=	x.reshape(in_channel // 4						,    4			)
    __UpperCamelCase  :Tuple    						=	x[:, [0, 2, 1, 3]].transpose(0						,    1			).reshape(SCREAMING_SNAKE_CASE			)
    return x



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	{
        '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
        '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
        '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
        '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
    }
    __UpperCamelCase  :List[str]    						=	model_name_to_url[model_name]
    __UpperCamelCase  :Union[str, Any]    						=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''						,    file_name=SCREAMING_SNAKE_CASE			)[
        '''state_dict'''
    ]

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

    __UpperCamelCase  :List[str]    						=	get_upernet_config(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :int    						=	UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __UpperCamelCase  :List[Any]    						=	state_dict.pop(SCREAMING_SNAKE_CASE			)
        if "bn" in key:
            __UpperCamelCase  :Any    						=	key.replace('''bn'''						,    '''batch_norm'''			)
        __UpperCamelCase  :str    						=	val

    # rename keys
    __UpperCamelCase  :Dict    						=	create_rename_keys(SCREAMING_SNAKE_CASE			)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    read_in_q_k_v(SCREAMING_SNAKE_CASE						,    config.backbone_config			)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                __UpperCamelCase  :int    						=	reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE			)
            if "norm" in key:
                __UpperCamelCase  :Tuple    						=	reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE			)

    model.load_state_dict(SCREAMING_SNAKE_CASE			)

    # verify on image
    __UpperCamelCase  :Optional[int]    						=	'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    __UpperCamelCase  :List[str]    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			).convert('''RGB'''			)

    __UpperCamelCase  :Optional[int]    						=	SegformerImageProcessor()
    __UpperCamelCase  :int    						=	processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			).pixel_values

    with torch.no_grad():
        __UpperCamelCase  :str    						=	model(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Dict    						=	outputs.logits

    print(logits.shape			)
    print('''First values of logits:'''						,    logits[0, 0, :3, :3]			)
    # assert values
    if model_name == "upernet-swin-tiny":
        __UpperCamelCase  :Optional[int]    						=	torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]			)
    elif model_name == "upernet-swin-small":
        __UpperCamelCase  :List[str]    						=	torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]			)
    elif model_name == "upernet-swin-base":
        __UpperCamelCase  :Union[str, Any]    						=	torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]			)
    elif model_name == "upernet-swin-large":
        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]			)
    print('''Logits:'''						,    outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]						,    SCREAMING_SNAKE_CASE						,    atol=1e-4			)
    print('''Looks ok!'''			)

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
        model.save_pretrained(SCREAMING_SNAKE_CASE			)
        print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE			)

    if push_to_hub:
        print(f"""Pushing model and processor for {model_name} to hub"""			)
        model.push_to_hub(f"""openmmlab/{model_name}"""			)
        processor.push_to_hub(f"""openmmlab/{model_name}"""			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''upernet-swin-tiny''',
        type=str,
        choices=[F'upernet-swin-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''']],
        help='''Name of the Swin + UperNet model you\'d like to convert.''',
    )
    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.'''
    )

    __lowercase											=      parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Union[str, Any]      			=   None
    a__			:		Tuple      			=   None







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(__lowercase				,				'''feature_size'''))
        self.assertTrue(hasattr(__lowercase				,				'''sampling_rate'''))
        self.assertTrue(hasattr(__lowercase				,				'''padding_value'''))







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Optional[int]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

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

        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Optional[int]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''np''')

        __UpperCamelCase  :Any    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :Optional[Any]    						=	batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    @require_torch
    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Tuple    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

        __UpperCamelCase  :Union[str, Any]    						=	processed_features[input_name]

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

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

        __UpperCamelCase  :int    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :List[Any]    						=	batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :List[str]    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

        def _inputs_are_equal(__lowercase				,				__lowercase):
            if len(__lowercase) != len(__lowercase):
                return False

            for input_slice_a, input_slice_a in zip(__lowercase				,				__lowercase):
                if not np.allclose(np.asarray(__lowercase)				,				np.asarray(__lowercase)				,				atol=1E-3):
                    return False
            return True

        __UpperCamelCase  :List[str]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :Optional[Any]    						=	self.feat_extract_tester.seq_length_diff
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.max_seq_length + pad_diff
        __UpperCamelCase  :Tuple    						=	self.feat_extract_tester.min_seq_length
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.batch_size
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding=__lowercase)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				padding='''longest''')
        __UpperCamelCase  :int    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[int]    						=	input_a[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''max_length''')[input_name]

        __UpperCamelCase  :Any    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				return_tensors='''np''')
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))
        self.assertTrue(len(input_a[0]) == pad_min_length)
        self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
        self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
        self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

        if feature_size > 1:
            self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

        self.assertTrue(all(len(__lowercase) % 10 == 0 for x in input_a))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))

        __UpperCamelCase  :str    						=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(__lowercase) == expected_mult_pad_length for x in input_a))
        self.assertEqual(input_a.shape[:2]				,				(batch_size, expected_mult_pad_length))

        if feature_size > 1:
            self.assertTrue(input_a.shape[2] == feature_size)

        # Check padding value is correct
        __UpperCamelCase  :Optional[Any]    						=	(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
            < 1E-3)
        self.assertTrue(
            abs(
                np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
            < 1E-3)
        self.assertTrue(
            abs(
                np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
            < 1E-3)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
            < 1E-3)







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :Dict    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

        def _inputs_are_equal(__lowercase				,				__lowercase):
            if len(__lowercase) != len(__lowercase):
                return False

            for input_slice_a, input_slice_a in zip(__lowercase				,				__lowercase):
                if not np.allclose(np.asarray(__lowercase)				,				np.asarray(__lowercase)				,				atol=1E-3):
                    return False
            return True

        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Tuple    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Dict    						=	BatchFeature({input_name: speech_inputs})

        # truncate to smallest
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				truncation=__lowercase)
        __UpperCamelCase  :str    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0]))
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to smallest with np
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[Any]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to middle
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowercase))
        self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				truncation=__lowercase)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''longest'''				,				truncation=__lowercase)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''longest'''				,				truncation=__lowercase)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(__lowercase):
            feat_extract.pad(__lowercase				,				padding='''max_length'''				,				truncation=__lowercase)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :Dict    						=	12
        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :Tuple    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        __UpperCamelCase  :Optional[Any]    						=	len(speech_inputs[0])
        if expected_length % pad_to_multiple_of != 0:
            __UpperCamelCase  :Dict    						=	((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_a[0]) == expected_length)
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_truncation(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        self._check_truncation(numpify=__lowercase)







    @require_torch
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				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)







    @require_tf
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Union[str, Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :str    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''tf''')[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_dict
        __UpperCamelCase  :Dict    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :int    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[int]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_dict
        __UpperCamelCase  :Optional[int]    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[str]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :List[Any]    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :List[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})
        __UpperCamelCase  :Dict    						=	min(__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				truncation=__lowercase				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        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])





