import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



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







    __UpperCamelCase  :str    						=	old_name

    if "patch_embed" in old_name:
        __UpperCamelCase  :Optional[int]    						=	old_name.split('''.'''			)

        if layer == "0":
            __UpperCamelCase  :str    						=	old_name.replace('''0'''						,    '''convolution1'''			)
        elif layer == "1":
            __UpperCamelCase  :Optional[int]    						=	old_name.replace('''1'''						,    '''batchnorm_before'''			)
        elif layer == "3":
            __UpperCamelCase  :Union[str, Any]    						=	old_name.replace('''3'''						,    '''convolution2'''			)
        else:
            __UpperCamelCase  :Union[str, Any]    						=	old_name.replace('''4'''						,    '''batchnorm_after'''			)

    if "network" in old_name and re.search(R'''\d\.\d'''						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :List[Any]    						=	R'''\b\d{2}\b'''
        if bool(re.search(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)			):
            __UpperCamelCase  :List[str]    						=	re.search(R'''\d\.\d\d.'''						,    SCREAMING_SNAKE_CASE			).group()
        else:
            __UpperCamelCase  :Any    						=	re.search(R'''\d\.\d.'''						,    SCREAMING_SNAKE_CASE			).group()
        if int(match[0]			) < 6:
            __UpperCamelCase  :List[Any]    						=	old_name.replace(SCREAMING_SNAKE_CASE						,    ''''''			)
            __UpperCamelCase  :List[str]    						=	trimmed_name.replace('''network'''						,    match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]			)
            __UpperCamelCase  :Optional[int]    						=	'''intermediate_stages.''' + trimmed_name
        else:
            __UpperCamelCase  :Tuple    						=	old_name.replace(SCREAMING_SNAKE_CASE						,    ''''''			)
            if int(match[2]			) < num_meta4D_last_stage:
                __UpperCamelCase  :str    						=	trimmed_name.replace('''network'''						,    '''meta4D_layers.blocks.''' + match[2]			)
            else:
                __UpperCamelCase  :List[str]    						=	str(int(match[2]			) - num_meta4D_last_stage			)
                __UpperCamelCase  :Optional[Any]    						=	trimmed_name.replace('''network'''						,    '''meta3D_layers.blocks.''' + layer_index			)
                if "norm1" in old_name:
                    __UpperCamelCase  :Optional[Any]    						=	trimmed_name.replace('''norm1'''						,    '''layernorm1'''			)
                elif "norm2" in old_name:
                    __UpperCamelCase  :Optional[int]    						=	trimmed_name.replace('''norm2'''						,    '''layernorm2'''			)
                elif "fc1" in old_name:
                    __UpperCamelCase  :List[str]    						=	trimmed_name.replace('''fc1'''						,    '''linear_in'''			)
                elif "fc2" in old_name:
                    __UpperCamelCase  :Union[str, Any]    						=	trimmed_name.replace('''fc2'''						,    '''linear_out'''			)

            __UpperCamelCase  :int    						=	'''last_stage.''' + trimmed_name

    elif "network" in old_name and re.search(R'''.\d.'''						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Dict    						=	old_name.replace('''network'''						,    '''intermediate_stages'''			)

    if "fc" in new_name:
        __UpperCamelCase  :Optional[Any]    						=	new_name.replace('''fc'''						,    '''convolution'''			)
    elif ("norm1" in new_name) and ("layernorm1" not in new_name):
        __UpperCamelCase  :Optional[Any]    						=	new_name.replace('''norm1'''						,    '''batchnorm_before'''			)
    elif ("norm2" in new_name) and ("layernorm2" not in new_name):
        __UpperCamelCase  :Optional[Any]    						=	new_name.replace('''norm2'''						,    '''batchnorm_after'''			)
    if "proj" in new_name:
        __UpperCamelCase  :str    						=	new_name.replace('''proj'''						,    '''projection'''			)
    if "dist_head" in new_name:
        __UpperCamelCase  :Any    						=	new_name.replace('''dist_head'''						,    '''distillation_classifier'''			)
    elif "head" in new_name:
        __UpperCamelCase  :Dict    						=	new_name.replace('''head'''						,    '''classifier'''			)
    elif "patch_embed" in new_name:
        __UpperCamelCase  :int    						=	'''efficientformer.''' + new_name
    elif new_name == "norm.weight" or new_name == "norm.bias":
        __UpperCamelCase  :int    						=	new_name.replace('''norm'''						,    '''layernorm'''			)
        __UpperCamelCase  :int    						=	'''efficientformer.''' + new_name
    else:
        __UpperCamelCase  :Dict    						=	'''efficientformer.encoder.''' + new_name

    return new_name



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







    for key in checkpoint.copy().keys():
        __UpperCamelCase  :Tuple    						=	checkpoint.pop(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Optional[Any]    						=	val

    return checkpoint



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







    __UpperCamelCase  :Tuple    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase  :Any    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			)

    return image



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







    __UpperCamelCase  :Union[str, Any]    						=	torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)['''model''']
    __UpperCamelCase  :Optional[int]    						=	EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	'''_'''.join(checkpoint_path.split('''/'''			)[-1].split('''.'''			)[0].split('''_'''			)[:-1]			)

    __UpperCamelCase  :Union[str, Any]    						=	config.depths[-1] - config.num_metaad_blocks + 1
    __UpperCamelCase  :List[Any]    						=	convert_torch_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    model.load_state_dict(SCREAMING_SNAKE_CASE			)
    model.eval()

    __UpperCamelCase  :Union[str, Any]    						=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    # prepare image
    __UpperCamelCase  :Any    						=	prepare_img()
    __UpperCamelCase  :int    						=	256
    __UpperCamelCase  :Tuple    						=	224
    __UpperCamelCase  :List[Any]    						=	EfficientFormerImageProcessor(
        size={'''shortest_edge''': image_size}						,    crop_size={'''height''': crop_size, '''width''': crop_size}						,    resample=pillow_resamplings['''bicubic''']						,    )
    __UpperCamelCase  :Tuple    						=	processor(images=SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			).pixel_values

    # original processing pipeline
    __UpperCamelCase  :List[Any]    						=	Compose(
        [
            Resize(SCREAMING_SNAKE_CASE						,    interpolation=pillow_resamplings['''bicubic''']			),
            CenterCrop(SCREAMING_SNAKE_CASE			),
            ToTensor(),
            Normalize(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			),
        ]			)
    __UpperCamelCase  :Dict    						=	image_transforms(SCREAMING_SNAKE_CASE			).unsqueeze(0			)

    assert torch.allclose(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[int]    						=	model(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Dict    						=	outputs.logits

    __UpperCamelCase  :Optional[Any]    						=	(1, 1_000)

    if "l1" in model_name:
        __UpperCamelCase  :Optional[Any]    						=	torch.Tensor(
            [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]			)
        assert torch.allclose(logits[0, :10]						,    SCREAMING_SNAKE_CASE						,    atol=1e-3			)
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        __UpperCamelCase  :List[Any]    						=	torch.Tensor(
            [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]			)
        assert torch.allclose(logits[0, :10]						,    SCREAMING_SNAKE_CASE						,    atol=1e-3			)
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        __UpperCamelCase  :List[Any]    						=	torch.Tensor(
            [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]			)
        assert logits.shape == expected_shape
    else:
        raise ValueError(
            f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""			)

    # Save Checkpoints
    Path(SCREAMING_SNAKE_CASE			).mkdir(exist_ok=SCREAMING_SNAKE_CASE			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)
    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""			)
    processor.save_pretrained(SCREAMING_SNAKE_CASE			)
    print(f"""Processor successfuly saved at {pytorch_dump_path}"""			)

    if push_to_hub:
        print('''Pushing model to the hub...'''			)

        model.push_to_hub(
            repo_id=f"""Bearnardd/{pytorch_dump_path}"""						,    commit_message='''Add model'''						,    use_temp_dir=SCREAMING_SNAKE_CASE						,    )
        processor.push_to_hub(
            repo_id=f"""Bearnardd/{pytorch_dump_path}"""						,    commit_message='''Add image processor'''						,    use_temp_dir=SCREAMING_SNAKE_CASE						,    )


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--pytorch_model_path''',
        default=None,
        type=str,
        required=True,
        help='''Path to EfficientFormer pytorch checkpoint.''',
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The json file for EfficientFormer model config.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )

    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
    parser.add_argument(
        '''--no-push_to_hub''',
        dest='''push_to_hub''',
        action='''store_false''',
        help='''Do not push model and image processor to the hub''',
    )
    parser.set_defaults(push_to_hub=True)

    __lowercase											=      parser.parse_args()
    convert_efficientformer_checkpoint(
        checkpoint_path=args.pytorch_model_path,
        efficientformer_config_file=args.config_file,
        pytorch_dump_path=args.pytorch_dump_path,
        push_to_hub=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])





