





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase       : List[str]			     =       logging.get_logger(__name__)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=False				)    ->  Tuple:
    lowerCamelCase__					:   Union[str, Any]      				=	OrderedDict()
    for key, value in state_dict.items():
        if encoder_only and not key.startswith('head'				):
            lowerCamelCase__					:   Tuple      				=	'segformer.encoder.' + key
        if key.startswith('backbone'				):
            lowerCamelCase__					:   Dict      				=	key.replace('backbone'		,			'segformer.encoder'				)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            lowerCamelCase__					:   Any      				=	key[key.find('patch_embed'				) + len('patch_embed'				)]
            lowerCamelCase__					:   List[str]      				=	key.replace(F"""patch_embed{idx}"""		,			F"""patch_embeddings.{int(_UpperCAmelCase				)-1}"""				)
        if "norm" in key:
            lowerCamelCase__					:   Optional[int]      				=	key.replace('norm'		,			'layer_norm'				)
        if "segformer.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            lowerCamelCase__					:   Tuple      				=	key[key.find('segformer.encoder.layer_norm'				) + len('segformer.encoder.layer_norm'				)]
            lowerCamelCase__					:   Dict      				=	key.replace(F"""layer_norm{idx}"""		,			F"""layer_norm.{int(_UpperCAmelCase				)-1}"""				)
        if "layer_norm1" in key:
            lowerCamelCase__					:   List[Any]      				=	key.replace('layer_norm1'		,			'layer_norm_1'				)
        if "layer_norm2" in key:
            lowerCamelCase__					:   List[str]      				=	key.replace('layer_norm2'		,			'layer_norm_2'				)
        if "block" in key:
            # replace for example block1 by block.0
            lowerCamelCase__					:   Optional[int]      				=	key[key.find('block'				) + len('block'				)]
            lowerCamelCase__					:   Any      				=	key.replace(F"""block{idx}"""		,			F"""block.{int(_UpperCAmelCase				)-1}"""				)
        if "attn.q" in key:
            lowerCamelCase__					:   Tuple      				=	key.replace('attn.q'		,			'attention.self.query'				)
        if "attn.proj" in key:
            lowerCamelCase__					:   Tuple      				=	key.replace('attn.proj'		,			'attention.output.dense'				)
        if "attn" in key:
            lowerCamelCase__					:   Tuple      				=	key.replace('attn'		,			'attention.self'				)
        if "fc1" in key:
            lowerCamelCase__					:   List[Any]      				=	key.replace('fc1'		,			'dense1'				)
        if "fc2" in key:
            lowerCamelCase__					:   Union[str, Any]      				=	key.replace('fc2'		,			'dense2'				)
        if "linear_pred" in key:
            lowerCamelCase__					:   Optional[Any]      				=	key.replace('linear_pred'		,			'classifier'				)
        if "linear_fuse" in key:
            lowerCamelCase__					:   Union[str, Any]      				=	key.replace('linear_fuse.conv'		,			'linear_fuse'				)
            lowerCamelCase__					:   List[Any]      				=	key.replace('linear_fuse.bn'		,			'batch_norm'				)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            lowerCamelCase__					:   Any      				=	key[key.find('linear_c'				) + len('linear_c'				)]
            lowerCamelCase__					:   Optional[int]      				=	key.replace(F"""linear_c{idx}"""		,			F"""linear_c.{int(_UpperCAmelCase				)-1}"""				)
        if key.startswith('head'				):
            lowerCamelCase__					:   int      				=	key.replace('head'		,			'classifier'				)
        lowerCamelCase__					:   Any      				=	value

    return new_state_dict


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks				):
        for j in range(config.depths[i]				):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            lowerCamelCase__					:   int      				=	state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
            lowerCamelCase__					:   List[str]      				=	state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""				)
            # next, add keys and values (in that order) to the state dict
            lowerCamelCase__					:   Any      				=	kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__					:   List[str]      				=	kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__					:   Optional[int]      				=	kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__					:   List[Any]      				=	kv_bias[
                config.hidden_sizes[i] :
            ]


def  SCREAMING_SNAKE_CASE   ( )    ->  List[str]:
    lowerCamelCase__					:   Optional[int]      				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__					:   Union[str, Any]      				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase				).raw				)

    return image


@torch.no_grad()
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   List[Any]      				=	SegformerConfig()
    lowerCamelCase__					:   List[Any]      				=	False

    # set attributes based on model_name
    lowerCamelCase__					:   Optional[int]      				=	'huggingface/label-files'
    if "segformer" in model_name:
        lowerCamelCase__					:   List[Any]      				=	model_name[len('segformer.'				) : len('segformer.'				) + 2]
        if "ade" in model_name:
            lowerCamelCase__					:   Tuple      				=	150
            lowerCamelCase__					:   Any      				=	'ade20k-id2label.json'
            lowerCamelCase__					:   List[Any]      				=	(1, 150, 128, 128)
        elif "city" in model_name:
            lowerCamelCase__					:   List[str]      				=	19
            lowerCamelCase__					:   Optional[Any]      				=	'cityscapes-id2label.json'
            lowerCamelCase__					:   str      				=	(1, 19, 128, 128)
        else:
            raise ValueError(F"""Model {model_name} not supported"""				)
    elif "mit" in model_name:
        lowerCamelCase__					:   Union[str, Any]      				=	True
        lowerCamelCase__					:   str      				=	model_name[4:6]
        lowerCamelCase__					:   str      				=	1000
        lowerCamelCase__					:   str      				=	'imagenet-1k-id2label.json'
        lowerCamelCase__					:   Tuple      				=	(1, 1000)
    else:
        raise ValueError(F"""Model {model_name} not supported"""				)

    # set config attributes
    lowerCamelCase__					:   int      				=	json.load(open(hf_hub_download(_UpperCAmelCase		,			_UpperCAmelCase		,			repo_type='dataset'				)		,			'r'				)				)
    lowerCamelCase__					:   int      				=	{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
    lowerCamelCase__					:   List[str]      				=	idalabel
    lowerCamelCase__					:   List[str]      				=	{v: k for k, v in idalabel.items()}
    if size == "b0":
        pass
    elif size == "b1":
        lowerCamelCase__					:   int      				=	[64, 128, 320, 512]
        lowerCamelCase__					:   Optional[Any]      				=	256
    elif size == "b2":
        lowerCamelCase__					:   Any      				=	[64, 128, 320, 512]
        lowerCamelCase__					:   List[Any]      				=	768
        lowerCamelCase__					:   Optional[Any]      				=	[3, 4, 6, 3]
    elif size == "b3":
        lowerCamelCase__					:   Optional[int]      				=	[64, 128, 320, 512]
        lowerCamelCase__					:   Optional[Any]      				=	768
        lowerCamelCase__					:   Optional[int]      				=	[3, 4, 18, 3]
    elif size == "b4":
        lowerCamelCase__					:   Any      				=	[64, 128, 320, 512]
        lowerCamelCase__					:   List[Any]      				=	768
        lowerCamelCase__					:   List[str]      				=	[3, 8, 27, 3]
    elif size == "b5":
        lowerCamelCase__					:   Union[str, Any]      				=	[64, 128, 320, 512]
        lowerCamelCase__					:   Dict      				=	768
        lowerCamelCase__					:   str      				=	[3, 6, 40, 3]
    else:
        raise ValueError(F"""Size {size} not supported"""				)

    # load image processor (only resize + normalize)
    lowerCamelCase__					:   Tuple      				=	SegformerImageProcessor(
        image_scale=(512, 512)		,			keep_ratio=_UpperCAmelCase		,			align=_UpperCAmelCase		,			do_random_crop=_UpperCAmelCase				)

    # prepare image
    lowerCamelCase__					:   int      				=	prepare_img()
    lowerCamelCase__					:   Optional[Any]      				=	image_processor(images=_UpperCAmelCase		,			return_tensors='pt'				).pixel_values

    logger.info(F"""Converting model {model_name}..."""				)

    # load original state dict
    if encoder_only:
        lowerCamelCase__					:   Dict      				=	torch.load(_UpperCAmelCase		,			map_location=torch.device('cpu'				)				)
    else:
        lowerCamelCase__					:   List[str]      				=	torch.load(_UpperCAmelCase		,			map_location=torch.device('cpu'				)				)['state_dict']

    # rename keys
    lowerCamelCase__					:   Optional[int]      				=	rename_keys(_UpperCAmelCase		,			encoder_only=_UpperCAmelCase				)
    if not encoder_only:
        del state_dict["decode_head.conv_seg.weight"]
        del state_dict["decode_head.conv_seg.bias"]

    # key and value matrices need special treatment
    read_in_k_v(_UpperCAmelCase		,			_UpperCAmelCase				)

    # create HuggingFace model and load state dict
    if encoder_only:
        lowerCamelCase__					:   Optional[int]      				=	False
        lowerCamelCase__					:   List[Any]      				=	SegformerForImageClassification(_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   Tuple      				=	SegformerForSemanticSegmentation(_UpperCAmelCase				)
    model.load_state_dict(_UpperCAmelCase				)
    model.eval()

    # forward pass
    lowerCamelCase__					:   List[str]      				=	model(_UpperCAmelCase				)
    lowerCamelCase__					:   Tuple      				=	outputs.logits

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        lowerCamelCase__					:   List[Any]      				=	torch.tensor(
            [
                [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
            ]				)
    elif model_name == "segformer.b1.512x512.ade.160k":
        lowerCamelCase__					:   List[str]      				=	torch.tensor(
            [
                [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
                [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
                [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
            ]				)
    elif model_name == "segformer.b2.512x512.ade.160k":
        lowerCamelCase__					:   Optional[Any]      				=	torch.tensor(
            [
                [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
                [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
                [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
            ]				)
    elif model_name == "segformer.b3.512x512.ade.160k":
        lowerCamelCase__					:   Dict      				=	torch.tensor(
            [
                [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
                [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
                [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
            ]				)
    elif model_name == "segformer.b4.512x512.ade.160k":
        lowerCamelCase__					:   Optional[int]      				=	torch.tensor(
            [
                [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
                [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
                [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
            ]				)
    elif model_name == "segformer.b5.640x640.ade.160k":
        lowerCamelCase__					:   int      				=	torch.tensor(
            [
                [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
                [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
                [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
            ]				)
    # Cityscapes checkpoints
    elif model_name == "segformer.b0.1024x1024.city.160k":
        lowerCamelCase__					:   Optional[Any]      				=	torch.tensor(
            [
                [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
                [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
                [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
            ]				)
    elif model_name == "segformer.b0.512x1024.city.160k":
        lowerCamelCase__					:   Optional[int]      				=	torch.tensor(
            [
                [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
                [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
                [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
            ]				)
    elif model_name == "segformer.b0.640x1280.city.160k":
        lowerCamelCase__					:   Dict      				=	torch.tensor(
            [
                [
                    [-1.1_372e01, -1.2_787e01, -1.3_477e01],
                    [-1.2_536e01, -1.4_194e01, -1.4_409e01],
                    [-1.3_217e01, -1.4_888e01, -1.5_327e01],
                ],
                [
                    [-1.4_791e01, -1.7_122e01, -1.8_277e01],
                    [-1.7_163e01, -1.9_192e01, -1.9_533e01],
                    [-1.7_897e01, -1.9_991e01, -2.0_315e01],
                ],
                [
                    [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
                    [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
                    [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
                ],
            ]				)
    elif model_name == "segformer.b0.768x768.city.160k":
        lowerCamelCase__					:   List[Any]      				=	torch.tensor(
            [
                [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
                [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
                [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
            ]				)
    elif model_name == "segformer.b1.1024x1024.city.160k":
        lowerCamelCase__					:   Union[str, Any]      				=	torch.tensor(
            [
                [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
            ]				)
    elif model_name == "segformer.b2.1024x1024.city.160k":
        lowerCamelCase__					:   Dict      				=	torch.tensor(
            [
                [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
                [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
                [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
            ]				)
    elif model_name == "segformer.b3.1024x1024.city.160k":
        lowerCamelCase__					:   Optional[Any]      				=	torch.tensor(
            [
                [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
                [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
                [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
            ]				)
    elif model_name == "segformer.b4.1024x1024.city.160k":
        lowerCamelCase__					:   Optional[Any]      				=	torch.tensor(
            [
                [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
                [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
                [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
            ]				)
    elif model_name == "segformer.b5.1024x1024.city.160k":
        lowerCamelCase__					:   Optional[Any]      				=	torch.tensor(
            [
                [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
                [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
                [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
            ]				)
    else:
        lowerCamelCase__					:   str      				=	logits.argmax(-1				).item()
        print('Predicted class:'		,			model.config.idalabel[predicted_class_idx]				)

    # verify logits
    if not encoder_only:
        assert logits.shape == expected_shape
        assert torch.allclose(logits[0, :3, :3, :3]		,			_UpperCAmelCase		,			atol=1e-2				)

    # finally, save model and image processor
    logger.info(F"""Saving PyTorch 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 __name__ == "__main__":
    _UpperCAmelCase       : int			     =       argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""segformer.b0.512x512.ade.160k""",
        type=str,
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    _UpperCAmelCase       : int			     =       parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      lowerCAmelCase							(      unittest.TestCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[Any]=3    ,  UpperCAmelCase						:   str=32    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Dict=10    ,  UpperCAmelCase						:   List[str]=[10, 20, 30, 40]    ,  UpperCAmelCase						:   Any=[1, 1, 2, 1]    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Dict="relu"    ,  UpperCAmelCase						:   Tuple=3    ,  UpperCAmelCase						:   Dict=None    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   Dict      				=	image_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embeddings_size
     lowerCamelCase__					:   str      				=	hidden_sizes
     lowerCamelCase__					:   Any      				=	depths
     lowerCamelCase__					:   str      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Union[str, Any]      				=	hidden_act
     lowerCamelCase__					:   Dict      				=	num_labels
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	len(UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     lowerCamelCase__					:   str      				=	self.get_config()

     return config, pixel_values
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     return RegNetConfig(
         num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  image_size=self.image_size    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	FlaxRegNetModel(config=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	model(UpperCAmelCase			)

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.num_labels
     lowerCamelCase__					:   str      				=	FlaxRegNetForImageClassification(config=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)







 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_config_and_inputs()
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	config_and_inputs
     lowerCamelCase__					:   Dict      				=	{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 def   A_			(   self						:   List[Any]			)    ->			None:
     lowerCamelCase__					:   List[Any]      				=	FlaxRegNetModelTester(self			)
     lowerCamelCase__					:   Union[str, Any]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Tuple:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
 def   A_			(   self						:   Any			)    ->			str:
     return
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)
 @unittest.skip(reason='RegNet does not use inputs_embeds'			)
 def   A_			(   self						:   Dict			)    ->			Dict:
     pass
 @unittest.skip(reason='RegNet does not support input and output embeddings'			)
 def   A_			(   self						:   Any			)    ->			Tuple:
     pass
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   Optional[int]      				=	model_class(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	inspect.signature(model.__call__			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__					:   List[Any]      				=	[*signature.parameters.keys()]

         lowerCamelCase__					:   int      				=	['pixel_values']
         self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     def check_hidden_states_output(UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]			):
         lowerCamelCase__					:   Any      				=	model_class(UpperCAmelCase			)

         lowerCamelCase__					:   Optional[Any]      				=	model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)			)

         lowerCamelCase__					:   Union[str, Any]      				=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCamelCase__					:   Optional[int]      				=	self.model_tester.num_stages
         self.assertEqual(len(UpperCAmelCase			)    ,  expected_num_stages + 1			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   List[str]      				=	True
         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__					:   List[Any]      				=	True

         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   int      				=	self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	model_class(UpperCAmelCase			)

             @jax.jit
             def model_jitted(UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Tuple			):
                 return model(pixel_values=UpperCAmelCase    ,  **UpperCAmelCase			)

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[int]      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             self.assertEqual(len(UpperCAmelCase			)    ,  len(UpperCAmelCase			)			)
             for jitted_output, output in zip(UpperCAmelCase    ,  UpperCAmelCase			):
                 self.assertEqual(jitted_output.shape    ,  output.shape			)


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 @cached_property
 def   A_			(   self						:   Any			)    ->			List[Any]:
     return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







 @slow
 def   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

     lowerCamelCase__					:   Dict      				=	self.default_image_processor
     lowerCamelCase__					:   List[Any]      				=	prepare_img()
     lowerCamelCase__					:   List[str]      				=	image_processor(images=UpperCAmelCase    ,  return_tensors='np'			)

     lowerCamelCase__					:   List[Any]      				=	model(**UpperCAmelCase			)

     # verify the logits
     lowerCamelCase__					:   Union[str, Any]      				=	(1, 1000)
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			)

     self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,  UpperCAmelCase    ,  atol=1e-4			)			)





