
"""simple docstring"""





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()
__magic_name__       =			logging.get_logger(__name__)



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

		return new_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		# 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)
						__SCREAMING_SNAKE_CASE            = state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"				)
						__SCREAMING_SNAKE_CASE            = 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
						__SCREAMING_SNAKE_CASE            = kv_weight[
						    : config.hidden_sizes[i], :
						]
						__SCREAMING_SNAKE_CASE            = kv_bias[: config.hidden_sizes[i]]
						__SCREAMING_SNAKE_CASE            = kv_weight[
						    config.hidden_sizes[i] :, :
						]
						__SCREAMING_SNAKE_CASE            = kv_bias[
						    config.hidden_sizes[i] :
						]



def 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = """http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE            = Image.open(requests.get(UpperCamelCase_  ,				stream=UpperCamelCase_				).raw				)

		return image



@torch.no_grad()
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = SegformerConfig()
		__SCREAMING_SNAKE_CASE            = False

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

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

		# load image processor (only resize + normalize)
		__SCREAMING_SNAKE_CASE            = SegformerImageProcessor(
		    image_scale=(512, 512)  ,				keep_ratio=UpperCamelCase_  ,				align=UpperCamelCase_  ,				do_random_crop=UpperCamelCase_				)

		# prepare image
		__SCREAMING_SNAKE_CASE            = prepare_img()
		__SCREAMING_SNAKE_CASE            = image_processor(images=UpperCamelCase_  ,				return_tensors="""pt"""				).pixel_values

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

		# load original state dict
		if encoder_only:
				__SCREAMING_SNAKE_CASE            = torch.load(UpperCamelCase_  ,				map_location=torch.device("""cpu"""				)				)
		else:
				__SCREAMING_SNAKE_CASE            = torch.load(UpperCamelCase_  ,				map_location=torch.device("""cpu"""				)				)["""state_dict"""]

		# rename keys
		__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = False
				__SCREAMING_SNAKE_CASE            = SegformerForImageClassification(UpperCamelCase_				)
		else:
				__SCREAMING_SNAKE_CASE            = SegformerForSemanticSegmentation(UpperCamelCase_				)
		model.load_state_dict(UpperCamelCase_				)
		model.eval()

		# forward pass
		__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [
				        [
				            [-1.1372e01, -1.2787e01, -1.3477e01],
				            [-1.2536e01, -1.4194e01, -1.4409e01],
				            [-1.3217e01, -1.4888e01, -1.5327e01],
				        ],
				        [
				            [-1.4791e01, -1.7122e01, -1.8277e01],
				            [-1.7163e01, -1.9192e01, -1.9533e01],
				            [-1.7897e01, -1.9991e01, -2.0315e01],
				        ],
				        [
				            [7.6723e-01, 4.1921e-01, -7.7878e-02],
				            [4.7772e-01, 9.5557e-03, -2.8082e-01],
				            [3.6032e-01, -2.4826e-01, -5.1168e-01],
				        ],
				    ]				)
		elif model_name == "segformer.b0.768x768.city.160k":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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":
				__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = 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__":
							__magic_name__       =			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."
							)
							__magic_name__       =			parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    """kwargs, expected"""  ,				[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCamelCase_  ,				i + 1				) for i in range(10				)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0  ,				4				), range(4  ,				7				), range(7  ,				10				)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0  ,				1				), range(1  ,				2				), range(2  ,				3				)]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _distribute_shards(**UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""  ,				[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _split_gen_kwargs(UpperCamelCase_  ,				UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, expected"""  ,				[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if expected is RuntimeError:
				with pytest.raises(UpperCamelCase_				):
						_number_of_shards_in_gen_kwargs(UpperCamelCase_				)
		else:
				__SCREAMING_SNAKE_CASE            = _number_of_shards_in_gen_kwargs(UpperCamelCase_				)
				assert out == expected




