"""simple docstring"""





import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    SwinConfig.from_pretrained(
							    """microsoft/swin-tiny-patch4-window7-224"""     ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    MaskFormerConfig(backbone_config=__SCREAMING_SNAKE_CASE  )

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

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

							return config



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						str			  =    []
							# stem
							# fmt: off
							rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")  )
							rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")  )
							rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")  )
							rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")  )
							# stages
							for i in range(len(config.backbone_config.depths  )  ):
														for j in range(config.backbone_config.depths[i]  ):
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")  )

														if i < 3:
																					rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")  )
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")  )
														rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")  )
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")  )

							# FPN
							rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")  )
							rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")  )
							rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")  )
							for source_index, target_index in zip(range(3     ,	0     ,	-1  )     ,	range(0     ,	3  )  ):
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")  )
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")  )
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")  )
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")  )
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")  )
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")  )
							rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")  )
							rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")  )

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers  ):
														# self-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")  )
														# cross-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")  )
														# MLP 1
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")  )
														# MLP 2
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")  )
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")  )
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")  )
														# layernorm 3 (final layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")  )

							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")  )
							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")  )

							# heads on top
							rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")  )

							rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")  )
							rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")  )

							rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")  )
							rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")  )

							for i in range(3  ):
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")  )
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")  )
							# fmt: on

							return rename_keys



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						str			  =    dct.pop(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    val



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
							for i in range(len(backbone_config.depths  )  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    num_features[i]
														for j in range(backbone_config.depths[i]  ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""  )
																					_SCREAMING_SNAKE_CASE		:						int			  =    state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""  )
																					# next, add query, keys and values (in that order) to the state dict
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    in_proj_weight[:dim, :]
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    in_proj_bias[: dim]
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_weight[
																					    dim : dim * 2, :
																					]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    in_proj_bias[
																					    dim : dim * 2
																					]
																					_SCREAMING_SNAKE_CASE		:						int			  =    in_proj_weight[
																					    -dim :, :
																					]
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_bias[-dim :]
																					# fmt: on



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							# fmt: off
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers  ):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														_SCREAMING_SNAKE_CASE		:						Any			  =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""  )
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""  )
														# next, add query, keys and values (in that order) to the state dict
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_weight[: hidden_size, :]
														_SCREAMING_SNAKE_CASE		:						str			  =    in_proj_bias[:config.hidden_size]
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    in_proj_weight[hidden_size : hidden_size * 2, :]
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    in_proj_bias[hidden_size : hidden_size * 2]
														_SCREAMING_SNAKE_CASE		:						str			  =    in_proj_weight[-hidden_size :, :]
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""  )
														_SCREAMING_SNAKE_CASE		:						str			  =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""  )
														# next, add query, keys and values (in that order) to the state dict
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    in_proj_weight[: hidden_size, :]
														_SCREAMING_SNAKE_CASE		:						Dict			  =    in_proj_bias[:config.hidden_size]
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    in_proj_weight[hidden_size : hidden_size * 2, :]
														_SCREAMING_SNAKE_CASE		:						Any			  =    in_proj_bias[hidden_size : hidden_size * 2]
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    in_proj_weight[-hidden_size :, :]
														_SCREAMING_SNAKE_CASE		:						Any			  =    in_proj_bias[-hidden_size :]
							# fmt: on



def      lowerCamelCase_()->    torch.Tensor:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,	stream=__SCREAMING_SNAKE_CASE  ).raw  )
							return im



@torch.no_grad()
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = False  )->    int:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_maskformer_config(__SCREAMING_SNAKE_CASE  )

							# load original state_dict
							with open(__SCREAMING_SNAKE_CASE     ,	"""rb"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pickle.load(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    data["""model"""]

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

							# rename keys
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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_swin_q_k_v(__SCREAMING_SNAKE_CASE     ,	config.backbone_config  )
							read_in_decoder_q_k_v(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

							# load 🤗 model
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    MaskFormerForInstanceSegmentation(__SCREAMING_SNAKE_CASE  )
							model.eval()

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

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

							# verify results
							_SCREAMING_SNAKE_CASE		:						str			  =    prepare_img()
							if "vistas" in model_name:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    65
							elif "cityscapes" in model_name:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    65_535
							else:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    255
							_SCREAMING_SNAKE_CASE		:						str			  =    True if """ade""" in model_name else False
							_SCREAMING_SNAKE_CASE		:						int			  =    MaskFormerImageProcessor(ignore_index=__SCREAMING_SNAKE_CASE     ,	reduce_labels=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**__SCREAMING_SNAKE_CASE  )

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

							if model_name == "maskformer-swin-tiny-ade":
														_SCREAMING_SNAKE_CASE		:						int			  =    torch.tensor(
														    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]  )
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1e-4  )
							print("""Looks ok!"""  )

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""  )
														Path(__SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE  )
														model.save_pretrained(__SCREAMING_SNAKE_CASE  )
														image_processor.save_pretrained(__SCREAMING_SNAKE_CASE  )

							if push_to_hub:
														print("""Pushing model and image processor to the hub..."""  )
														model.push_to_hub(F"""nielsr/{model_name}"""  )
														image_processor.push_to_hub(F"""nielsr/{model_name}"""  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''maskformer-swin-tiny-ade''',
			    type=str,
			    help=('''Name of the MaskFormer model you\'d like to convert''',),
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
			    type=str,
			    help='''Path to the original state dict (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

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



"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"   ,       "False" ) ) is not True   ,       reason="Skipping test because should only be run when releasing minor transformers version"   ,       )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											if self.framework == "pytorch":
																		subprocess.run(
																		    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()      ,    encoding="""utf-8"""      ,    check=_A      ,    )
											assert hasattr(self      ,    """env""")





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											return HuggingFace(
											    entry_point=self.script      ,    source_dir=self.env.test_path      ,    role=self.env.role      ,    image_uri=self.env.image_uri      ,    base_job_name=f"""{self.env.base_job_name}-single"""      ,    instance_count=_A      ,    instance_type=self.instance_type      ,    debugger_hook_config=_A      ,    hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,    metric_definitions=self.env.metric_definitions      ,    py_version="""py36"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											_SCREAMING_SNAKE_CASE		:						Any			  =    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

											# extract kpis
											_SCREAMING_SNAKE_CASE		:						Any			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											_SCREAMING_SNAKE_CASE		:						int			  =    (
											    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""      ,    9_9_9_9_9_9)
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy)
											assert all(t <= self.results["""eval_loss"""] for t in eval_loss)

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json"""      ,    """w""") as outfile:
																		json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}      ,    _A)


