



import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCAmelCase_							:      Dict      =  "sshleifer/mar_enro_6_3_student"






class      UpperCamelCase    (						UpperCAmelCase_     ):



	def 			__A							(       self		):
							super().setUp()

							A__							=       cached_path(
							    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"			,	extract_compressed_file=__lowercase			,	)
							A__							=       F"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""



	@slow
	@require_torch_gpu
	def 			__A							(       self		):
							MarianMTModel.from_pretrained(__lowercase		)



	@slow
	@require_torch_gpu
	def 			__A							(       self		):
							A__							=       {
							    '''$MAX_LEN''': 64,
							    '''$BS''': 64,
							    '''$GAS''': 1,
							    '''$ENRO_DIR''': self.data_dir,
							    '''facebook/mbart-large-cc25''': MARIAN_MODEL,
							    # "val_check_interval=0.25": "val_check_interval=1.0",
							    '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
							    '''--num_train_epochs 6''': '''--num_train_epochs 1''',
							}

							# Clean up bash script
							A__							=       (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split("finetune.py"		)[1].strip()
							A__							=       bash_script.replace("\\\n"			,	""		).strip().replace("\"$@\""			,	""		)
							for k, v in env_vars_to_replace.items():
													A__							=       bash_script.replace(__lowercase			,	str(__lowercase		)		)
							A__							=       self.get_auto_remove_tmp_dir()

							# bash_script = bash_script.replace("--fp16 ", "")
							A__							=       F"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
							# XXX: args.gpus > 1 : handle multi_gpu in the future

							A__							=       ['''finetune.py'''] + bash_script.split() + args
							with patch.object(__lowercase			,	"argv"			,	__lowercase		):
													A__							=       argparse.ArgumentParser()
													A__							=       pl.Trainer.add_argparse_args(__lowercase		)
													A__							=       SummarizationModule.add_model_specific_args(__lowercase			,	os.getcwd()		)
													A__							=       parser.parse_args()
													A__							=       main(__lowercase		)

							# Check metrics
							A__							=       load_json(model.metrics_save_path		)
							A__							=       metrics['''val'''][0]
							A__							=       metrics['''val'''][-1]
							self.assertEqual(len(metrics["val"]		)			,	(args.max_epochs / args.val_check_interval)		)
							assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""]			,	__lowercase		)

							self.assertGreater(last_step_stats["val_avg_gen_time"]			,	0.01		)
							# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
							self.assertLessEqual(last_step_stats["val_avg_gen_time"]			,	1.0		)

							# test learning requirements:

							# 1. BLEU improves over the course of training by more than 2 pts
							self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]			,	2		)

							# 2. BLEU finishes above 17
							self.assertGreater(last_step_stats["val_avg_bleu"]			,	17		)

							# 3. test BLEU and val BLEU within ~1.1 pt.
							self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]		)			,	1.1		)

							# check lightning ckpt can be loaded and has a reasonable statedict
							A__							=       os.listdir(__lowercase		)
							A__							=       [x for x in contents if x.endswith(".ckpt"		)][0]
							A__							=       os.path.join(args.output_dir			,	__lowercase		)
							A__							=       torch.load(__lowercase			,	map_location="cpu"		)
							A__							=       '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
							assert expected_key in ckpt["state_dict"]
							assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

							# TODO: turn on args.do_predict when PL bug fixed.
							if args.do_predict:
													A__							=       {os.path.basename(__lowercase		) for p in contents}
													assert "test_generations.txt" in contents
													assert "test_results.txt" in contents
													# assert len(metrics["val"]) ==  desired_n_evals
													assert len(metrics["test"]		) == 1






class      UpperCamelCase    (						UpperCAmelCase_     ):



	@timeout_decorator.timeout(600		)
	@slow
	@require_torch_gpu
	def 			__A							(       self		):
							A__							=       F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
							A__							=       {
							    '''--fp16_opt_level=O1''': '''''',
							    '''$MAX_LEN''': 128,
							    '''$BS''': 16,
							    '''$GAS''': 1,
							    '''$ENRO_DIR''': data_dir,
							    '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
							    '''val_check_interval=0.25''': '''val_check_interval=1.0''',
							}

							# Clean up bash script
							A__							=       (
							    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split("distillation.py"		)[1].strip()
							)
							A__							=       bash_script.replace("\\\n"			,	""		).strip().replace("\"$@\""			,	""		)
							A__							=       bash_script.replace("--fp16 "			,	" "		)

							for k, v in env_vars_to_replace.items():
													A__							=       bash_script.replace(__lowercase			,	str(__lowercase		)		)
							A__							=       self.get_auto_remove_tmp_dir()
							A__							=       bash_script.replace("--fp16"			,	""		)
							A__							=       6
							A__							=       (
							    ['''distillation.py''']
							    + bash_script.split()
							    + [
							        F"""--output_dir={output_dir}""",
							        '''--gpus=1''',
							        '''--learning_rate=1e-3''',
							        F"""--num_train_epochs={epochs}""",
							        '''--warmup_steps=10''',
							        '''--val_check_interval=1.0''',
							        '''--do_predict''',
							    ]
							)
							with patch.object(__lowercase			,	"argv"			,	__lowercase		):
													A__							=       argparse.ArgumentParser()
													A__							=       pl.Trainer.add_argparse_args(__lowercase		)
													A__							=       SummarizationDistiller.add_model_specific_args(__lowercase			,	os.getcwd()		)
													A__							=       parser.parse_args()
													# assert args.gpus == gpus THIS BREAKS for multi_gpu

													A__							=       distill_main(__lowercase		)

							# Check metrics
							A__							=       load_json(model.metrics_save_path		)
							A__							=       metrics['''val'''][0]
							A__							=       metrics['''val'''][-1]
							assert len(metrics["val"]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

							assert last_step_stats["val_avg_gen_time"] >= 0.01

							assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
							assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
							assert isinstance(last_step_stats[F"""val_avg_{model.val_metric}"""]			,	__lowercase		)

							# check lightning ckpt can be loaded and has a reasonable statedict
							A__							=       os.listdir(__lowercase		)
							A__							=       [x for x in contents if x.endswith(".ckpt"		)][0]
							A__							=       os.path.join(args.output_dir			,	__lowercase		)
							A__							=       torch.load(__lowercase			,	map_location="cpu"		)
							A__							=       '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
							assert expected_key in ckpt["state_dict"]
							assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

							# TODO: turn on args.do_predict when PL bug fixed.
							if args.do_predict:
													A__							=       {os.path.basename(__lowercase		) for p in contents}
													assert "test_generations.txt" in contents
													assert "test_results.txt" in contents
													# assert len(metrics["val"]) ==  desired_n_evals
													assert len(metrics["test"]		) == 1










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()
UpperCAmelCase_							:      str      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str    )->       List[str]:





						"""simple docstring"""





						A__							=       SwinConfig.from_pretrained(
						    "microsoft/swin-tiny-patch4-window7-224"			,							out_features=["stage1", "stage2", "stage3", "stage4"]    )
						A__							=       MaskFormerConfig(backbone_config=_A    )

						A__							=       "huggingface/label-files"
						if "ade20k-full" in model_name:
												# this should be ok
												A__							=       847
												A__							=       "maskformer-ade20k-full-id2label.json"
						elif "ade" in model_name:
												# this should be ok
												A__							=       150
												A__							=       "ade20k-id2label.json"
						elif "coco-stuff" in model_name:
												# this should be ok
												A__							=       171
												A__							=       "maskformer-coco-stuff-id2label.json"
						elif "coco" in model_name:
												# TODO
												A__							=       133
												A__							=       "coco-panoptic-id2label.json"
						elif "cityscapes" in model_name:
												# this should be ok
												A__							=       19
												A__							=       "cityscapes-id2label.json"
						elif "vistas" in model_name:
												# this should be ok
												A__							=       65
												A__							=       "mapillary-vistas-id2label.json"

						A__							=       json.load(open(hf_hub_download(_A			,							_A			,							repo_type="dataset"    )			,							"r"    )    )
						A__							=       {int(_A    ): v for k, v in idalabel.items()}

						return config
def  UpperCamelCase						(       _A			:				Tuple    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       []
						# 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  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				List[str]    )->       Any:





						"""simple docstring"""





						A__							=       dct.pop(_A    )
						A__							=       val
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				List[str]    )->       Dict:





						"""simple docstring"""





						A__							=       [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
						for i in range(len(backbone_config.depths    )    ):
												A__							=       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)
																		A__							=       state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""    )
																		A__							=       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
																		A__							=       in_proj_weight[:dim, :]
																		A__							=       in_proj_bias[: dim]
																		A__							=       in_proj_weight[
																		    dim : dim * 2, :
																		]
																		A__							=       in_proj_bias[
																		    dim : dim * 2
																		]
																		A__							=       in_proj_weight[
																		    -dim :, :
																		]
																		A__							=       in_proj_bias[-dim :]
																		# fmt: on
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Optional[int]    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       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)
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
												A__							=       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
												A__							=       in_proj_weight[: hidden_size, :]
												A__							=       in_proj_bias[:config.hidden_size]
												A__							=       in_proj_weight[hidden_size : hidden_size * 2, :]
												A__							=       in_proj_bias[hidden_size : hidden_size * 2]
												A__							=       in_proj_weight[-hidden_size :, :]
												A__							=       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)
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
												A__							=       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
												A__							=       in_proj_weight[: hidden_size, :]
												A__							=       in_proj_bias[:config.hidden_size]
												A__							=       in_proj_weight[hidden_size : hidden_size * 2, :]
												A__							=       in_proj_bias[hidden_size : hidden_size * 2]
												A__							=       in_proj_weight[-hidden_size :, :]
												A__							=       in_proj_bias[-hidden_size :]
						# fmt: on
def  UpperCamelCase						(       )->       torch.Tensor:





						"""simple docstring"""





						A__							=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__							=       Image.open(requests.get(_A			,							stream=_A    ).raw    )
						return im
@torch.no_grad()
def  UpperCamelCase						(       _A			:				str			,							_A			:				str			,							_A			:				str			,							_A			:				bool = False    )->       Tuple:





						"""simple docstring"""





						A__							=       get_maskformer_config(_A    )

						# load original state_dict
						with open(_A			,							"rb"    ) as f:
												A__							=       pickle.load(_A    )
						A__							=       data["model"]

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

						# rename keys
						A__							=       create_rename_keys(_A    )
						for src, dest in rename_keys:
												rename_key(_A			,							_A			,							_A    )
						read_in_swin_q_k_v(_A			,							config.backbone_config    )
						read_in_decoder_q_k_v(_A			,							_A    )

						# update to torch tensors
						for key, value in state_dict.items():
												A__							=       torch.from_numpy(_A    )

						# load 🤗 model
						A__							=       MaskFormerForInstanceSegmentation(_A    )
						model.eval()

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

						A__						,       A__							=       model.load_state_dict(_A			,							strict=_A    )
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(_A    ) == 0, f"""Unexpected keys: {unexpected_keys}"""

						# verify results
						A__							=       prepare_img()
						if "vistas" in model_name:
												A__							=       65
						elif "cityscapes" in model_name:
												A__							=       65535
						else:
												A__							=       255
						A__							=       True if "ade" in model_name else False
						A__							=       MaskFormerImageProcessor(ignore_index=_A			,							reduce_labels=_A    )

						A__							=       image_processor(_A			,							return_tensors="pt"    )

						A__							=       model(**_A    )

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

						if model_name == "maskformer-swin-tiny-ade":
												A__							=       torch.tensor(
												    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]    )
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,							_A			,							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(_A    ).mkdir(exist_ok=_A    )
												model.save_pretrained(_A    )
												image_processor.save_pretrained(_A    )

						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__":
						UpperCAmelCase_							:      str      =  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."
						)

						UpperCAmelCase_							:      Tuple      =  parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)





