

import collections
import os
import re
from pathlib import Path


a__												=						'''src/transformers'''


# Matches is_xxx_available()
a__												=						re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a__												=						re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
a__												=						re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
a__												=						re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
a__												=						re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
a__												=						re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
a__												=						re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
a__												=						re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
a__												=						re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
a__												=						re.compile(R'''^\s*try:''')
# Catches a line with else:
a__												=						re.compile(R'''^\s*else:''')



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Optional[int]:

					"""simple docstring"""

					if _re_test_backend.search(__a      ) is None:
										return None
					_a      :       List[str]   =						[b[0] for b in _re_backend.findall(__a      )]
					backends.sort()
					return "_and_".join(__a      )



def   __UpperCAmelCase					(      __a :     Optional[int]      )     ->	int:

					"""simple docstring"""

					with open(__a						,'''r'''						,encoding='''utf-8'''						,newline='''\n'''      ) as f:
										_a      :       List[str]   =						f.readlines()

					_a      :       List[str]   =						0
					while line_index < len(__a      ) and not lines[line_index].startswith('''_import_structure = {'''      ):
										line_index += 1

					# If this is a traditional init, just return.
					if line_index >= len(__a      ):
										return None

					# First grab the objects without a specific backend in _import_structure
					_a      :       List[Any]   =						[]
					while not lines[line_index].startswith('''if TYPE_CHECKING'''      ) and find_backend(lines[line_index]      ) is None:
										_a      :       str   =						lines[line_index]
										# If we have everything on a single line, let's deal with it.
										if _re_one_line_import_struct.search(__a      ):
															_a      :       Optional[Any]   =						_re_one_line_import_struct.search(__a      ).groups()[0]
															_a      :       Optional[int]   =						re.findall(R'''\[([^\]]+)\]'''						,__a      )
															for imp in imports:
																				objects.extend([obj[1:-1] for obj in imp.split(''', '''      )]      )
															line_index += 1
															continue
										_a      :       Optional[Any]   =						_re_import_struct_key_value.search(__a      )
										if single_line_import_search is not None:
															_a      :       List[Any]   =						[obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''      ) if len(__a      ) > 0]
															objects.extend(__a      )
										elif line.startswith(''' ''' * 8 + '''"'''      ):
															objects.append(line[9:-3]      )
										line_index += 1

					_a      :       int   =						{'''none''': objects}
					# Let's continue with backend-specific objects in _import_structure
					while not lines[line_index].startswith('''if TYPE_CHECKING'''      ):
										# If the line is an if not is_backend_available, we grab all objects associated.
										_a      :       Optional[int]   =						find_backend(lines[line_index]      )
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]      ) is None:
															_a      :       int   =						None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]      ) is None:
																				line_index += 1

															line_index += 1

															_a      :       str   =						[]
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(''' ''' * 4      ):
																				_a      :       str   =						lines[line_index]
																				if _re_import_struct_add_one.search(__a      ) is not None:
																									objects.append(_re_import_struct_add_one.search(__a      ).groups()[0]      )
																				elif _re_import_struct_add_many.search(__a      ) is not None:
																									_a      :       Any   =						_re_import_struct_add_many.search(__a      ).groups()[0].split(''', '''      )
																									_a      :       List[Any]   =						[obj[1:-1] for obj in imports if len(__a      ) > 0]
																									objects.extend(__a      )
																				elif _re_between_brackets.search(__a      ) is not None:
																									_a      :       List[Any]   =						_re_between_brackets.search(__a      ).groups()[0].split(''', '''      )
																									_a      :       Dict   =						[obj[1:-1] for obj in imports if len(__a      ) > 0]
																									objects.extend(__a      )
																				elif _re_quote_object.search(__a      ) is not None:
																									objects.append(_re_quote_object.search(__a      ).groups()[0]      )
																				elif line.startswith(''' ''' * 8 + '''"'''      ):
																									objects.append(line[9:-3]      )
																				elif line.startswith(''' ''' * 12 + '''"'''      ):
																									objects.append(line[13:-3]      )
																				line_index += 1

															_a      :       Any   =						objects
										else:
															line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
					_a      :       str   =						[]
					while (
					    line_index < len(__a      )
					    and find_backend(lines[line_index]      ) is None
					    and not lines[line_index].startswith('''else'''      )
					):
										_a      :       Union[str, Any]   =						lines[line_index]
										_a      :       Optional[int]   =						_re_import.search(__a      )
										if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(''', '''      )      )
										elif line.startswith(''' ''' * 8      ):
															objects.append(line[8:-2]      )
										line_index += 1

					_a      :       Optional[Any]   =						{'''none''': objects}
					# Let's continue with backend-specific objects
					while line_index < len(__a      ):
										# If the line is an if is_backend_available, we grab all objects associated.
										_a      :       int   =						find_backend(lines[line_index]      )
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]      ) is None:
															_a      :       Optional[int]   =						None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]      ) is None:
																				line_index += 1

															line_index += 1

															_a      :       str   =						[]
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(''' ''' * 8      ):
																				_a      :       Tuple   =						lines[line_index]
																				_a      :       Optional[Any]   =						_re_import.search(__a      )
																				if single_line_import_search is not None:
																									objects.extend(single_line_import_search.groups()[0].split(''', '''      )      )
																				elif line.startswith(''' ''' * 12      ):
																									objects.append(line[12:-2]      )
																				line_index += 1

															_a      :       Optional[int]   =						objects
										else:
															line_index += 1

					return import_dict_objects, type_hint_objects



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	Tuple:

					"""simple docstring"""

					def find_duplicates(__a :     str      ):
										return [k for k, v in collections.Counter(__a      ).items() if v > 1]

					if list(import_dict_objects.keys()      ) != list(type_hint_objects.keys()      ):
										return ["Both sides of the init do not have the same backends!"]

					_a      :       Union[str, Any]   =						[]
					for key in import_dict_objects.keys():
										_a      :       Tuple   =						find_duplicates(import_dict_objects[key]      )
										if duplicate_imports:
															errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""      )
										_a      :       Tuple   =						find_duplicates(type_hint_objects[key]      )
										if duplicate_type_hints:
															errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""      )

										if sorted(set(import_dict_objects[key]      )      ) != sorted(set(type_hint_objects[key]      )      ):
															_a      :       List[Any]   =						'''base imports''' if key == '''none''' else F"""{key} backend"""
															errors.append(F"""Differences for {name}:"""      )
															for a in type_hint_objects[key]:
																				if a not in import_dict_objects[key]:
																									errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""      )
															for a in import_dict_objects[key]:
																				if a not in type_hint_objects[key]:
																									errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""      )
					return errors



def   __UpperCAmelCase					(      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       List[str]   =						[]
					for root, _, files in os.walk(__a      ):
										if "__init__.py" in files:
															_a      :       Optional[Any]   =						os.path.join(__a						,'''__init__.py'''      )
															_a      :       Union[str, Any]   =						parse_init(__a      )
															if objects is not None:
																				_a      :       Optional[int]   =						analyze_results(*__a      )
																				if len(__a      ) > 0:
																									_a      :       int   =						F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																									failures.append('''\n'''.join(__a      )      )
					if len(__a      ) > 0:
										raise ValueError('''\n\n'''.join(__a      )      )



def   __UpperCAmelCase					(      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Dict   =						[]
					for path, directories, files in os.walk(__a      ):
										for folder in directories:
															# Ignore private modules
															if folder.startswith('''_'''      ):
																				directories.remove(__a      )
																				continue
															# Ignore leftovers from branches (empty folders apart from pycache)
															if len(list((Path(__a      ) / folder).glob('''*.py'''      )      )      ) == 0:
																				continue
															_a      :       Optional[Any]   =						str((Path(__a      ) / folder).relative_to(__a      )      )
															_a      :       int   =						short_path.replace(os.path.sep						,'''.'''      )
															submodules.append(__a      )
										for fname in files:
															if fname == "__init__.py":
																				continue
															_a      :       List[Any]   =						str((Path(__a      ) / fname).relative_to(__a      )      )
															_a      :       Dict   =						short_path.replace('''.py'''						,''''''      ).replace(os.path.sep						,'''.'''      )
															if len(submodule.split('''.'''      )      ) == 1:
																				submodules.append(__a      )
					return submodules


a__												=						[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]



def   __UpperCAmelCase					(      )     ->	Optional[int]:

					"""simple docstring"""

					from transformers.utils import direct_transformers_import

					_a      :       str   =						direct_transformers_import(__a      )

					_a      :       Dict   =						set(transformers._import_structure.keys()      )
					# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
					# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
					# (potentiall re-) add them.
					with open(os.path.join(__a						,'''__init__.py'''      )						,'''r'''      ) as f:
										_a      :       int   =						f.read()
					import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]'''						,__a      )      )      )

					_a      :       Optional[Any]   =						[
					    module
					    for module in get_transformers_submodules()
					    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
					]

					if len(__a      ) > 0:
										_a      :       List[Any]   =						'''\n'''.join(F"""- {module}""" for module in module_not_registered      )
										raise ValueError(
										    '''The following submodules are not properly registed in the main init of Transformers:\n'''
										    F"""{list_of_modules}\n"""
										    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''      )


if __name__ == "__main__":
			check_all_inits()
			check_submodules()






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()
a__												=						logging.get_logger(__name__)



def   __UpperCAmelCase					(      __a :     str      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Tuple   =						SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224'''						,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']      )
					_a      :       Dict   =						MaskFormerConfig(backbone_config=__a      )

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

					_a      :       Optional[int]   =						json.load(open(hf_hub_download(__a						,__a						,repo_type='''dataset'''      )						,'''r'''      )      )
					_a      :       Tuple   =						{int(__a      ): v for k, v in idalabel.items()}

					return config



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[Any]   =						[]
					# 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 :     List[str]						,__a :     List[Any]						,__a :     Optional[Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       str   =						dct.pop(__a      )
					_a      :       str   =						val



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						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      :       Union[str, Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
										_a      :       List[Any]   =						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      :       Union[str, Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       List[Any]   =						in_proj_bias[:config.hidden_size]
										_a      :       Dict   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Any   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       Tuple   =						in_proj_weight[-hidden_size :, :]
										_a      :       List[Any]   =						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      :       List[Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
										_a      :       List[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
										_a      :       Optional[Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       Any   =						in_proj_bias[:config.hidden_size]
										_a      :       List[str]   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Optional[Any]   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       List[str]   =						in_proj_weight[-hidden_size :, :]
										_a      :       int   =						in_proj_bias[-hidden_size :]
					# fmt: on



def   __UpperCAmelCase					(      )     ->	torch.Tensor:

					"""simple docstring"""

					_a      :       str   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )
					return im



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     str						,__a :     str						,__a :     str						,__a :     bool = False      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						get_maskformer_config(__a      )

					# load original state_dict
					with open(__a						,'''rb'''      ) as f:
										_a      :       str   =						pickle.load(__a      )
					_a      :       Union[str, Any]   =						data['''model''']

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

					# rename keys
					_a      :       Any   =						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      :       Optional[int]   =						torch.from_numpy(__a      )

					# load 🤗 model
					_a      :       Dict   =						MaskFormerForInstanceSegmentation(__a      )
					model.eval()

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

					_a ,   _a      :       Tuple   =						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      :       Union[str, Any]   =						prepare_img()
					if "vistas" in model_name:
										_a      :       int   =						65
					elif "cityscapes" in model_name:
										_a      :       Tuple   =						65_535
					else:
										_a      :       str   =						255
					_a      :       Dict   =						True if '''ade''' in model_name else False
					_a      :       Optional[Any]   =						MaskFormerImageProcessor(ignore_index=__a						,reduce_labels=__a      )

					_a      :       Optional[Any]   =						image_processor(__a						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

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

					if model_name == "maskformer-swin-tiny-ade":
										_a      :       Union[str, Any]   =						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]						,__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__":
			a__												=						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.'''
			)

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



