
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


lowercase__		:Optional[Any]   =    (
    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)


lowercase__		:str   =    logging.get_logger(__name__)  # pylint: disable=invalid-name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''https://pypi.org/pypi/diffusers/json'''
					lowercase							=						json.loads(request.urlopen(lowerCAmelCase__		).read()		)['''releases'''].keys()
					return sorted(lowerCAmelCase__ ,			key=lambda lowerCAmelCase__		: version.Version(lowerCAmelCase__		)		)



def        UpperCamelCase    (							):
					'''simple docstring'''
					# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
					if HF_MODULES_CACHE in sys.path:
										return

					sys.path.append(lowerCAmelCase__		)
					os.makedirs(lowerCAmelCase__ ,			exist_ok=lowerCAmelCase__		)
					lowercase							=						Path(lowerCAmelCase__		) / '''__init__.py'''
					if not init_path.exists():
										init_path.touch()



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					init_hf_modules()
					lowercase							=						Path(lowerCAmelCase__		) / name
					# If the parent module does not exist yet, recursively create it.
					if not dynamic_module_path.parent.exists():
										create_dynamic_module(dynamic_module_path.parent		)
					os.makedirs(lowerCAmelCase__ ,			exist_ok=lowerCAmelCase__		)
					lowercase							=						dynamic_module_path / '''__init__.py'''
					if not init_path.exists():
										init_path.touch()



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					with open(lowerCAmelCase__ ,			'''r''' ,			encoding='''utf-8'''		) as f:
										lowercase							=						f.read()

					# Imports of the form `import .xxx`
					lowercase							=						re.findall('''^\s*import\s+\.(\S+)\s*$''' ,			lowerCAmelCase__ ,			flags=re.MULTILINE		)
					# Imports of the form `from .xxx import yyy`
					relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' ,			lowerCAmelCase__ ,			flags=re.MULTILINE		)
					# Unique-ify
					return list(set(lowerCAmelCase__		)		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						False
					lowercase							=						[module_file]
					lowercase							=						[]

					# Let's recurse through all relative imports
					while not no_change:
										lowercase							=						[]
										for f in files_to_check:
															new_imports.extend(get_relative_imports(lowerCAmelCase__		)		)

										lowercase							=						Path(lowerCAmelCase__		).parent
										lowercase							=						[str(module_path / m		) for m in new_imports]
										lowercase							=						[f for f in new_import_files if f not in all_relative_imports]
										lowercase							=						[f'{f}.py' for f in new_import_files]

										lowercase							=						len(lowerCAmelCase__		) == 0
										all_relative_imports.extend(lowerCAmelCase__		)

					return all_relative_imports



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					with open(lowerCAmelCase__ ,			'''r''' ,			encoding='''utf-8'''		) as f:
										lowercase							=						f.read()

					# Imports of the form `import xxx`
					lowercase							=						re.findall('''^\s*import\s+(\S+)\s*$''' ,			lowerCAmelCase__ ,			flags=re.MULTILINE		)
					# Imports of the form `from xxx import yyy`
					imports += re.findall('''^\s*from\s+(\S+)\s+import''' ,			lowerCAmelCase__ ,			flags=re.MULTILINE		)
					# Only keep the top-level module
					lowercase							=						[imp.split('''.'''		)[0] for imp in imports if not imp.startswith('''.'''		)]

					# Unique-ify and test we got them all
					lowercase							=						list(set(lowerCAmelCase__		)		)
					lowercase							=						[]
					for imp in imports:
										try:
															importlib.import_module(lowerCAmelCase__		)
										except ImportError:
															missing_packages.append(lowerCAmelCase__		)

					if len(lowerCAmelCase__		) > 0:
										raise ImportError(
										    '''This modeling file requires the following packages that were not found in your environment: '''
										    f'{", ".join(lowerCAmelCase__		)}. Run `pip install {" ".join(lowerCAmelCase__		)}`'		)

					return get_relative_imports(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						module_path.replace(os.path.sep ,			'''.'''		)
					lowercase							=						importlib.import_module(lowerCAmelCase__		)

					if class_name is None:
										return find_pipeline_class(lowerCAmelCase__		)
					return getattr(lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					from ..pipelines import DiffusionPipeline

					lowercase							=						dict(inspect.getmembers(lowerCAmelCase__ ,			inspect.isclass		)		)

					lowercase							=						None
					for cls_name, cls in cls_members.items():
										if (
										    cls_name != DiffusionPipeline.__name__
										    and issubclass(cls ,			lowerCAmelCase__		)
										    and cls.__module__.split('''.'''		)[0] != "diffusers"
										):
															if pipeline_class is not None:
																				raise ValueError(
																				    f'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
																				    f' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
																				    f' {loaded_module}.'		)
															lowercase							=						cls

					return pipeline_class



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = False ,			lowerCAmelCase__ = False ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = False ,			):
					'''simple docstring'''
					lowercase							=						str(lowerCAmelCase__		)

					lowercase							=						os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)

					if os.path.isfile(lowerCAmelCase__		):
										lowercase							=						module_file_or_url
										lowercase							=						'''local'''
					elif pretrained_model_name_or_path.count('''/'''		) == 0:
										lowercase							=						get_diffusers_versions()
										# cut ".dev0"
										lowercase							=						'''v''' + '''.'''.join(__version__.split('''.'''		)[:3]		)

										# retrieve github version that matches
										if revision is None:
															lowercase							=						latest_version if latest_version[1:] in available_versions else '''main'''
															logger.info(f'Defaulting to latest_version: {revision}.'		)
										elif revision in available_versions:
															lowercase							=						f'v{revision}'
										elif revision == "main":
															lowercase							=						revision
										else:
															raise ValueError(
															    f'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
															    f' {", ".join(available_versions + ["main"]		)}.'		)

										# community pipeline on GitHub
										lowercase							=						COMMUNITY_PIPELINES_URL.format(revision=lowerCAmelCase__ ,			pipeline=lowerCAmelCase__		)
										try:
															lowercase							=						cached_download(
															    lowerCAmelCase__ ,			cache_dir=lowerCAmelCase__ ,			force_download=lowerCAmelCase__ ,			proxies=lowerCAmelCase__ ,			resume_download=lowerCAmelCase__ ,			local_files_only=lowerCAmelCase__ ,			use_auth_token=lowerCAmelCase__ ,			)
															lowercase							=						'''git'''
															lowercase							=						pretrained_model_name_or_path + '''.py'''
										except EnvironmentError:
															logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'		)
															raise
					else:
										try:
															# Load from URL or cache if already cached
															lowercase							=						hf_hub_download(
															    lowerCAmelCase__ ,			lowerCAmelCase__ ,			cache_dir=lowerCAmelCase__ ,			force_download=lowerCAmelCase__ ,			proxies=lowerCAmelCase__ ,			resume_download=lowerCAmelCase__ ,			local_files_only=lowerCAmelCase__ ,			use_auth_token=lowerCAmelCase__ ,			)
															lowercase							=						os.path.join('''local''' ,			'''--'''.join(pretrained_model_name_or_path.split('''/'''		)		)		)
										except EnvironmentError:
															logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'		)
															raise

    # Check we have all the requirements in our environment
					lowercase							=						check_imports(lowerCAmelCase__		)

					# Now we move the module inside our cached dynamic modules.
					lowercase							=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
					create_dynamic_module(lowerCAmelCase__		)
					lowercase							=						Path(lowerCAmelCase__		) / full_submodule
					if submodule == "local" or submodule == "git":
										# We always copy local files (we could hash the file to see if there was a change, and give them the name of
										# that hash, to only copy when there is a modification but it seems overkill for now).
										# The only reason we do the copy is to avoid putting too many folders in sys.path.
										shutil.copy(lowerCAmelCase__ ,			submodule_path / module_file		)
										for module_needed in modules_needed:
															lowercase							=						f'{module_needed}.py'
															shutil.copy(os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		) ,			submodule_path / module_needed		)
					else:
										# Get the commit hash
										# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
										if isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
															lowercase							=						use_auth_token
										elif use_auth_token is True:
															lowercase							=						HfFolder.get_token()
										else:
															lowercase							=						None

										lowercase							=						model_info(lowerCAmelCase__ ,			revision=lowerCAmelCase__ ,			token=lowerCAmelCase__		).sha

										# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
										# benefit of versioning.
										lowercase							=						submodule_path / commit_hash
										lowercase							=						full_submodule + os.path.sep + commit_hash
										create_dynamic_module(lowerCAmelCase__		)

										if not (submodule_path / module_file).exists():
															shutil.copy(lowerCAmelCase__ ,			submodule_path / module_file		)
										# Make sure we also have every file with relative
										for module_needed in modules_needed:
															if not (submodule_path / module_needed).exists():
																				get_cached_module_file(
																				    lowerCAmelCase__ ,			f'{module_needed}.py' ,			cache_dir=lowerCAmelCase__ ,			force_download=lowerCAmelCase__ ,			resume_download=lowerCAmelCase__ ,			proxies=lowerCAmelCase__ ,			use_auth_token=lowerCAmelCase__ ,			revision=lowerCAmelCase__ ,			local_files_only=lowerCAmelCase__ ,			)
					return os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = False ,			lowerCAmelCase__ = False ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = False ,			**lowerCAmelCase__ ,			):
					'''simple docstring'''
					lowercase							=						get_cached_module_file(
					    lowerCAmelCase__ ,			lowerCAmelCase__ ,			cache_dir=lowerCAmelCase__ ,			force_download=lowerCAmelCase__ ,			resume_download=lowerCAmelCase__ ,			proxies=lowerCAmelCase__ ,			use_auth_token=lowerCAmelCase__ ,			revision=lowerCAmelCase__ ,			local_files_only=lowerCAmelCase__ ,			)
					return get_class_in_module(lowerCAmelCase__ ,			final_module.replace('''.py''' ,			''''''		)		)






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:str   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''huggingface/label-files'''
					lowercase							=						'''imagenet-1k-id2label.json'''
					lowercase							=						json.load(open(hf_hub_download(lowerCAmelCase__ ,			lowerCAmelCase__ ,			repo_type='''dataset'''		) ,			'''r'''		)		)
					lowercase							=						{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
					lowercase							=						{v: k for k, v in idalabel.items()}

					lowercase							=						'''std_conv''' if '''bit''' in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					lowercase							=						BitConfig(
					    conv_layer=lowerCAmelCase__ ,			num_labels=1000 ,			idalabel=lowerCAmelCase__ ,			labelaid=lowerCAmelCase__ ,			)

					return config



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "stem.conv" in name:
										lowercase							=						name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''		)
					if "blocks" in name:
										lowercase							=						name.replace('''blocks''' ,			'''layers'''		)
					if "head.fc" in name:
										lowercase							=						name.replace('''head.fc''' ,			'''classifier.1'''		)
					if name.startswith('''norm'''		):
										lowercase							=						'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										lowercase							=						'''bit.encoder.''' + name

					return name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)
					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_config(lowerCAmelCase__		)

					# load original model from timm
					lowercase							=						create_model(lowerCAmelCase__ ,			pretrained=lowerCAmelCase__		)
					timm_model.eval()

					# load state_dict of original model
					lowercase							=						timm_model.state_dict()
					for key in state_dict.copy().keys():
										lowercase							=						state_dict.pop(lowerCAmelCase__		)
										lowercase							=						val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					lowercase							=						BitForImageClassification(lowerCAmelCase__		)
					model.eval()
					model.load_state_dict(lowerCAmelCase__		)

					# create image processor
					lowercase							=						create_transform(**resolve_data_config({} ,			model=lowerCAmelCase__		)		)
					lowercase							=						transform.transforms

					lowercase							=						{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					lowercase							=						BitImageProcessor(
					    do_resize=lowerCAmelCase__ ,			size={'''shortest_edge''': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=lowerCAmelCase__ ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,			do_normalize=lowerCAmelCase__ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

					lowercase							=						prepare_img()
					lowercase							=						transform(lowerCAmelCase__		).unsqueeze(0		)
					lowercase							=						processor(lowerCAmelCase__ ,			return_tensors='''pt'''		).pixel_values

					# verify pixel values
					assert torch.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# verify logits
					with torch.no_grad():
										lowercase							=						model(lowerCAmelCase__		)
										lowercase							=						outputs.logits

					print('''Logits:''' ,			logits[0, :3]		)
					print('''Predicted class:''' ,			model.config.idalabel[logits.argmax(-1		).item()]		)
					lowercase							=						timm_model(lowerCAmelCase__		)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(lowerCAmelCase__ ,			outputs.logits ,			atol=1E-3		)
					print('''Looks ok!'''		)

					if pytorch_dump_folder_path is not None:
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'		)
										model.save_pretrained(lowerCAmelCase__		)
										processor.save_pretrained(lowerCAmelCase__		)

					if push_to_hub:
										print(f'Pushing model {model_name} and processor to the hub'		)
										model.push_to_hub(f'ybelkada/{model_name}'		)
										processor.push_to_hub(f'ybelkada/{model_name}'		)


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="resnetv2_50x1_bitm",
							    type=str,
							    help="Name of the BiT timm model you'd like to convert.",
							)
							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 to push the model to the hub.",
							)

							lowercase__		:List[str]   =    parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




