




"""simple docstring"""






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCamelCase__						=			get_logger(__name__)


UpperCamelCase__						=			Path(__file__).parent / '''model_card_template.md'''
UpperCamelCase__						=			uuida().hex
UpperCamelCase__						=			os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__						=			os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__						=			HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''





def 							UpperCAmelCase		(		snake_case			:					Union[Dict, str, None] = None    ):
			_lowerCAmelCase:List[str]									=  F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += F'; torch/{_torch_version}'
			if is_flax_available():
						ua += F'; jax/{_jax_version}'
						ua += F'; flax/{_flax_version}'
			if is_onnx_available():
						ua += F'; onnxruntime/{_onnxruntime_version}'
			# CI will set this value to True
			if os.environ.get('''DIFFUSERS_IS_CI'''      ,       ''''''    ).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__    ):
						ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()    )
			elif isinstance(lowerCAmelCase__      ,       lowerCAmelCase__    ):
						ua += "; " + user_agent
			return ua





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					Optional[str] = None      ,       snake_case			:					Optional[str] = None    ):
			if token is None:
						_lowerCAmelCase:int									=  HfFolder.get_token()
			if organization is None:
						_lowerCAmelCase:List[Any]									=  whoami(lowerCAmelCase__    )['''name''']
						return F'{username}/{model_id}'
			else:
						return F'{organization}/{model_id}'





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					Optional[Any]    ):
			if not is_jinja_available():
						raise ValueError(
						    '''Modelcard rendering is based on Jinja templates.'''
						    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
						    ''' To install it, please run `pip install Jinja2`.'''    )

			if hasattr(lowerCAmelCase__      ,       '''local_rank'''    ) and args.local_rank not in [-1, 0]:
						return

			_lowerCAmelCase:List[str]									=  args.hub_token if hasattr(lowerCAmelCase__      ,       '''hub_token'''    ) else None
			_lowerCAmelCase:Dict									=  get_full_repo_name(lowerCAmelCase__      ,       token=lowerCAmelCase__    )

			_lowerCAmelCase:int									=  ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language='''en'''      ,       license='''apache-2.0'''      ,       library_name='''diffusers'''      ,       tags=[]      ,       datasets=args.dataset_name      ,       metrics=[]      ,       )      ,       template_path=lowerCAmelCase__      ,       model_name=lowerCAmelCase__      ,       repo_name=lowerCAmelCase__      ,       dataset_name=args.dataset_name if hasattr(lowerCAmelCase__      ,       '''dataset_name'''    ) else None      ,       learning_rate=args.learning_rate      ,       train_batch_size=args.train_batch_size      ,       eval_batch_size=args.eval_batch_size      ,       gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(lowerCAmelCase__      ,       '''gradient_accumulation_steps'''    ) else None
			    )      ,       adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__      ,       '''adam_beta1'''    ) else None      ,       adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__      ,       '''adam_beta2'''    ) else None      ,       adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase__      ,       '''adam_weight_decay'''    ) else None      ,       adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase__      ,       '''adam_epsilon'''    ) else None      ,       lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase__      ,       '''lr_scheduler'''    ) else None      ,       lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase__      ,       '''lr_warmup_steps'''    ) else None      ,       ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase__      ,       '''ema_inv_gamma'''    ) else None      ,       ema_power=args.ema_power if hasattr(lowerCAmelCase__      ,       '''ema_power'''    ) else None      ,       ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase__      ,       '''ema_max_decay'''    ) else None      ,       mixed_precision=args.mixed_precision      ,       )

			_lowerCAmelCase:str									=  os.path.join(args.output_dir      ,       '''README.md'''    )
			model_card.save(lowerCAmelCase__    )





def 							UpperCAmelCase		(		snake_case			:					Optional[str]      ,       snake_case			:					Optional[str] = None    ):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			_lowerCAmelCase:List[Any]									=  str(Path(lowerCAmelCase__    ).as_posix()    )
			_lowerCAmelCase:Optional[int]									=  re.search(R'''snapshots/([^/]+)/'''      ,       lowerCAmelCase__    )
			if search is None:
						return None
			_lowerCAmelCase:List[str]									=  search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase__    ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCamelCase__						=			os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
UpperCamelCase__						=			os.path.join(hf_cache_home, '''diffusers''')





def 							UpperCAmelCase		(		snake_case			:					Optional[str] = None      ,       snake_case			:					Optional[str] = None    ):
			if new_cache_dir is None:
						_lowerCAmelCase:List[str]									=  DIFFUSERS_CACHE
			if old_cache_dir is None:
						_lowerCAmelCase:Optional[int]									=  old_diffusers_cache

			_lowerCAmelCase:int									=  Path(lowerCAmelCase__    ).expanduser()
			_lowerCAmelCase:str									=  Path(lowerCAmelCase__    ).expanduser()
			for old_blob_path in old_cache_dir.glob('''**/blobs/*'''    ):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									_lowerCAmelCase:int									=  new_cache_dir / old_blob_path.relative_to(lowerCAmelCase__    )
									new_blob_path.parent.mkdir(parents=lowerCAmelCase__      ,       exist_ok=lowerCAmelCase__    )
									os.replace(lowerCAmelCase__      ,       lowerCAmelCase__    )
									try:
												os.symlink(lowerCAmelCase__      ,       lowerCAmelCase__    )
									except OSError:
												logger.warning(
												    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''    )
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCamelCase__						=			os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
				UpperCamelCase__						=			0
else:
				with open(cache_version_file) as f:
								try:
												UpperCamelCase__						=			int(f.read())
								except ValueError:
												UpperCamelCase__						=			0

if cache_version < 1:
				UpperCamelCase__						=			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
				if old_cache_is_not_empty:
								logger.warning(
								    '''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
								    '''existing cached models. This is a one-time operation, you can interrupt it or run it '''
								    '''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
								)
								try:
												move_cache()
								except Exception as e:
												UpperCamelCase__						=			'''\n'''.join(traceback.format_tb(e.__traceback__))
												logger.error(
												    F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
												    '''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
												    '''message and we will do our best to help.'''
												)

if cache_version < 1:
				try:
								os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
								with open(cache_version_file, '''w''') as f:
												f.write('''1''')
				except Exception:
								logger.warning(
								    F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
								    '''the directory exists and can be written to.'''
								)





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					Optional[str] = None    ):
			if variant is not None:
						_lowerCAmelCase:List[str]									=  weights_name.split('''.'''    )
						_lowerCAmelCase:Tuple									=  splits[:-1] + [variant] + splits[-1:]
						_lowerCAmelCase:Any									=  '''.'''.join(lowerCAmelCase__    )

			return weights_name





def 							UpperCAmelCase		(		snake_case			:					int      ,       *,
    snake_case			:					Optional[Any]      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[Any]      ,       snake_case			:					Optional[Any]      ,       snake_case			:					Tuple      ,       snake_case			:					int      ,       snake_case			:					List[str]      ,       snake_case			:					Tuple      ,       snake_case			:					int      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[str]=None      ,       ):
			_lowerCAmelCase:Tuple									=  str(lowerCAmelCase__    )
			if os.path.isfile(lowerCAmelCase__    ):
						return pretrained_model_name_or_path
			elif os.path.isdir(lowerCAmelCase__    ):
						if os.path.isfile(os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__    )    ):
									# Load from a PyTorch checkpoint
									_lowerCAmelCase:Optional[int]									=  os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__    )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__    )    ):
									_lowerCAmelCase:int									=  os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__    )
									return model_file
						else:
									raise EnvironmentError(
									    F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'    )
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(lowerCAmelCase__    ).base_version    ) >= version.parse('''0.20.0'''    )
						):
									try:
												_lowerCAmelCase:Tuple									=  hf_hub_download(
												    lowerCAmelCase__      ,       filename=_add_variant(lowerCAmelCase__      ,       lowerCAmelCase__    )      ,       cache_dir=lowerCAmelCase__      ,       force_download=lowerCAmelCase__      ,       proxies=lowerCAmelCase__      ,       resume_download=lowerCAmelCase__      ,       local_files_only=lowerCAmelCase__      ,       use_auth_token=lowerCAmelCase__      ,       user_agent=lowerCAmelCase__      ,       subfolder=lowerCAmelCase__      ,       revision=revision or commit_hash      ,       )
												warnings.warn(
												    F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'      ,       lowerCAmelCase__      ,       )
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase__      ,       lowerCAmelCase__    )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase__      ,       lowerCAmelCase__    )}\' so that the correct variant file can be added.'      ,       lowerCAmelCase__      ,       )
						try:
									# 2. Load model file as usual
									_lowerCAmelCase:str									=  hf_hub_download(
									    lowerCAmelCase__      ,       filename=lowerCAmelCase__      ,       cache_dir=lowerCAmelCase__      ,       force_download=lowerCAmelCase__      ,       proxies=lowerCAmelCase__      ,       resume_download=lowerCAmelCase__      ,       local_files_only=lowerCAmelCase__      ,       use_auth_token=lowerCAmelCase__      ,       user_agent=lowerCAmelCase__      ,       subfolder=lowerCAmelCase__      ,       revision=revision or commit_hash      ,       )
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
									    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
									    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
									    '''login`.'''    )
						except RevisionNotFoundError:
									raise EnvironmentError(
									    F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
									    '''this model name. Check the model page at '''
									    F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'    )
						except EntryNotFoundError:
									raise EnvironmentError(
									    F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'    )
						except HTTPError as err:
									raise EnvironmentError(
									    F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'    )
						except ValueError:
									raise EnvironmentError(
									    F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
									    F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
									    F' directory containing a file named {weights_name} or'
									    ''' \nCheckout your internet connection or see how to run the library in'''
									    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''    )
						except EnvironmentError:
									raise EnvironmentError(
									    F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
									    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
									    F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
									    F'containing a file named {weights_name}'    )












"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=  MobileNetVaConfig(layer_norm_eps=0.0_01    )

					if "_quant" in model_name:
										raise ValueError('''Quantized models are not supported.'''    )

					_lowerCAmelCase:List[Any]									=  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,       snake_case    )
					if matches:
										_lowerCAmelCase:str									=  float(matches[1]    )
										_lowerCAmelCase:str									=  int(matches[2]    )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_lowerCAmelCase:int									=  1001
					_lowerCAmelCase:Union[str, Any]									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Union[str, Any]									=  {int(snake_case    ) + 1: v for k, v in idalabel.items()}
					_lowerCAmelCase:Optional[Any]									=  '''background'''
					_lowerCAmelCase:Optional[Any]									=  idalabel
					_lowerCAmelCase:List[Any]									=  {v: k for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Optional[Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					Any=False    ):
					_lowerCAmelCase:List[str]									=  get_mobilenet_va_config(snake_case    )

					# Load 🤗 model
					_lowerCAmelCase:Any									=  MobileNetVaForImageClassification(snake_case    ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(snake_case      ,       snake_case      ,       snake_case    )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_lowerCAmelCase:Dict									=  MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,       size={'''shortest_edge''': config.image_size + 32}      ,       )
					_lowerCAmelCase:List[str]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:List[str]									=  model(**snake_case    )
					_lowerCAmelCase:int									=  outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
					elif model_name == "mobilenet_v1_0.75_192":
										_lowerCAmelCase:Any									=  torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
					else:
										_lowerCAmelCase:Dict									=  None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]      ,       snake_case      ,       atol=1e-4    )

					Path(snake_case    ).mkdir(exist_ok=snake_case    )
					print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
					model.save_pretrained(snake_case    )
					print(F'Saving image processor to {pytorch_dump_folder_path}'    )
					image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing to the hub...'''    )
										_lowerCAmelCase:List[str]									=  '''google/''' + model_name
										image_processor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''mobilenet_v1_1.0_224''',
				    type=str,
				    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, 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__						=			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






