

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


A_       : int         =			get_logger(__name__)


A_       : List[str]         =			Path(__file__).parent / 'model_card_template.md'
A_       : str         =			uuida().hex
A_       : List[str]         =			os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
A_       : Optional[Any]         =			os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
A_       : str         =			HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'

def 						UpperCamelCase					(lowercase_: Union[Dict, str, None] = None					)     ->      str:
	A__							:    Union[str, Any]        =		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(lowercase_    ,      lowercase_					):
		ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()					)
	elif isinstance(lowercase_    ,      lowercase_					):
		ua += "; " + user_agent
	return ua

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: Optional[str] = None    ,      lowercase_: Optional[str] = None					)     ->      Dict:
	if token is None:
		A__							:    Dict        =		HfFolder.get_token()
	if organization is None:
		A__							:    int        =		whoami(lowercase_					)["""name"""]
		return f"""{username}/{model_id}"""
	else:
		return f"""{organization}/{model_id}"""

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: Any					)     ->      List[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(lowercase_    ,      """local_rank"""					) and args.local_rank not in [-1, 0]:
		return

	A__							:    Union[str, Any]        =		args.hub_token if hasattr(lowercase_    ,      """hub_token"""					) else None
	A__							:    Union[str, Any]        =		get_full_repo_name(lowercase_    ,      token=lowercase_					)

	A__							:    Dict        =		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=lowercase_    ,      model_name=lowercase_    ,      repo_name=lowercase_    ,      dataset_name=args.dataset_name if hasattr(lowercase_    ,      """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(lowercase_    ,      """gradient_accumulation_steps"""					) else None
	    )    ,      adam_betaa=args.adam_betaa if hasattr(lowercase_    ,      """adam_beta1"""					) else None    ,      adam_betaa=args.adam_betaa if hasattr(lowercase_    ,      """adam_beta2"""					) else None    ,      adam_weight_decay=args.adam_weight_decay if hasattr(lowercase_    ,      """adam_weight_decay"""					) else None    ,      adam_epsilon=args.adam_epsilon if hasattr(lowercase_    ,      """adam_epsilon"""					) else None    ,      lr_scheduler=args.lr_scheduler if hasattr(lowercase_    ,      """lr_scheduler"""					) else None    ,      lr_warmup_steps=args.lr_warmup_steps if hasattr(lowercase_    ,      """lr_warmup_steps"""					) else None    ,      ema_inv_gamma=args.ema_inv_gamma if hasattr(lowercase_    ,      """ema_inv_gamma"""					) else None    ,      ema_power=args.ema_power if hasattr(lowercase_    ,      """ema_power"""					) else None    ,      ema_max_decay=args.ema_max_decay if hasattr(lowercase_    ,      """ema_max_decay"""					) else None    ,      mixed_precision=args.mixed_precision    ,      )

	A__							:    int        =		os.path.join(args.output_dir    ,      """README.md"""					)
	model_card.save(lowercase_					)

def 						UpperCamelCase					(lowercase_: Optional[str]    ,      lowercase_: Optional[str] = None					)     ->      Optional[int]:
	if resolved_file is None or commit_hash is not None:
		return commit_hash
	A__							:    List[str]        =		str(Path(lowercase_					).as_posix()					)
	A__							:    str        =		re.search(r"""snapshots/([^/]+)/"""    ,      lowercase_					)
	if search is None:
		return None
	A__							:    Union[str, Any]        =		search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(lowercase_					) 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.
A_       : Optional[int]         =			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
A_       : str         =			os.path.join(hf_cache_home, 'diffusers')

def 						UpperCamelCase					(lowercase_: Optional[str] = None    ,      lowercase_: Optional[str] = None					)     ->      None:
	if new_cache_dir is None:
		A__							:    Union[str, Any]        =		DIFFUSERS_CACHE
	if old_cache_dir is None:
		A__							:    str        =		old_diffusers_cache

	A__							:    List[str]        =		Path(lowercase_					).expanduser()
	A__							:    Tuple        =		Path(lowercase_					).expanduser()
	for old_blob_path in old_cache_dir.glob("""**/blobs/*"""					):
		if old_blob_path.is_file() and not old_blob_path.is_symlink():
			A__							:    Dict        =		new_cache_dir / old_blob_path.relative_to(lowercase_					)
			new_blob_path.parent.mkdir(parents=lowercase_    ,      exist_ok=lowercase_					)
			os.replace(lowercase_    ,      lowercase_					)
			try:
				os.symlink(lowercase_    ,      lowercase_					)
			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).


A_       : int         =			os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
	A_       : Union[str, Any]         =			0
else:
	with open(cache_version_file) as f:
		try:
			A_       : Optional[int]         =			int(f.read())
		except ValueError:
			A_       : int         =			0

if cache_version < 1:
	A_       : int         =			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:
			A_       : int         =			'\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					(lowercase_: str    ,      lowercase_: Optional[str] = None					)     ->      str:
	if variant is not None:
		A__							:    Union[str, Any]        =		weights_name.split("""."""					)
		A__							:    Optional[Any]        =		splits[:-1] + [variant] + splits[-1:]
		A__							:    str        =		""".""".join(lowercase_					)

	return weights_name


def 						UpperCamelCase					(lowercase_: Dict    ,      *,
    lowercase_: Tuple    ,      lowercase_: Any    ,      lowercase_: Optional[int]    ,      lowercase_: Dict    ,      lowercase_: Any    ,      lowercase_: Any    ,      lowercase_: List[str]    ,      lowercase_: Optional[Any]    ,      lowercase_: str    ,      lowercase_: Any    ,      lowercase_: List[Any]=None    ,      )     ->      int:
	A__							:    Optional[int]        =		str(lowercase_					)
	if os.path.isfile(lowercase_					):
		return pretrained_model_name_or_path
	elif os.path.isdir(lowercase_					):
		if os.path.isfile(os.path.join(lowercase_    ,      lowercase_					)					):
			# Load from a PyTorch checkpoint
			A__							:    Union[str, Any]        =		os.path.join(lowercase_    ,      lowercase_					)
			return model_file
		elif subfolder is not None and os.path.isfile(
		    os.path.join(lowercase_    ,      lowercase_    ,      lowercase_					)					):
			A__							:    List[str]        =		os.path.join(lowercase_    ,      lowercase_    ,      lowercase_					)
			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(lowercase_					).base_version					) >= version.parse("""0.20.0"""					)
		):
			try:
				A__							:    int        =		hf_hub_download(
				    lowercase_    ,      filename=_add_variant(lowercase_    ,      lowercase_					)    ,      cache_dir=lowercase_    ,      force_download=lowercase_    ,      proxies=lowercase_    ,      resume_download=lowercase_    ,      local_files_only=lowercase_    ,      use_auth_token=lowercase_    ,      user_agent=lowercase_    ,      subfolder=lowercase_    ,      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."""    ,      lowercase_    ,      )
				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(lowercase_    ,      lowercase_					)} 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(lowercase_    ,      lowercase_					)}' so that the correct variant file can be added."""    ,      lowercase_    ,      )
		try:
			# 2. Load model file as usual
			A__							:    Any        =		hf_hub_download(
			    lowercase_    ,      filename=lowercase_    ,      cache_dir=lowercase_    ,      force_download=lowercase_    ,      proxies=lowercase_    ,      resume_download=lowercase_    ,      local_files_only=lowercase_    ,      use_auth_token=lowercase_    ,      user_agent=lowercase_    ,      subfolder=lowercase_    ,      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}"""					)






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



