

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


SCREAMING_SNAKE_CASE_     :   Optional[int]        =      get_logger(__name__)


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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[Dict, str, None] = None )   ->       str:
				_UpperCAmelCase       :		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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Optional[str] = None  ,      lowerCAmelCase:  Optional[str] = None )   ->       Optional[Any]:
				if token is None:
								_UpperCAmelCase       :		List[str] 		=    HfFolder.get_token()
				if organization is None:
								_UpperCAmelCase       :		List[str] 		=    whoami(lowerCAmelCase )["name"]
								return F'{username}/{model_id}'
				else:
								return F'{organization}/{model_id}'

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Dict )   ->       List[str]:
				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

				_UpperCAmelCase       :		Optional[int] 		=    args.hub_token if hasattr(lowerCAmelCase  ,      "hub_token" ) else None
				_UpperCAmelCase       :		Union[str, Any] 		=    get_full_repo_name(lowerCAmelCase  ,      token=lowerCAmelCase )

				_UpperCAmelCase       :		List[str] 		=    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  ,      )

				_UpperCAmelCase       :		Dict 		=    os.path.join(args.output_dir  ,      "README.md" )
				model_card.save(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[str]  ,      lowerCAmelCase:  Optional[str] = None )   ->       Dict:
				if resolved_file is None or commit_hash is not None:
								return commit_hash
				_UpperCAmelCase       :		List[str] 		=    str(Path(lowerCAmelCase ).as_posix() )
				_UpperCAmelCase       :		Optional[int] 		=    re.search(R"snapshots/([^/]+)/"  ,      lowerCAmelCase )
				if search is None:
								return None
				_UpperCAmelCase       :		Any 		=    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.
SCREAMING_SNAKE_CASE_     :   Any        =      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
SCREAMING_SNAKE_CASE_     :   List[Any]        =      os.path.join(hf_cache_home, 'diffusers')

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[str] = None  ,      lowerCAmelCase:  Optional[str] = None )   ->       None:
				if new_cache_dir is None:
								_UpperCAmelCase       :		str 		=    DIFFUSERS_CACHE
				if old_cache_dir is None:
								_UpperCAmelCase       :		Optional[Any] 		=    old_diffusers_cache

				_UpperCAmelCase       :		List[Any] 		=    Path(lowerCAmelCase ).expanduser()
				_UpperCAmelCase       :		Tuple 		=    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():
												_UpperCAmelCase       :		List[Any] 		=    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).


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

if cache_version < 1:
					SCREAMING_SNAKE_CASE_     :   List[Any]        =      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:
															SCREAMING_SNAKE_CASE_     :   Any        =      '\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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Optional[str] = None )   ->       str:
				if variant is not None:
								_UpperCAmelCase       :		Optional[Any] 		=    weights_name.split("." )
								_UpperCAmelCase       :		int 		=    splits[:-1] + [variant] + splits[-1:]
								_UpperCAmelCase       :		List[Any] 		=    ".".join(lowerCAmelCase )

				return weights_name





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      *,
    lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]=None  ,      )   ->       Dict:
				_UpperCAmelCase       :		Optional[int] 		=    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
												_UpperCAmelCase       :		List[str] 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )
												return model_file
								elif subfolder is not None and os.path.isfile(
								    os.path.join(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase ) ):
												_UpperCAmelCase       :		Tuple 		=    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:
																_UpperCAmelCase       :		Any 		=    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
												_UpperCAmelCase       :		Any 		=    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}' )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"openai-gpt"
						_lowercase    						=			{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=40478						,					A_=512						,					A_=768						,					A_=12						,					A_=12						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_="cls_index"						,					A_=True						,					A_=None						,					A_=True						,					A_=0.1						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		str 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Any 		=    n_head
										_UpperCAmelCase       :		int 		=    afn
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Tuple 		=    embd_pdrop
										_UpperCAmelCase       :		Optional[Any] 		=    attn_pdrop
										_UpperCAmelCase       :		Any 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[str] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_type
										_UpperCAmelCase       :		List[Any] 		=    summary_use_proj
										_UpperCAmelCase       :		Optional[Any] 		=    summary_activation
										_UpperCAmelCase       :		int 		=    summary_first_dropout
										_UpperCAmelCase       :		List[str] 		=    summary_proj_to_labels
										super().__init__(**A_	)


