




"""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


a_        =       get_logger(__name__)


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







def      __UpperCAmelCase     ( __UpperCamelCase = None	):
			__lowercase			:  Optional[int]								=       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(__UpperCamelCase	,				__UpperCamelCase	):
						ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()	)
			elif isinstance(__UpperCamelCase	,				__UpperCamelCase	):
						ua += "; " + user_agent
			return ua







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase = None	,				__UpperCamelCase = None	):
			if token is None:
						__lowercase			:  List[Any]								=       HfFolder.get_token()
			if organization is None:
						__lowercase			:  Union[str, Any]								=       whoami(__UpperCamelCase	)['''name''']
						return f"""{username}/{model_id}"""
			else:
						return f"""{organization}/{model_id}"""







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			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(__UpperCamelCase	,				'''local_rank'''	) and args.local_rank not in [-1, 0]:
						return

			__lowercase			:  Optional[int]								=       args.hub_token if hasattr(__UpperCamelCase	,				'''hub_token'''	) else None
			__lowercase			:  Tuple								=       get_full_repo_name(__UpperCamelCase	,				token=__UpperCamelCase	)

			__lowercase			:  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=__UpperCamelCase	,				model_name=__UpperCamelCase	,				repo_name=__UpperCamelCase	,				dataset_name=args.dataset_name if hasattr(__UpperCamelCase	,				'''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(__UpperCamelCase	,				'''gradient_accumulation_steps'''	) else None
			    )	,				adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase	,				'''adam_beta1'''	) else None	,				adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase	,				'''adam_beta2'''	) else None	,				adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCamelCase	,				'''adam_weight_decay'''	) else None	,				adam_epsilon=args.adam_epsilon if hasattr(__UpperCamelCase	,				'''adam_epsilon'''	) else None	,				lr_scheduler=args.lr_scheduler if hasattr(__UpperCamelCase	,				'''lr_scheduler'''	) else None	,				lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCamelCase	,				'''lr_warmup_steps'''	) else None	,				ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCamelCase	,				'''ema_inv_gamma'''	) else None	,				ema_power=args.ema_power if hasattr(__UpperCamelCase	,				'''ema_power'''	) else None	,				ema_max_decay=args.ema_max_decay if hasattr(__UpperCamelCase	,				'''ema_max_decay'''	) else None	,				mixed_precision=args.mixed_precision	,				)

			__lowercase			:  Optional[Any]								=       os.path.join(args.output_dir	,				'''README.md'''	)
			model_card.save(__UpperCamelCase	)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase = None	):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			__lowercase			:  Tuple								=       str(Path(__UpperCamelCase	).as_posix()	)
			__lowercase			:  Any								=       re.search(R'''snapshots/([^/]+)/'''	,				__UpperCamelCase	)
			if search is None:
						return None
			__lowercase			:  Optional[Any]								=       search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(__UpperCamelCase	) 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_        =       os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
a_        =       os.path.join(hf_cache_home, 'diffusers')







def      __UpperCAmelCase     ( __UpperCamelCase = None	,				__UpperCamelCase = None	):
			if new_cache_dir is None:
						__lowercase			:  Tuple								=       DIFFUSERS_CACHE
			if old_cache_dir is None:
						__lowercase			:  Tuple								=       old_diffusers_cache

			__lowercase			:  Union[str, Any]								=       Path(__UpperCamelCase	).expanduser()
			__lowercase			:  Optional[Any]								=       Path(__UpperCamelCase	).expanduser()
			for old_blob_path in old_cache_dir.glob('''**/blobs/*'''	):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									__lowercase			:  List[str]								=       new_cache_dir / old_blob_path.relative_to(__UpperCamelCase	)
									new_blob_path.parent.mkdir(parents=__UpperCamelCase	,				exist_ok=__UpperCamelCase	)
									os.replace(__UpperCamelCase	,				__UpperCamelCase	)
									try:
												os.symlink(__UpperCamelCase	,				__UpperCamelCase	)
									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_        =       os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
			a_        =       0
else:
			with open(cache_version_file) as f:
						try:
									a_        =       int(f.read())
						except ValueError:
									a_        =       0

if cache_version < 1:
			a_        =       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_        =       '\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     ( __UpperCamelCase	,				__UpperCamelCase = None	):
			if variant is not None:
						__lowercase			:  Optional[int]								=       weights_name.split('''.'''	)
						__lowercase			:  List[Any]								=       splits[:-1] + [variant] + splits[-1:]
						__lowercase			:  str								=       '''.'''.join(__UpperCamelCase	)

			return weights_name







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



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__UpperCamelCase   :							Optional[Any]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
__UpperCamelCase   :							List[str]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="whisper"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self			, lowerCamelCase=5_1865			, lowerCamelCase=80			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=1536			, lowerCamelCase=1536			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=5_0257			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="gelu"			, lowerCamelCase=256			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=1500			, lowerCamelCase=448			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=None			, lowerCamelCase=[220, 5_0256]			, lowerCamelCase=False			, lowerCamelCase=256			, lowerCamelCase=False			, lowerCamelCase=0.05			, lowerCamelCase=10			, lowerCamelCase=2			, lowerCamelCase=0.0			, lowerCamelCase=10			, lowerCamelCase=0			, lowerCamelCase=7			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     num_mel_bins
         __a					  =     d_model
         __a					  =     encoder_layers
         __a					  =     encoder_attention_heads
         __a					  =     decoder_layers
         __a					  =     decoder_attention_heads
         __a					  =     decoder_ffn_dim
         __a					  =     encoder_ffn_dim
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     activation_function
         __a					  =     init_std
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop
         __a					  =     use_cache
         __a					  =     encoder_layers
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     max_source_positions
         __a					  =     max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         __a					  =     classifier_proj_size
         __a					  =     use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __a					  =     apply_spec_augment
         __a					  =     mask_time_prob
         __a					  =     mask_time_length
         __a					  =     mask_time_min_masks
         __a					  =     mask_feature_prob
         __a					  =     mask_feature_length
         __a					  =     mask_feature_min_masks

         __a					  =     median_filter_width

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, suppress_tokens=lowerCamelCase			, begin_suppress_tokens=lowerCamelCase			, **lowerCamelCase			, )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict(
             [
                 ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
             ]   )
         if self.use_past:
            __a					  =     {0: 'batch'}
         else:
            __a					  =     {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )

         return common_inputs




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, lowerCamelCase = 2_2050			, lowerCamelCase = 5.0			, lowerCamelCase = 220			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     OrderedDict()
         __a					  =     OnnxConfig.generate_dummy_inputs(
             self			, preprocessor=preprocessor.feature_extractor			, batch_size=lowerCamelCase			, framework=lowerCamelCase			, sampling_rate=lowerCamelCase			, time_duration=lowerCamelCase			, frequency=lowerCamelCase			, )
         __a					  =     encoder_inputs['input_features'].shape[2]
         __a					  =     encoder_sequence_length // 2 if self.use_past else seq_length

         __a					  =     super().generate_dummy_inputs(
             preprocessor.tokenizer			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     encoder_inputs.pop('input_features'   )
         __a					  =     decoder_inputs.pop('decoder_input_ids'   )

         if "past_key_values" in decoder_inputs:
            __a					  =     decoder_inputs.pop('past_key_values'   )

         return dummy_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->float:



         '''simple docstring'''
         return 1e-3