



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__       =      get_logger(__name__)


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


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


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Dict:
			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

			A__       =    args.hub_token if hasattr(__UpperCamelCase ,	'hub_token'					) else None
			A__       =    get_full_repo_name(__UpperCamelCase ,	token=__UpperCamelCase					)

			A__       =    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 ,	)

			A__       =    os.path.join(args.output_dir ,	'README.md'					)
			model_card.save(__UpperCamelCase					)


def A						(       __UpperCamelCase ,	__UpperCamelCase = None					)						->      Optional[int]:
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			A__       =    str(Path(__UpperCamelCase					).as_posix()					)
			A__       =    re.search(r'snapshots/([^/]+)/' ,	__UpperCamelCase					)
			if search is None:
						return None
			A__       =    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.
SCREAMING_SNAKE_CASE__       =      os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
SCREAMING_SNAKE_CASE__       =      os.path.join(hf_cache_home, '''diffusers''')


def A						(       __UpperCamelCase = None ,	__UpperCamelCase = None					)						->      None:
			if new_cache_dir is None:
						A__       =    DIFFUSERS_CACHE
			if old_cache_dir is None:
						A__       =    old_diffusers_cache

			A__       =    Path(__UpperCamelCase					).expanduser()
			A__       =    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():
									A__       =    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).


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

if cache_version < 1:
							SCREAMING_SNAKE_CASE__       =      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__       =      '''\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 A						(       __UpperCamelCase ,	__UpperCamelCase = None					)						->      str:
			if variant is not None:
						A__       =    weights_name.split('.'					)
						A__       =    splits[:-1] + [variant] + splits[-1:]
						A__       =    '.'.join(__UpperCamelCase					)

			return weights_name






def A						(       __UpperCamelCase ,	*,
    __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=None ,	)						->      str:
			A__       =    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
									A__       =    os.path.join(__UpperCamelCase ,	__UpperCamelCase					)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)					):
									A__       =    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:
												A__       =    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
									A__       =    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}'''					)









import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


SCREAMING_SNAKE_CASE__       =      get_logger(__name__)


SCREAMING_SNAKE_CASE__       =      r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''


class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Any				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								for processor in self:
											A__       =    inspect.signature(processor.__call__					).parameters
											if len(_snake_case					) > 3:
														if not all(arg in kwargs for arg in list(function_args.keys()					)[2:]					):
																	raise ValueError(
																	    F'''Make sure that all the required parameters: {list(function_args.keys()					)} for '''
																	    F'''{processor.__class__} are passed to the logits processor.'''					)
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case				,					**_snake_case					)
											else:
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       float					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or not (temperature > 0):
											raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''					)

								A__       =    temperature



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores / self.temperature
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       float				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 1					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or (top_p < 0 or top_p > 1.0):
											raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''					)
								if not isinstance(_snake_case				,					_snake_case					) or (min_tokens_to_keep < 1):
											raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''					)

								A__       =    top_p
								A__       =    filter_value
								A__       =    min_tokens_to_keep



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    lax.top_k(_snake_case				,					scores.shape[-1]					)

								A__       =    jnp.full_like(_snake_case				,					self.filter_value					)
								A__       =    jax.nn.softmax(_snake_case				,					axis=-1					).cumsum(axis=-1					)
								A__       =    cumulative_probs < self.top_p

								# include the token that is higher than top_p as well
								A__       =    jnp.roll(_snake_case				,					1					)
								score_mask |= score_mask.at[:, 0].set(_snake_case					)

								# min tokens to keep
								A__       =    score_mask.at[:, : self.min_tokens_to_keep].set(_snake_case					)

								A__       =    jnp.where(_snake_case				,					_snake_case				,					_snake_case					)
								A__       =    jax.lax.sort_key_val(_snake_case				,					_snake_case					)[-1]

								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       int				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 1					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or top_k <= 0:
											raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''					)

								A__       =    max(_snake_case				,					_snake_case					)
								A__       =    filter_value



					def __call__(     self					:       Optional[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    scores.shape
								A__       =    jnp.full(batch_size * vocab_size				,					self.filter_value					)

								A__       =    min(self.top_k				,					scores.shape[-1]					)  # Safety check
								A__     , A__       =    lax.top_k(_snake_case				,					_snake_case					)
								A__       =    jnp.broadcast_to((jnp.arange(_snake_case					) * vocab_size)[:, None]				,					(batch_size, topk)					).flatten()
								A__       =    topk_scores.flatten()
								A__       =    topk_indices.flatten() + shift

								A__       =    next_scores_flat.at[topk_indices_flat].set(_snake_case					)
								A__       =    next_scores_flat.reshape(_snake_case				,					_snake_case					)
								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    bos_token_id



					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

								A__       =    1 - jnp.bool_(cur_len - 1					)

								A__       =    jnp.where(_snake_case				,					new_scores.at[:, self.bos_token_id].set(0					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    max_length
								A__       =    eos_token_id



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

								A__       =    1 - jnp.bool_(cur_len - self.max_length + 1					)

								A__       =    jnp.where(_snake_case				,					new_scores.at[:, self.eos_token_id].set(0					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								if not isinstance(_snake_case				,					_snake_case					) or min_length < 0:
											raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''					)

								if not isinstance(_snake_case				,					_snake_case					) or eos_token_id < 0:
											raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''					)

								A__       =    min_length
								A__       =    eos_token_id



					def __call__(     self					:       int				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.clip(cur_len - self.min_length				,					0				,					1					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.eos_token_id].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       Tuple				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    list(_snake_case					)
								A__       =    begin_index



					def __call__(     self					:       Union[str, Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       str				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.bool_(cur_len - self.begin_index					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.begin_suppress_tokens].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       list					):






								"""simple docstring"""


								A__       =    list(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores.at[..., self.suppress_tokens].set(-float('inf'					)					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    dict(_snake_case					)
								# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
								# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
								# Indexes without forced tokens will have a negative value.
								A__       =    jnp.ones((max(force_token_map.keys()					) + 1)				,					dtype=jnp.intaa					) * -1
								for index, token in force_token_map.items():
											if token is not None:
														A__       =    force_token_array.at[index].set(_snake_case					)
								A__       =    jnp.intaa(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								def _force_token(_snake_case					:       Dict					):
											A__       =    scores.shape[0]
											A__       =    self.force_token_array[generation_idx]

											A__       =    jnp.ones_like(_snake_case				,					dtype=scores.dtype					) * -float('inf'					)
											A__       =    jnp.zeros((batch_size, 1)				,					dtype=scores.dtype					)
											A__       =    lax.dynamic_update_slice(_snake_case				,					_snake_case				,					(0, current_token)					)
											return new_scores

								A__       =    lax.cond(
								    cur_len >= self.force_token_array.shape[0]				,					lambda: scores				,					lambda: lax.cond(
								        self.force_token_array[cur_len] >= 0				,					lambda: _force_token(_snake_case					)				,					lambda: scores				,					)				,					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Dict				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								A__       =    generate_config.eos_token_id
								A__       =    generate_config.no_timestamps_token_id
								A__       =    generate_config.no_timestamps_token_id + 1

								A__       =    decoder_input_length + 1

								if generate_config.is_multilingual:
											# room for language token and task token
											self.begin_index += 2
								if hasattr(_snake_case				,					'max_initial_timestamp_index'					):
											A__       =    generate_config.max_initial_timestamp_index
								else:
											A__       =    model_config.vocab_size
								if self.max_initial_timestamp_index is None:
											A__       =    model_config.vocab_size



					def __call__(     self					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Dict				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    scores.at[:, self.no_timestamps_token_id].set(-float('inf'					)					)

								def handle_pairs(_snake_case					:       Dict				,					_snake_case					:       str					):
											A__       =    jnp.where((cur_len - self.begin_index) >= 1				,					_snake_case				,					_snake_case					)
											A__       =    jnp.where(
											    input_ids_k[cur_len - 1] >= self.timestamp_begin				,					True and last_was_timestamp				,					_snake_case				,					)

											A__       =    jnp.where((cur_len - self.begin_index) < 2				,					_snake_case				,					_snake_case					)
											A__       =    jnp.where(
											    input_ids_k[cur_len - 2] >= self.timestamp_begin				,					_snake_case				,					_snake_case				,					)

											return jnp.where(
											    _snake_case				,					jnp.where(
											        penultimate_was_timestamp > 0				,					scores_k.at[self.timestamp_begin :].set(-float('inf'					)					)				,					scores_k.at[: self.eos_token_id].set(-float('inf'					)					)				,					)				,					_snake_case				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

								A__       =    jnp.where(cur_len == self.begin_index				,					_snake_case				,					_snake_case					)
								A__       =    jnp.where(
								    self.max_initial_timestamp_index is not None				,					True and apply_max_initial_timestamp				,					_snake_case				,					)

								A__       =    self.timestamp_begin + self.max_initial_timestamp_index

								A__       =    jnp.where(
								    _snake_case				,					scores.at[:, last_allowed + 1 :].set(-float('inf'					)					)				,					_snake_case				,					)

								# if sum of probability over timestamps is above any other token, sample timestamp
								A__       =    jax.nn.log_softmax(_snake_case				,					axis=-1					)

								def handle_cumulative_probs(_snake_case					:       List[Any]				,					_snake_case					:       Union[str, Any]					):
											A__       =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,					axis=-1					)
											A__       =    jnp.max(logprobs_k[: self.timestamp_begin]					)
											return jnp.where(
											    timestamp_logprob > max_text_token_logprob				,					scores_k.at[: self.timestamp_begin].set(-float('inf'					)					)				,					_snake_case				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

								return scores




