


"""simple docstring"""







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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

													super().__init__(
													    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)