




import warnings

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


SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="mvp"
						lowerCAmelCase__			      =["past_key_values"]
						lowerCAmelCase__			      ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



						def __init__(			self	:   int					,							snake_case__	:   Union[str, Any]=5_02_67					,							snake_case__	:   Dict=10_24					,							snake_case__	:   int=12					,							snake_case__	:   Tuple=40_96					,							snake_case__	:   Optional[int]=16					,							snake_case__	:   List[Any]=12					,							snake_case__	:   Any=40_96					,							snake_case__	:   Tuple=16					,							snake_case__	:   Any=0.0					,							snake_case__	:   List[str]=0.0					,							snake_case__	:   List[Any]="gelu"					,							snake_case__	:   List[str]=10_24					,							snake_case__	:   List[Any]=0.1					,							snake_case__	:   Any=0.0					,							snake_case__	:   Optional[Any]=0.0					,							snake_case__	:   Optional[int]=0.02					,							snake_case__	:   Tuple=0.0					,							snake_case__	:   Union[str, Any]=False					,							snake_case__	:   Dict=True					,							snake_case__	:   Optional[Any]=1					,							snake_case__	:   List[Any]=0					,							snake_case__	:   str=2					,							snake_case__	:   Any=True					,							snake_case__	:   Tuple=2					,							snake_case__	:   Optional[int]=2					,							snake_case__	:   List[str]=False					,							snake_case__	:   int=1_00					,							snake_case__	:   int=8_00					,							**snake_case__	:   Union[str, Any]					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							d_model
								SCREAMING_SNAKE_CASE_					      =							encoder_ffn_dim
								SCREAMING_SNAKE_CASE_					      =							encoder_layers
								SCREAMING_SNAKE_CASE_					      =							encoder_attention_heads
								SCREAMING_SNAKE_CASE_					      =							decoder_ffn_dim
								SCREAMING_SNAKE_CASE_					      =							decoder_layers
								SCREAMING_SNAKE_CASE_					      =							decoder_attention_heads
								SCREAMING_SNAKE_CASE_					      =							dropout
								SCREAMING_SNAKE_CASE_					      =							attention_dropout
								SCREAMING_SNAKE_CASE_					      =							activation_dropout
								SCREAMING_SNAKE_CASE_					      =							activation_function
								SCREAMING_SNAKE_CASE_					      =							init_std
								SCREAMING_SNAKE_CASE_					      =							encoder_layerdrop
								SCREAMING_SNAKE_CASE_					      =							decoder_layerdrop
								SCREAMING_SNAKE_CASE_					      =							classifier_dropout
								SCREAMING_SNAKE_CASE_					      =							use_cache
								SCREAMING_SNAKE_CASE_					      =							encoder_layers
								SCREAMING_SNAKE_CASE_					      =							scale_embedding  # scale factor will be sqrt(d_model) if True
								SCREAMING_SNAKE_CASE_					      =							use_prompt
								SCREAMING_SNAKE_CASE_					      =							prompt_length
								SCREAMING_SNAKE_CASE_					      =							prompt_mid_dim

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

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							self.bos_token_id
										warnings.warn(
										    f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
										    'The config can simply be saved and uploaded again to be fixed.'  )





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)