




'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCamelCase_		:    Any									=		logging.get_logger(__name__)

UpperCamelCase_		:    Union[str, Any]									=		{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""plbart"""
     SCREAMING_SNAKE_CASE_							:					Any					       =		["""past_key_values"""]
     SCREAMING_SNAKE_CASE_							:					Any					       =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(				self      ,_SCREAMING_SNAKE_CASE=50_005      ,_SCREAMING_SNAKE_CASE=1_024      ,_SCREAMING_SNAKE_CASE=6      ,_SCREAMING_SNAKE_CASE=3_072      ,_SCREAMING_SNAKE_CASE=12      ,_SCREAMING_SNAKE_CASE=6      ,_SCREAMING_SNAKE_CASE=3_072      ,_SCREAMING_SNAKE_CASE=12      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=768      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=1      ,_SCREAMING_SNAKE_CASE=0      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=2      ,**_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   vocab_size
          _snake_case							=   max_position_embeddings
          _snake_case							=   d_model
          _snake_case							=   encoder_ffn_dim
          _snake_case							=   encoder_layers
          _snake_case							=   encoder_attention_heads
          _snake_case							=   decoder_ffn_dim
          _snake_case							=   decoder_layers
          _snake_case							=   decoder_attention_heads
          _snake_case							=   dropout
          _snake_case							=   attention_dropout
          _snake_case							=   activation_dropout
          _snake_case							=   activation_function
          _snake_case							=   init_std
          _snake_case							=   encoder_layerdrop
          _snake_case							=   decoder_layerdrop
          _snake_case							=   classifier_dropout
          _snake_case							=   use_cache
          _snake_case							=   encoder_layers
          _snake_case							=   scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=_SCREAMING_SNAKE_CASE      ,bos_token_id=_SCREAMING_SNAKE_CASE      ,eos_token_id=_SCREAMING_SNAKE_CASE      ,is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,forced_eos_token_id=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE      ,)







class      _a       (		__lowerCAmelCase							):






     @property
     def        _lowercase			(				self					)							->    Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "sequence"}),
                  ("attention_mask", {0: "batch", 1: "sequence"}),
              ]					)






     @property
     def        _lowercase			(				self					)							->    Mapping[str, Mapping[int, str]]:
          if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]					)
          else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]					)








'''simple docstring'''





UpperCamelCase_		:    Optional[Any]									=		'''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

UpperCamelCase_		:    List[Any]									=		[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]


