from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
			UpperCamelCase_				:						List[str] 			=   'plbart'
			UpperCamelCase_				:						int 			=   ['past_key_values']
			UpperCamelCase_				:						Dict 			=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(    self							,     lowerCAmelCase_=5_00_05							,     lowerCAmelCase_=10_24							,     lowerCAmelCase_=6							,     lowerCAmelCase_=30_72							,     lowerCAmelCase_=12							,     lowerCAmelCase_=6							,     lowerCAmelCase_=30_72							,     lowerCAmelCase_=12							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=True							,     lowerCAmelCase_=True							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=7_68							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=0.02							,     lowerCAmelCase_=0.0							,     lowerCAmelCase_=True							,     lowerCAmelCase_=1							,     lowerCAmelCase_=0							,     lowerCAmelCase_=2							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Dict:
								a  :    Optional[int]				=						vocab_size
								a  :    Union[str, Any]				=						max_position_embeddings
								a  :    Optional[Any]				=						d_model
								a  :    Optional[int]				=						encoder_ffn_dim
								a  :    Any				=						encoder_layers
								a  :    Any				=						encoder_attention_heads
								a  :    Tuple				=						decoder_ffn_dim
								a  :    Optional[Any]				=						decoder_layers
								a  :    int				=						decoder_attention_heads
								a  :    Optional[Any]				=						dropout
								a  :    int				=						attention_dropout
								a  :    Union[str, Any]				=						activation_dropout
								a  :    Tuple				=						activation_function
								a  :    Union[str, Any]				=						init_std
								a  :    List[str]				=						encoder_layerdrop
								a  :    Optional[Any]				=						decoder_layerdrop
								a  :    int				=						classifier_dropout
								a  :    Tuple				=						use_cache
								a  :    Optional[Any]				=						encoder_layers
								a  :    Tuple				=						scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     is_encoder_decoder=lowerCAmelCase_							,     forced_eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
			@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'''}),
													    ]    )
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]