






"""simple docstring"""
from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowerCAmelCase       =				{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_lowerCAmelCase       =				{
    "facebook/blenderbot_small-90M": 512,
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Dict 			=					VOCAB_FILES_NAMES
	_A							:					List[str] 			=					PRETRAINED_VOCAB_FILES_MAP
	_A							:					Dict 			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A							:					str 			=					BlenderbotSmallTokenizer





	def __init__(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_="<|endoftext|>"  ,  lowerCAmelCase_="<|endoftext|>"  ,  lowerCAmelCase_="<|endoftext|>"  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=True  ,  **lowerCAmelCase_  ,  ):
		super().__init__(
		    ByteLevelBPETokenizer(
		        vocab=lowerCAmelCase_  ,  merges=lowerCAmelCase_  ,  add_prefix_space=lowerCAmelCase_  ,  trim_offsets=lowerCAmelCase_  ,  )  ,  bos_token=lowerCAmelCase_  ,  eos_token=lowerCAmelCase_  ,  unk_token=lowerCAmelCase_  ,  **lowerCAmelCase_  ,  )
		A_		:    Dict						     = add_prefix_space





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None				):
		A_		:    str						     = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
		if token_ids_a is None:
			return output

		return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None				):
		A_		:    Tuple						     = [self.sep_token_id]
		A_		:    Union[str, Any]						     = [self.cls_token_id]

		if token_ids_a is None:
			return len(cls + token_ids_a + sep				) * [0]
		return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]









"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

