




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    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			@register_to_config
			def __init__( self			:   Any       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   float       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   bool = False       ,						)						->   Optional[int]:
								super().__init__()

								__magic_name__							:	List[str]    							=							nn.Embedding(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							nn.Embedding(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	int    							=							False

								__magic_name__							:	Union[str, Any]    							=							nn.Dropout(p=lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							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_       ,						)

								__magic_name__							:	Dict    							=							nn.ModuleList()
								for lyr_num in range(lowerCamelCase_  ):
													__magic_name__							:	List[Any]    							=							TaBlock(lowerCamelCase_  )
													self.encoders.append(lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							TaLayerNorm(lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							nn.Dropout(p=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[Any]  )						->   Dict:
								__magic_name__							:	Union[str, Any]    							=							self.token_embedder(lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							encoder_input_tokens.shape[1]
								__magic_name__							:	List[str]    							=							torch.arange(lowerCamelCase_       ,						device=encoder_input_tokens.device  )
								x += self.position_encoding(lowerCamelCase_  )

								__magic_name__							:	Optional[Any]    							=							self.dropout_pre(lowerCamelCase_  )

								# inverted the attention mask
								__magic_name__							:	Optional[Any]    							=							encoder_input_tokens.size()
								__magic_name__							:	List[str]    							=							self.get_extended_attention_mask(lowerCamelCase_       ,						lowerCamelCase_  )

								for lyr in self.encoders:
													__magic_name__							:	List[str]    							=							lyr(lowerCamelCase_       ,						lowerCamelCase_  )[0]
								__magic_name__							:	str    							=							self.layer_norm(lowerCamelCase_  )

								return self.dropout_post(lowerCamelCase_  ), encoder_inputs_mask









import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



