






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__A      =				logging.get_logger(__name__)

__A      =				{
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}





class 						SCREAMING_SNAKE_CASE				( lowercase__					):





							"""simple docstring"""

							A_						  =					"longformer"

							def __init__(  self:   List[str]		,     __A:   Union[List[int], int] = 5_12		,     __A:   int = 2		,     __A:   int = 1		,     __A:   int = 0		,     __A:   int = 2		,     __A:   int = 3_05_22		,     __A:   int = 7_68		,     __A:   int = 12		,     __A:   int = 12		,     __A:   int = 30_72		,     __A:   str = "gelu"		,     __A:   float = 0.1		,     __A:   float = 0.1		,     __A:   int = 5_12		,     __A:   int = 2		,     __A:   float = 0.02		,     __A:   float = 1e-12		,     __A:   bool = False		,     **__A:   Dict		,     )    ->				Tuple:
												super().__init__(pad_token_id=__A		,     **__A   )

												_A								= attention_window
												_A								= sep_token_id
												_A								= bos_token_id
												_A								= eos_token_id
												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= onnx_export





class 						SCREAMING_SNAKE_CASE				( lowercase__					):





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   "PretrainedConfig"		,     __A:   str = "default"		,     __A:   "List[PatchingSpec]" = None   )    ->				str:
												super().__init__(__A		,     __A		,     __A   )
												_A								= True

							@property
							def       __A (  self:   int   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: "batch", 1: "choice", 2: "sequence"}
												else:
																	_A								= {0: "batch", 1: "sequence"}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''global_attention_mask''', dynamic_axis),
												    ]   )

							@property
							def       __A (  self:   Optional[Any]   )    ->				Mapping[str, Mapping[int, str]]:
												_A								= super().outputs
												if self.task == "default":
																	_A								= {0: "batch"}
												return outputs

							@property
							def       __A (  self:   str   )    ->				float:
												return 1e-4

							@property
							def       __A (  self:   Any   )    ->				int:
												# needs to be >= 14 to support tril operator
												return max(super().default_onnx_opset		,     14   )



							def       __A (  self:   str		,     __A:   "PreTrainedTokenizerBase"		,     __A:   int = -1		,     __A:   int = -1		,     __A:   bool = False		,     __A:   Optional[TensorType] = None		,     )    ->				Mapping[str, Any]:
												_A								= super().generate_dummy_inputs(
												    preprocessor=__A		,     batch_size=__A		,     seq_length=__A		,     is_pair=__A		,     framework=__A   )
												import torch

												# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
												# makes the export fail randomly
												_A								= torch.zeros_like(inputs['''input_ids''']   )
												# make every second token global
												_A								= 1

												return inputs











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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

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

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= 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(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

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

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

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

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

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



