

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 			a  (	UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			BlenderbotSmallTokenizer
						_lowercase    						=			False


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().setUp()

										_UpperCAmelCase       :		Union[str, Any] 		=    ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
										_UpperCAmelCase       :		Any 		=    dict(zip(A_						,					range(len(A_	)	)	)	)

										_UpperCAmelCase       :		Any 		=    ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
										_UpperCAmelCase       :		str 		=    {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

										_UpperCAmelCase       :		Any 		=    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES["vocab_file"]	)
										_UpperCAmelCase       :		Optional[Any] 		=    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        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										kwargs.update(self.special_tokens_map	)
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    "adapt act apte"
										_UpperCAmelCase       :		Dict 		=    "adapt act apte"
										return input_text, output_text


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    BlenderbotSmallTokenizer(self.vocab_file						,					self.merges_file						,					**self.special_tokens_map	)
										_UpperCAmelCase       :		Tuple 		=    "adapt act apte"
										_UpperCAmelCase       :		str 		=    ["adapt", "act", "ap@@", "te"]
										_UpperCAmelCase       :		Optional[Any] 		=    tokenizer.tokenize(A_	)
										self.assertListEqual(A_						,					A_	)

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

										_UpperCAmelCase       :		int 		=    [0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"	)
										assert tok("sam"	).input_ids == [1384]
										_UpperCAmelCase       :		Any 		=    "I am a small frog."
										_UpperCAmelCase       :		List[Any] 		=    tok([src_text]						,					padding=A_						,					truncation=A_	)["input_ids"]
										_UpperCAmelCase       :		Optional[int] 		=    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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"	)
										_UpperCAmelCase       :		List[Any] 		=    "I am a small frog ."
										_UpperCAmelCase       :		str 		=    "."
										_UpperCAmelCase       :		str 		=    tok(A_	)["input_ids"]
										_UpperCAmelCase       :		str 		=    tok(A_	)["input_ids"]

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


