




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__lowerCAmelCase		:				List[str]       			=		logging.get_logger(__name__)


__lowerCAmelCase		:				Union[str, Any]       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCAmelCase		:				str       			=		{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase		:				Optional[int]       			=		{
    'RUCAIBox/mvp': 1024,
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Dict							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					MvpTokenizer





				def __init__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     List[Any]=None					,      __lowerCamelCase				:     str=None					,      __lowerCamelCase				:     Optional[int]="replace"					,      __lowerCamelCase				:     Optional[Any]="<s>"					,      __lowerCamelCase				:     Union[str, Any]="</s>"					,      __lowerCamelCase				:     Any="</s>"					,      __lowerCamelCase				:     Tuple="<s>"					,      __lowerCamelCase				:     Any="<unk>"					,      __lowerCamelCase				:     List[str]="<pad>"					,      __lowerCamelCase				:     int="<mask>"					,      __lowerCamelCase				:     List[Any]=False					,      __lowerCamelCase				:     int=True					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     Tuple:
							super().__init__(
							    __lowerCamelCase					,      __lowerCamelCase					,      tokenizer_file=__lowerCamelCase					,      errors=__lowerCamelCase					,      bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase					,      **__lowerCamelCase					,      )

							a    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get("add_prefix_space"					,      __lowerCamelCase							) != add_prefix_space:
										a    =			getattr(__lowerCamelCase					,      pre_tok_state.pop("type"							)							)
										a    =			add_prefix_space
										a    =			pre_tok_class(**__lowerCamelCase							)

							a    =			add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							a    =			"post_processor"
							a    =			getattr(self.backend_tokenizer					,      __lowerCamelCase					,      __lowerCamelCase							)
							if tokenizer_component_instance:
										a    =			json.loads(tokenizer_component_instance.__getstate__()							)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
													a    =			tuple(state["sep"]							)
										if "cls" in state:
													a    =			tuple(state["cls"]							)

										a    =			False

										if state.get("add_prefix_space"					,      __lowerCamelCase							) != add_prefix_space:
													a    =			add_prefix_space
													a    =			True

										if state.get("trim_offsets"					,      __lowerCamelCase							) != trim_offsets:
													a    =			trim_offsets
													a    =			True

										if changes_to_apply:
													a    =			getattr(__lowerCamelCase					,      state.pop("type"							)							)
													a    =			component_class(**__lowerCamelCase							)
													setattr(self.backend_tokenizer					,      __lowerCamelCase					,      __lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							if self._mask_token is None:
										if self.verbose:
													logger.error("Using mask_token, but it is not set yet."							)
										return None
							return str(self._mask_token							)





				@mask_token.setter
				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     Dict							)					->     List[str]:
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else value
							a    =			value





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      *__lowerCamelCase				:     str					,      **__lowerCamelCase				:     int							)					->     BatchEncoding:
							a    =			kwargs.get("is_split_into_words"					,      __lowerCamelCase							)

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."							)

							return super()._batch_encode_plus(*__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      *__lowerCamelCase				:     Dict					,      **__lowerCamelCase				:     List[str]							)					->     BatchEncoding:
							a    =			kwargs.get("is_split_into_words"					,      __lowerCamelCase							)

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."							)

							return super()._encode_plus(*__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							a    =			self._tokenizer.model.save(__lowerCamelCase					,      name=__lowerCamelCase							)
							return tuple(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict=None							)					->     List[Any]:
							a    =			[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 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							a    =			[self.sep_token_id]
							a    =			[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]













import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    str				=  ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
							with NamedTemporaryFile(mode='''w+t'''    ) as vocab_file:
											vocab_file.write('''\n'''.join(__magic_name__    )    )
											vocab_file.flush()
											snake_case_						:    Optional[Any]				=  BertTokenizerFast(vocab_file.name    )

							with TemporaryDirectory() as bert_save_dir:
											snake_case_						:    str				=  BertModel(BertConfig(vocab_size=len(__magic_name__    )    )    )
											model.save_pretrained(__magic_name__    )
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							__magic_name__    )






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Tuple				=  self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )
											snake_case_						:    List[str]				=  quantize(Path(__magic_name__    )    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Any				=  self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )
											snake_case_						:    Any				=  quantize(__magic_name__    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None					,							**__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															snake_case_						:    List[str]				=  Path(__magic_name__    ).joinpath('''model.onnx'''    )

											# Remove folder if exists
											if path.parent.exists():
															path.parent.rmdir()

											# Export
											convert(__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )

											return path
							except Exception as e:
											self.fail(__magic_name__    )






			@require_torch
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    Optional[Any]				=  BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    int				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''pt'''    )






			@require_tf
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							from transformers import TFBertModel

							snake_case_						:    Any				=  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    str				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''tf'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  FeatureExtractionPipeline(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  infer_shapes(__magic_name__					,							__magic_name__    )

							# Assert all variables are present
							self.assertEqual(len(__magic_name__    )					,							len(__magic_name__    )    )
							self.assertTrue(all(var_name in shapes for var_name in variable_names    )    )
							self.assertSequenceEqual(variable_names[:3]					,							__magic_name__    )
							self.assertSequenceEqual(variable_names[3:]					,							__magic_name__    )

							# Assert inputs are {0: batch, 1: sequence}
							for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]					,							{0: '''batch''', 1: '''sequence'''}    )

							# Assert outputs are {0: batch, 1: sequence} and {0: batch}
							self.assertDictEqual(shapes['''output_0''']					,							{0: '''batch''', 1: '''sequence'''}    )
							self.assertDictEqual(shapes['''output_1''']					,							{0: '''batch'''}    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
							snake_case_						:    List[str]				=  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							snake_case_			,							snake_case_						:    Tuple				=  ensure_valid_input(FuncContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the same number of args (all are valid)
							self.assertEqual(len(__magic_name__    )					,							3    )

							# Should have exactly the same input names
							self.assertEqual(set(__magic_name__    )					,							set(__magic_name__    )    )

							# Parameter should be reordered according to their respective place in the function:
							# (input_ids, token_type_ids, attention_mask)
							self.assertEqual(__magic_name__					,							(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])    )

							# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
							snake_case_			,							snake_case_						:    Dict				=  ensure_valid_input(FuncNonContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(__magic_name__    )					,							1    )
							self.assertEqual(len(__magic_name__    )					,							1    )

							# Should have only "input_ids"
							self.assertEqual(inputs_args[0]					,							tokens['''input_ids''']    )
							self.assertEqual(ordered_input_names[0]					,							'''input_ids'''    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )					,							'''-test'''    )
							self.assertEqual('''/home/something/my_fake_model-test.onnx'''					,							generated.as_posix()    )
