"""simple docstring"""



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_roberta import RobertaTokenizer


__A :     Union[str, Any]				       =				logging.get_logger(__name__)

__A :     List[str]				       =				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A :     int				       =				{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__A :     str				       =				{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class 	__lowerCAmelCase					(      a__):




    '''simple docstring'''






    __magic_name__   :			Optional[Any]       		=       VOCAB_FILES_NAMES
    __magic_name__   :			Dict       		=       PRETRAINED_VOCAB_FILES_MAP
    __magic_name__   :			Dict       		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__   :			int       		=       ["input_ids", "attention_mask"]
    __magic_name__   :			Optional[int]       		=       RobertaTokenizer





    def __init__(     self				:	Union[str, Any]					,				UpperCamelCase__				:	str=None					,				UpperCamelCase__				:	List[Any]=None					,				UpperCamelCase__				:	Any=None					,				UpperCamelCase__				:	str="replace"					,				UpperCamelCase__				:	Optional[Any]="<s>"					,				UpperCamelCase__				:	Dict="</s>"					,				UpperCamelCase__				:	int="</s>"					,				UpperCamelCase__				:	Tuple="<s>"					,				UpperCamelCase__				:	Any="<unk>"					,				UpperCamelCase__				:	int="<pad>"					,				UpperCamelCase__				:	Tuple="<mask>"					,				UpperCamelCase__				:	List[str]=False					,				UpperCamelCase__				:	str=True					,				**UpperCamelCase__				:	Optional[int]					,				):
          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__	:      Union[str, Any]      =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
          if pre_tok_state.get("add_prefix_space"					,				lowerCamelCase_							) != add_prefix_space:
                A__	:      List[str]      =getattr(lowerCamelCase_					,				pre_tok_state.pop("type"							)							)
                A__	:      Tuple      =add_prefix_space
                A__	:      Any      =pre_tok_class(**lowerCamelCase_							)

          A__	:      Union[str, Any]      =add_prefix_space

          A__	:      List[Any]      ="post_processor"
          A__	:      int      =getattr(self.backend_tokenizer					,				lowerCamelCase_					,				lowerCamelCase_							)
          if tokenizer_component_instance:
                A__	:      Optional[Any]      =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      =tuple(state["sep"]							)
                if "cls" in state:
                      A__	:      str      =tuple(state["cls"]							)

                A__	:      Any      =False

                if state.get("add_prefix_space"					,				lowerCamelCase_							) != add_prefix_space:
                      A__	:      int      =add_prefix_space
                      A__	:      Optional[Any]      =True

                if state.get("trim_offsets"					,				lowerCamelCase_							) != trim_offsets:
                      A__	:      List[Any]      =trim_offsets
                      A__	:      int      =True

                if changes_to_apply:
                      A__	:      List[str]      =getattr(lowerCamelCase_					,				state.pop("type"							)							)
                      A__	:      int      =component_class(**lowerCamelCase_							)
                      setattr(self.backend_tokenizer					,				lowerCamelCase_					,				lowerCamelCase_							)





    @property
    def 					_UpperCAmelCase		(     self				:	int							):
          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				:	List[str]					,				UpperCamelCase__				:	int							):
          A__	:      Optional[int]      =AddedToken(lowerCamelCase_					,				lstrip=lowerCamelCase_					,				rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,				lowerCamelCase_							) else value
          A__	:      List[Any]      =value





    def 					_UpperCAmelCase		(     self				:	Dict					,				*UpperCamelCase__				:	List[str]					,				**UpperCamelCase__				:	str							):
          A__	:      str      =kwargs.get("is_split_into_words"					,				lowerCamelCase_							)
          assert self.add_prefix_space or not is_split_into_words, (
              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				:	Union[str, Any]					,				*UpperCamelCase__				:	Tuple					,				**UpperCamelCase__				:	Union[str, Any]							):
          A__	:      Optional[Any]      =kwargs.get("is_split_into_words"					,				lowerCamelCase_							)

          assert self.add_prefix_space or not is_split_into_words, (
              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]					,				UpperCamelCase__				:	int					,				UpperCamelCase__				:	List[str] = None							):
          A__	:      Optional[int]      =self._tokenizer.model.save(lowerCamelCase_					,				name=lowerCamelCase_							)
          return tuple(lowerCamelCase_							)





    def 					_UpperCAmelCase		(     self				:	Any					,				UpperCamelCase__				:	Dict					,				UpperCamelCase__				:	str=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 					_UpperCAmelCase		(     self				:	Any					,				UpperCamelCase__				:	Optional[int]					,				UpperCamelCase__				:	Tuple = None							):
          A__	:      Tuple      =[self.sep_token_id]
          A__	:      int      =[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'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__UpperCAmelCase       							=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__UpperCAmelCase       							=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__UpperCAmelCase       							=				dict(zip(vocab, range(len(vocab))))
__UpperCAmelCase       							=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
		__UpperCAmelCase       							=				Path(tmpdirname)
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
		with open(src_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(tgt_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(merges_file, '''w''') as fp:
				fp.write('''\n'''.join(merges))

		__UpperCAmelCase       							=				FSMTTokenizer(
		    langs=['''en''', '''ru'''],
		    src_vocab_size=len(vocab),
		    tgt_vocab_size=len(vocab),
		    src_vocab_file=src_vocab_file,
		    tgt_vocab_file=tgt_vocab_file,
		    merges_file=merges_file,
		)

__UpperCAmelCase       							=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__UpperCAmelCase       							=				FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__UpperCAmelCase       							=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCAmelCase       							=				tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru