
"""simple docstring"""




import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
   if is_torch_available():
      import torch
   if is_tf_available():
      import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


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

_A     :			int  						=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_A     :			List[Any]  						=			{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

_A     :			Optional[Any]  						=			{
    """Salesforce/codegen-350M-mono""": 20_48,
}

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase				=							CodeGenTokenizer
   def __init__(     self      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a="<|endoftext|>"      ,       _a="<|endoftext|>"      ,       _a="<|endoftext|>"      ,       _a=False      ,       **_a      ,       ):
          super().__init__(
              _a      ,       _a      ,       tokenizer_file=_a      ,       unk_token=_a      ,       bos_token=_a      ,       eos_token=_a      ,       add_prefix_space=_a      ,       **_a      ,       )

          if kwargs.pop("add_bos_token"      ,       _a							):
                 lowercase       :							Optional[Any]									=				kwargs.pop("name_or_path"      ,       ""							)
                 raise ValueError(
                     "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
                     "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
                     f"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
                     f"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
                     "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
                     " so that the fast tokenizer works correctly."							)

          lowercase       :							Union[str, Any]									=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
          if pre_tok_state.get("add_prefix_space"      ,       _a							) != add_prefix_space:
                 lowercase       :							Tuple									=				getattr(_a      ,       pre_tok_state.pop("type"							)							)
                 lowercase       :							Union[str, Any]									=				add_prefix_space
                 lowercase       :							Optional[Any]									=				pre_tok_class(**_a							)

          lowercase       :							Any									=				add_prefix_space
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							Optional[Any]									=				kwargs.get("is_split_into_words"      ,       _a							)
          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(*_a      ,       **_a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							Optional[Any]									=				kwargs.get("is_split_into_words"      ,       _a							)

          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(*_a      ,       **_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[Any]									=				self._tokenizer.model.save(_a      ,       name=_a							)
          return tuple(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = False      ,       _a = None      ,       _a = None      ,       **_a      ,       ):
          lowercase       :							List[str]									=				super().decode(
              token_ids=_a      ,       skip_special_tokens=_a      ,       clean_up_tokenization_spaces=_a      ,       **_a      ,       )

          if truncate_before_pattern is not None and len(_a							) > 0:
                 lowercase       :							Union[str, Any]									=				self.truncate(_a      ,       _a							)

          return decoded_text

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          def find_re(_a      ,       _a      ,       _a							):
                 lowercase       :							List[Any]									=				pattern.search(_a      ,       _a							)
                 return m.start() if m else -1

          lowercase       :							List[Any]									=				[re.compile(_a      ,       re.MULTILINE							) for pattern in truncate_before_pattern]

          lowercase       :							Tuple									=				list(re.finditer("^print"      ,       _a      ,       re.MULTILINE							)							)

          if len(_a							) > 1:
                 lowercase       :							List[Any]									=				completion[: prints[1].start()]

          lowercase       :							Dict									=				list(re.finditer("^def"      ,       _a      ,       re.MULTILINE							)							)

          if len(_a							) > 1:
                 lowercase       :							Optional[Any]									=				completion[: defs[1].start()]

          lowercase       :							Any									=				0

          lowercase       :							int									=				[
              pos for pos in [find_re(_a      ,       _a      ,       _a							) for terminal in terminals] if pos != -1
          ]

          if len(_a							) > 0:
                 return completion[: min(_a							)]
          else:
                 return completion




"""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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

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

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
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-de


