import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


a  :      List[Any]            =    logging.get_logger(__name__)


a  :      Any            =    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a  :      Tuple            =    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

a  :      Union[str, Any]            =    {
    """allenai/led-base-16384""": 1_6_3_8_4,
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				List[str]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[Any]          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE:				str          =    LEDTokenizer
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    ['input_ids', 'attention_mask']




    def __init__(    self       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__="replace"       ,						lowerCamelCase__="<s>"       ,						lowerCamelCase__="</s>"       ,						lowerCamelCase__="</s>"       ,						lowerCamelCase__="<s>"       ,						lowerCamelCase__="<unk>"       ,						lowerCamelCase__="<pad>"       ,						lowerCamelCase__="<mask>"       ,						lowerCamelCase__=False       ,						lowerCamelCase__=True       ,						**lowerCamelCase__       ,						):
       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__       ,						)

       lowerCAmelCase_:							str       =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get("add_prefix_space"       ,						lowerCamelCase__ ) != add_prefix_space:
          lowerCAmelCase_:							Tuple       =	getattr(lowerCamelCase__       ,						pre_tok_state.pop("type" ) )
          lowerCAmelCase_:							Dict       =	add_prefix_space
          lowerCAmelCase_:							Tuple       =	pre_tok_class(**lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       lowerCAmelCase_:							List[Any]       =	"post_processor"
       lowerCAmelCase_:							Dict       =	getattr(self.backend_tokenizer       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       if tokenizer_component_instance:
          lowerCAmelCase_:							List[str]       =	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:
             lowerCAmelCase_:							Optional[int]       =	tuple(state["sep"] )
          if "cls" in state:
             lowerCAmelCase_:							Dict       =	tuple(state["cls"] )

          lowerCAmelCase_:							List[Any]       =	False

          if state.get("add_prefix_space"       ,						lowerCamelCase__ ) != add_prefix_space:
             lowerCAmelCase_:							int       =	add_prefix_space
             lowerCAmelCase_:							Tuple       =	True

          if state.get("trim_offsets"       ,						lowerCamelCase__ ) != trim_offsets:
             lowerCAmelCase_:							List[Any]       =	trim_offsets
             lowerCAmelCase_:							Optional[Any]       =	True

          if changes_to_apply:
             lowerCAmelCase_:							Tuple       =	getattr(lowerCamelCase__       ,						state.pop("type" ) )
             lowerCAmelCase_:							Union[str, Any]       =	component_class(**lowerCamelCase__ )
             setattr(self.backend_tokenizer       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def      _a    (    self ):
       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      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	AddedToken(lowerCamelCase__       ,						lstrip=lowerCamelCase__       ,						rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ) else value
       lowerCAmelCase_:							Dict       =	value




    def      _a    (    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	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      _a    (    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       lowerCAmelCase_:							List[Any]       =	self._tokenizer.model.save(lowerCamelCase__       ,						name=lowerCamelCase__ )
       return tuple(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=None ):
       lowerCAmelCase_:							Optional[int]       =	[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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       lowerCAmelCase_:							Union[str, Any]       =	[self.sep_token_id]
       lowerCAmelCase_:							List[str]       =	[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]




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = PaddingStrategy.DO_NOT_PAD       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						):
       lowerCAmelCase_:							Any       =	super()._pad(
           encoded_inputs=lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding_strategy=lowerCamelCase__       ,						pad_to_multiple_of=lowerCamelCase__       ,						return_attention_mask=lowerCamelCase__       ,						)

       # Load from model defaults
       if return_attention_mask is None:
          lowerCAmelCase_:							List[str]       =	"attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
          lowerCAmelCase_:							int       =	encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          lowerCAmelCase_:							Union[str, Any]       =	len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase__ )

          if needs_to_be_padded:
             lowerCAmelCase_:							Union[str, Any]       =	len(lowerCamelCase__ ) - len(encoded_inputs["global_attention_mask"] )

             if self.padding_side == "right":
                # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                lowerCAmelCase_:							str       =	(
                    encoded_inputs["global_attention_mask"] + [-1] * difference
                )
             elif self.padding_side == "left":
                lowerCAmelCase_:							List[str]       =	[-1] * difference + encoded_inputs[
                    "global_attention_mask"
                ]
             else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

       return encoded_inputs
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	huggingface_hub.__version__

       lowerCAmelCase_:							List[str]       =	"not installed"
       lowerCAmelCase_:							Dict       =	"NA"
       if is_torch_available():
          import torch

          lowerCAmelCase_:							List[str]       =	torch.__version__
          lowerCAmelCase_:							int       =	torch.cuda.is_available()

       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_transformers_available():
          import transformers

          lowerCAmelCase_:							Tuple       =	transformers.__version__

       lowerCAmelCase_:							Optional[int]       =	"not installed"
       if is_accelerate_available():
          import accelerate

          lowerCAmelCase_:							List[Any]       =	accelerate.__version__

       lowerCAmelCase_:							Dict       =	"not installed"
       if is_xformers_available():
          import xformers

          lowerCAmelCase_:							Dict       =	xformers.__version__

       lowerCAmelCase_:							Union[str, Any]       =	{
           "`diffusers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Huggingface_hub version": hub_version,
           "Transformers version": transformers_version,
           "Accelerate version": accelerate_version,
           "xFormers version": xformers_version,
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
       print(self.format_dict(lowerCamelCase__ ) )

       return info




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"