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


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__											=					{
    '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',
    },
}

UpperCamelCase__											=					{
    'allenai/led-base-16384': 1_6_3_8_4,
}





class      A							(				_UpperCAmelCase     ):
   __UpperCAmelCase : List[str]      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : List[str]      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Any      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Optional[int]      =			LEDTokenizer
   __UpperCAmelCase : int      =			["input_ids", "attention_mask"]


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						Optional[Any]="replace"       ,  __UpperCAmelCase     :						Any="<s>"       ,  __UpperCAmelCase     :						Any="</s>"       ,  __UpperCAmelCase     :						Optional[Any]="</s>"       ,  __UpperCAmelCase     :						Optional[int]="<s>"       ,  __UpperCAmelCase     :						Optional[Any]="<unk>"       ,  __UpperCAmelCase     :						Optional[Any]="<pad>"       ,  __UpperCAmelCase     :						Any="<mask>"       ,  __UpperCAmelCase     :						Union[str, Any]=False       ,  __UpperCAmelCase     :						Dict=True       ,  **__UpperCAmelCase     :						List[str]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             _UpperCAmelCase       ,  _UpperCAmelCase       ,  tokenizer_file=_UpperCAmelCase       ,  errors=_UpperCAmelCase       ,  bos_token=_UpperCAmelCase       ,  eos_token=_UpperCAmelCase       ,  sep_token=_UpperCAmelCase       ,  cls_token=_UpperCAmelCase       ,  unk_token=_UpperCAmelCase       ,  pad_token=_UpperCAmelCase       ,  mask_token=_UpperCAmelCase       ,  add_prefix_space=_UpperCAmelCase       ,  trim_offsets=_UpperCAmelCase       ,  **_UpperCAmelCase       ,  )

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

         UpperCAmelCase__								=  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase__								=  '''post_processor'''
         UpperCAmelCase__								=  getattr(self.backend_tokenizer       ,  _UpperCAmelCase       ,  _UpperCAmelCase		)
         if tokenizer_component_instance:
               UpperCAmelCase__								=  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:
                     UpperCAmelCase__								=  tuple(state["sep"]		)
               if "cls" in state:
                     UpperCAmelCase__								=  tuple(state["cls"]		)

               UpperCAmelCase__								=  False

               if state.get("add_prefix_space"       ,  _UpperCAmelCase		) != add_prefix_space:
                     UpperCAmelCase__								=  add_prefix_space
                     UpperCAmelCase__								=  True

               if state.get("trim_offsets"       ,  _UpperCAmelCase		) != trim_offsets:
                     UpperCAmelCase__								=  trim_offsets
                     UpperCAmelCase__								=  True

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


   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def 			lowercase_      (self     :						str		)						->				Any:
         """simple docstring"""



         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 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(_UpperCAmelCase       ,  lstrip=_UpperCAmelCase       ,  rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase       ,  _UpperCAmelCase		) else value
         UpperCAmelCase__								=  value


   def 			lowercase_      (self     :						Dict       ,  *__UpperCAmelCase     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"       ,  _UpperCAmelCase		)

         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(*_UpperCAmelCase       ,  **_UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]       ,  *__UpperCAmelCase     :						str       ,  **__UpperCAmelCase     :						int		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  kwargs.get("is_split_into_words"       ,  _UpperCAmelCase		)

         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(*_UpperCAmelCase       ,  **_UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Union[str, Any] = None		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self._tokenizer.model.save(_UpperCAmelCase       ,  name=_UpperCAmelCase		)
         return tuple(_UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						str=None		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  [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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Dict = None		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [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 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, Any] = None       ,  __UpperCAmelCase     :						str = PaddingStrategy.DO_NOT_PAD       ,  __UpperCAmelCase     :						List[Any] = None       ,  __UpperCAmelCase     :						Tuple = None       ,  )						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  super()._pad(
             encoded_inputs=_UpperCAmelCase       ,  max_length=_UpperCAmelCase       ,  padding_strategy=_UpperCAmelCase       ,  pad_to_multiple_of=_UpperCAmelCase       ,  return_attention_mask=_UpperCAmelCase       ,  )

         # Load from model defaults
         if return_attention_mask is None:
               UpperCAmelCase__								=  '''attention_mask''' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
               UpperCAmelCase__								=  encoded_inputs[self.model_input_names[0]]
               # `global_attention_mask` need to have the same length as other (sequential) inputs.
               UpperCAmelCase__								=  len(encoded_inputs["global_attention_mask"]		) != len(_UpperCAmelCase		)

               if needs_to_be_padded:
                     UpperCAmelCase__								=  len(_UpperCAmelCase		) - 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`
                           UpperCAmelCase__								=  (
                               encoded_inputs['''global_attention_mask'''] + [-1] * difference
                           )
                     elif self.padding_side == "left":
                           UpperCAmelCase__								=  [-1] * difference + encoded_inputs[
                               '''global_attention_mask'''
                           ]
                     else:
                           raise ValueError("Invalid padding strategy:" + str(self.padding_side		)		)

         return encoded_inputs




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
