

"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			int											= {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
A_							:			Union[str, Any]											= {
    "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_							:			List[str]											= {
    "allenai/led-base-16384": 16_384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      (
			    list(range(ord("""!"""    )		,				ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )		,				ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )		,				ord("""ÿ"""    ) + 1    )    )
			)
			SCREAMING_SNAKE_CASE__								=      bs[:]
			SCREAMING_SNAKE_CASE__								=      0
			for b in range(2**8    ):
						if b not in bs:
									bs.append(snake_case__    )
									cs.append(2**8 + n    )
									n += 1
			SCREAMING_SNAKE_CASE__								=      [chr(snake_case__    ) for n in cs]
			return dict(zip(snake_case__		,				snake_case__    )    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      set()
			SCREAMING_SNAKE_CASE__								=      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)    )
						SCREAMING_SNAKE_CASE__								=      char
			return pairs





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Union[str, Any]							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		List[str]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		str							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		List[str]							=							['input_ids', 'attention_mask']

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[Any]="replace"    ,				__UpperCAmelCase				:						Tuple="<s>"    ,				__UpperCAmelCase				:						List[Any]="</s>"    ,				__UpperCAmelCase				:						Optional[int]="</s>"    ,				__UpperCAmelCase				:						Optional[Any]="<s>"    ,				__UpperCAmelCase				:						Dict="<unk>"    ,				__UpperCAmelCase				:						str="<pad>"    ,				__UpperCAmelCase				:						int="<mask>"    ,				__UpperCAmelCase				:						int=False    ,				**__UpperCAmelCase				:						Optional[Any]    ,				)   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else bos_token
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else eos_token
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else sep_token
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else cls_token
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else unk_token
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE__								=      AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else mask_token

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

						with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as vocab_handle:
									SCREAMING_SNAKE_CASE__								=      json.load(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}
						SCREAMING_SNAKE_CASE__								=      errors  # how to handle errors in decoding
						SCREAMING_SNAKE_CASE__								=      bytes_to_unicode()
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.byte_encoder.items()}
						with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as merges_handle:
									SCREAMING_SNAKE_CASE__								=      merges_handle.read().split("""\n"""       )[1:-1]
						SCREAMING_SNAKE_CASE__								=      [tuple(merge.split()       ) for merge in bpe_merges]
						SCREAMING_SNAKE_CASE__								=      dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase       )       )       )       )
						SCREAMING_SNAKE_CASE__								=      {}
						SCREAMING_SNAKE_CASE__								=      add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						SCREAMING_SNAKE_CASE__								=      re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )

			@property
			# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						return len(self.encoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:
						return dict(self.encoder    ,				**self.added_tokens_encoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[str]       )   ->    Optional[int]:
						if token in self.cache:
									return self.cache[token]
						SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )

						if not pairs:
									return token

						while True:
									SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase       : self.bpe_ranks.get(__UpperCAmelCase    ,				float("""inf"""       )       )       )
									if bigram not in self.bpe_ranks:
												break
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      bigram
									SCREAMING_SNAKE_CASE__								=      []
									SCREAMING_SNAKE_CASE__								=      0
									while i < len(__UpperCAmelCase       ):
												try:
															SCREAMING_SNAKE_CASE__								=      word.index(__UpperCAmelCase    ,				__UpperCAmelCase       )
												except ValueError:
															new_word.extend(word[i:]       )
															break
												else:
															new_word.extend(word[i:j]       )
															SCREAMING_SNAKE_CASE__								=      j

												if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
															new_word.append(first + second       )
															i += 2
												else:
															new_word.append(word[i]       )
															i += 1
									SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      new_word
									if len(__UpperCAmelCase       ) == 1:
												break
									else:
												SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      word
						return word

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						List[str]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      []
						for token in re.findall(self.pat    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      """""".join(
									    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase       ).split(""" """       )       )
						return bpe_tokens

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						int       )   ->    Dict:
						return self.encoder.get(__UpperCAmelCase    ,				self.encoder.get(self.unk_token       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Any       )   ->    Dict:
						return self.decoder.get(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      """""".join(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""    ,				errors=self.errors       )
						return text

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
									return
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as f:
									f.write(json.dumps(self.encoder    ,				indent=2    ,				sort_keys=__UpperCAmelCase    ,				ensure_ascii=__UpperCAmelCase       ) + """\n"""       )

						SCREAMING_SNAKE_CASE__								=      0
						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as writer:
									writer.write("""#version: 0.2\n"""       )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,				key=lambda __UpperCAmelCase       : kv[1]       ):
												if index != token_index:
															logger.warning(
															    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
															    """ Please check that the tokenizer is not corrupted!"""       )
															SCREAMING_SNAKE_CASE__								=      token_index
												writer.write(""" """.join(__UpperCAmelCase       ) + """\n"""       )
												index += 1

						return vocab_file, merge_file

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.cls_token_id]
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:
						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase       )

						if token_ids_a is None:
									return [1] + ([0] * len(__UpperCAmelCase       )) + [1]
						return [1] + ([0] * len(__UpperCAmelCase       )) + [1, 1] + ([0] * len(__UpperCAmelCase       )) + [1]

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [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 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						List[str]=False    ,				**__UpperCAmelCase				:						Tuple       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      kwargs.pop("""add_prefix_space"""    ,				self.add_prefix_space       )
						if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase       ) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE__								=      """ """ + text
						return (text, kwargs)





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[Dict[str, EncodedInput], BatchEncoding]    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				)   ->    dict:
						SCREAMING_SNAKE_CASE__								=      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:
									SCREAMING_SNAKE_CASE__								=      """attention_mask""" in self.model_input_names

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

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

						return encoded_inputs





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """A red cat sitting on a park bench"""

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


