



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

UpperCAmelCase    :		Any      =			logging.get_logger(__name__)


UpperCAmelCase    :		List[Any]      =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase    :		Optional[Any]      =			{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

UpperCAmelCase    :		Optional[Any]      =			{"facebook/blenderbot-3B": 128}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       _SCREAMING_SNAKE_CASE    ()			->    str:







  '''simple docstring'''


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


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase				)			->    Optional[int]:







  '''simple docstring'''


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


class  SCREAMING_SNAKE_CASE__			(				UpperCamelCase__ ):
    lowercase__  			= VOCAB_FILES_NAMES
    lowercase__  			= PRETRAINED_VOCAB_FILES_MAP
    lowercase__  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__  			= ["input_ids", "attention_mask"]

    def __init__(					self      :					Dict			,	lowerCAmelCase_      :					Tuple			,	lowerCAmelCase_      :					Union[str, Any]			,	lowerCAmelCase_      :					int="replace"			,	lowerCAmelCase_      :					int="<s>"			,	lowerCAmelCase_      :					str="</s>"			,	lowerCAmelCase_      :					Dict="</s>"			,	lowerCAmelCase_      :					List[str]="<s>"			,	lowerCAmelCase_      :					Dict="<unk>"			,	lowerCAmelCase_      :					Optional[Any]="<pad>"			,	lowerCAmelCase_      :					Tuple="<mask>"			,	lowerCAmelCase_      :					List[Any]=False			,	**lowerCAmelCase_      :					List[Any]			,	):







      """simple docstring"""







      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else bos_token
      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else eos_token
      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else sep_token
      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else cls_token
      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else unk_token
      lowercase_             =			AddedToken(__A			,	lstrip=__A			,	rstrip=__A) if isinstance(__A			,	__A) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase_             =			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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
    def        _UpperCAmelCase						(					self      :					Dict):







      """simple docstring"""







      return len(self.encoder)

    def        _UpperCAmelCase						(					self      :					Tuple):







      """simple docstring"""







      return dict(self.encoder			,	**self.added_tokens_encoder)

    def        _UpperCAmelCase						(					self      :					int			,	lowerCAmelCase_      :					List[Any]):







      """simple docstring"""







      if token in self.cache:
        return self.cache[token]
      lowercase_             =			tuple(__A)
      lowercase_             =			get_pairs(__A)

      if not pairs:
        return token

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

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

    def        _UpperCAmelCase						(					self      :					Union[str, Any]			,	lowerCAmelCase_      :					List[str]):







      """simple docstring"""







      lowercase_             =			[]
      for token in re.findall(self.pat			,	__A):
        lowercase_             =			"""""".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(__A).split(""" """))
      return bpe_tokens

    def        _UpperCAmelCase						(					self      :					Union[str, Any]			,	lowerCAmelCase_      :					Union[str, Any]):







      """simple docstring"""







      return self.encoder.get(__A			,	self.encoder.get(self.unk_token))

    def        _UpperCAmelCase						(					self      :					int			,	lowerCAmelCase_      :					Tuple):







      """simple docstring"""







      return self.decoder.get(__A)

    def        _UpperCAmelCase						(					self      :					Any			,	lowerCAmelCase_      :					Tuple):







      """simple docstring"""







      lowercase_             =			"""""".join(__A)
      lowercase_             =			bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""			,	errors=self.errors)
      return text

    def        _UpperCAmelCase						(					self      :					List[Any]			,	lowerCAmelCase_      :					str			,	lowerCAmelCase_      :					Optional[str] = None):







      """simple docstring"""







      if not os.path.isdir(__A):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
        return
      lowercase_             =			os.path.join(
          __A			,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
      lowercase_             =			os.path.join(
          __A			,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

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

      lowercase_             =			0
      with open(__A			,	"""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 lowerCAmelCase_: 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!""")
            lowercase_             =			token_index
          writer.write(""" """.join(__A) + """\n""")
          index += 1

      return vocab_file, merge_file

    def        _UpperCAmelCase						(					self      :					str			,	lowerCAmelCase_      :					List[int]			,	lowerCAmelCase_      :					Optional[List[int]] = None			,	lowerCAmelCase_      :					bool = False):







      """simple docstring"""







      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__A			,	token_ids_a=__A			,	already_has_special_tokens=__A)

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

    def        _UpperCAmelCase						(					self      :					Any			,	lowerCAmelCase_      :					List[int]			,	lowerCAmelCase_      :					Optional[List[int]] = None):







      """simple docstring"""







      lowercase_             =			[self.sep_token_id]
      lowercase_             =			[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        _UpperCAmelCase						(					self      :					Optional[int]			,	lowerCAmelCase_      :					Union[str, Any]			,	lowerCAmelCase_      :					int=False			,	**lowerCAmelCase_      :					Optional[Any]):







      """simple docstring"""







      lowercase_             =			kwargs.pop("""add_prefix_space"""			,	self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(__A) > 0 and not text[0].isspace()):
        lowercase_             =			""" """ + text
      return (text, kwargs)

    def        _UpperCAmelCase						(					self      :					Any			,	lowerCAmelCase_      :					List[int]			,	lowerCAmelCase_      :					Optional[List[int]] = None):







      """simple docstring"""







      return token_ids_a + [self.eos_token_id]




    def        _UpperCAmelCase						(					self      :					Any			,	lowerCAmelCase_      :					"Conversation"):







      """simple docstring"""







      lowercase_             =			[]
      for is_user, text in conversation.iter_texts():
        if is_user:
          # We need to space prefix as it's being done within blenderbot
          inputs.append(""" """ + text)
        else:
          # Generated responses should contain them already.
          inputs.append(__A)

      lowercase_             =			"""  """.join(__A)
      lowercase_             =			self.encode(__A)
      if len(__A) > self.model_max_length:
        lowercase_             =			input_ids[-self.model_max_length :]
        logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''')
      return input_ids








import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




@slow
@require_torch_gpu
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Union[str, Any]		) -> List[str]:



									"""simple docstring"""



									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def   _snake_case	(				self     :Any		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_euler"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe([prompt]				,					generator=__A				,					guidance_scale=9.0				,					num_inference_steps=20				,					output_type="""np"""		)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]		)

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


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_euler"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe([prompt]				,					generator=__A				,					guidance_scale=9.0				,					num_inference_steps=20				,					output_type="""np"""		)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-1


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_dpmpp_2m"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=7.5				,					num_inference_steps=15				,					output_type="""np"""				,					use_karras_sigmas=__A				,					)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array(
									    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]		)

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