



'''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

lowercase_	 =						logging.get_logger(__name__)


lowercase_	 =						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_	 =						{
    "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"
    },
}

lowercase_	 =						{"facebook/blenderbot-3B": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase						():



     """simple docstring"""



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




def lowerCAmelCase						(__A):



     """simple docstring"""



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






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = VOCAB_FILES_NAMES
 __lowerCamelCase					: Tuple    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: int    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: str    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A				,     A="replace"				,     A="<s>"				,     A="</s>"				,     A="</s>"				,     A="<s>"				,     A="<unk>"				,     A="<pad>"				,     A="<mask>"				,     A=False				,     **A				,     )					->  Optional[Any]:


      """simple docstring"""



      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else bos_token
      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else eos_token
      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else sep_token
      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else cls_token
      _a											=						AddedToken(A				,     lstrip=A				,     rstrip=A ) if isinstance(A				,     A ) else unk_token
      _a											=						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
      _a											=						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:
           _a											=						json.load(A )
      _a											=						{v: k for k, v in self.encoder.items()}
      _a											=						errors  # how to handle errors in decoding
      _a											=						bytes_to_unicode()
      _a											=						{v: k for k, v in self.byte_encoder.items()}
      with open(A				,     encoding='''utf-8''' ) as merges_handle:
           _a											=						merges_handle.read().split('''\n''' )[1:-1]
      _a											=						[tuple(merge.split() ) for merge in bpe_merges]
      _a											=						dict(zip(A				,     range(len(A ) ) ) )
      _a											=						{}
      _a											=						add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _a											=						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       a__						(self )					->  str:


      """simple docstring"""



      return len(self.encoder )



 def       a__						(self )					->  Dict:


      """simple docstring"""



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



 def       a__						(self				,     A )					->  int:


      """simple docstring"""



      if token in self.cache:
           return self.cache[token]
      _a											=						tuple(A )
      _a											=						get_pairs(A )

      if not pairs:
           return token

      while True:
           _a											=						min(A				,     key=lambda A : self.bpe_ranks.get(A				,     float('''inf''' ) ) )
           if bigram not in self.bpe_ranks:
                break
           _a						,					_a											=						bigram
           _a											=						[]
           _a											=						0
           while i < len(A ):
                try:
                     _a											=						word.index(A				,     A )
                except ValueError:
                     new_word.extend(word[i:] )
                     break
                else:
                     new_word.extend(word[i:j] )
                     _a											=						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
           _a											=						tuple(A )
           _a											=						new_word
           if len(A ) == 1:
                break
           else:
                _a											=						get_pairs(A )
      _a											=						''' '''.join(A )
      _a											=						word
      return word



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



      _a											=						[]
      for token in re.findall(self.pat				,     A ):
           _a											=						''''''.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       a__						(self				,     A )					->  List[Any]:


      """simple docstring"""



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



 def       a__						(self				,     A )					->  Dict:


      """simple docstring"""



      return self.decoder.get(A )



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


      """simple docstring"""



      _a											=						''''''.join(A )
      _a											=						bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''				,     errors=self.errors )
      return text



 def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



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

      _a											=						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 A : 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!''' )
                     _a											=						token_index
                writer.write(''' '''.join(A ) + '''\n''' )
                index += 1

      return vocab_file, merge_file



 def       a__						(self				,     A				,     A = None				,     A = False )					->  List[int]:


      """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       a__						(self				,     A				,     A = None )					->  List[int]:


      """simple docstring"""



      _a											=						[self.sep_token_id]
      _a											=						[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				,     A				,     A=False				,     **A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						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()):
           _a											=						''' ''' + text
      return (text, kwargs)



 def       a__						(self				,     A				,     A = None )					->  Optional[int]:


      """simple docstring"""



      return token_ids_a + [self.eos_token_id]



 def       a__						(self				,     A )					->  List[int]:


      """simple docstring"""



      _a											=						[]
      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 )

      _a											=						'''  '''.join(A )
      _a											=						self.encode(A )
      if len(A ) > self.model_max_length:
           _a											=						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








'''simple docstring'''





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



 def       a__						(self				,     A=0 )					->  str:


      """simple docstring"""



      _a											=						floats_tensor((1, 3, 128, 128)				,     rng=random.Random(A ) )
      _a											=						np.random.RandomState(A )
      _a											=						{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''strength''': 0.75,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087] )
      assert np.abs(image_slice - expected_slice ).max() < 1E-1



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=A )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154] )

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



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      # warmup pass to apply optimizations
      _a											=						pipe(**self.get_dummy_inputs() )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203] )

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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

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



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )

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



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''' )
      _a											=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
      pipe.set_progress_bar_config(disable=A )

      _a											=						self.get_dummy_inputs()
      _a											=						pipe(**A ).images
      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      _a											=						np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552] )

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






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



 '''simple docstring'''







 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						ort.SessionOptions()
      _a											=						False
      return options



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      # using the PNDM scheduler by default
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''CompVis/stable-diffusion-v1-4'''				,     revision='''onnx'''				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/img2img/sketch-mountains-input.jpg''' )
      _a											=						init_image.resize((768, 512) )
      _a											=						LMSDiscreteScheduler.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx''' )
      _a											=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=A				,     safety_checker=A				,     feature_extractor=A				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''A fantasy landscape, trending on artstation'''

      _a											=						np.random.RandomState(0 )
      _a											=						pipe(
          prompt=A				,     image=A				,     strength=0.75				,     guidance_scale=7.5				,     num_inference_steps=20				,     generator=A				,     output_type='''np'''				,     )
      _a											=						output.images
      _a											=						images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      _a											=						np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



