




from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase__ :   str					=							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> List[str]:
      if isinstance(__UpperCAmelCase			,			torch.Tensor							):
            return image
      elif isinstance(__UpperCAmelCase			,			PIL.Image.Image							):
            SCREAMING_SNAKE_CASE_							=			[image]

      SCREAMING_SNAKE_CASE_							=			[trans(img.convert('RGB'							)							) for img in image]
      SCREAMING_SNAKE_CASE_							=			torch.stack(__UpperCAmelCase							)
      return image





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		List[Any]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		int						):
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       SCREAMING_SNAKE_CASE_							=			DDIMScheduler.from_config(scheduler.config						)

       self.register_modules(unet=_lowerCAmelCase				,				scheduler=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		Tuple						):
       if strength < 0 or strength > 1:
             raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Tuple						):
       # get the original timestep using init_timestep
       SCREAMING_SNAKE_CASE_							=			min(int(num_inference_steps * strength						)				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			max(num_inference_steps - init_timestep				,				0						)
       SCREAMING_SNAKE_CASE_							=			self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start



 def       lowerCAmelCase_  (							self  :		Dict				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any=None						):
       if not isinstance(_lowerCAmelCase				,				(torch.Tensor, PIL.Image.Image, list)						):
             raise ValueError(
                 F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase						)}"						)

       SCREAMING_SNAKE_CASE_							=			image.to(device=_lowerCAmelCase				,				dtype=_lowerCAmelCase						)

       if isinstance(_lowerCAmelCase				,				_lowerCAmelCase						) and len(_lowerCAmelCase						) != batch_size:
             raise ValueError(
                 F"You have passed a list of generators of length {len(_lowerCAmelCase						)}, but requested an effective batch"
                 F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

       SCREAMING_SNAKE_CASE_							=			init_latents.shape
       SCREAMING_SNAKE_CASE_							=			randn_tensor(_lowerCAmelCase				,				generator=_lowerCAmelCase				,				device=_lowerCAmelCase				,				dtype=_lowerCAmelCase						)

       # get latents
       print('add noise to latents at timestep'				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.scheduler.add_noise(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			init_latents

       return latents




 @torch.no_grad()
 def __call__(							self  :		str				,				_lowerCAmelCase  :		Union[torch.FloatTensor, PIL.Image.Image] = None				,				_lowerCAmelCase  :		float = 0.8				,				_lowerCAmelCase  :		int = 1				,				_lowerCAmelCase  :		Optional[Union[torch.Generator, List[torch.Generator]]] = None				,				_lowerCAmelCase  :		float = 0.0				,				_lowerCAmelCase  :		int = 50				,				_lowerCAmelCase  :		Optional[bool] = None				,				_lowerCAmelCase  :		Optional[str] = "pil"				,				_lowerCAmelCase  :		bool = True				,				):
       self.check_inputs(_lowerCAmelCase						)

       # 2. Preprocess image
       SCREAMING_SNAKE_CASE_							=			preprocess(_lowerCAmelCase						)

       # 3. set timesteps
       self.scheduler.set_timesteps(_lowerCAmelCase				,				device=self.device						)
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.get_timesteps(_lowerCAmelCase				,				_lowerCAmelCase				,				self.device						)
       SCREAMING_SNAKE_CASE_							=			timesteps[:1].repeat(_lowerCAmelCase						)

       # 4. Prepare latent variables
       SCREAMING_SNAKE_CASE_							=			self.prepare_latents(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				self.unet.dtype				,				self.device				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			latents

       # 5. Denoising loop
       for t in self.progress_bar(_lowerCAmelCase						):
             # 1. predict noise model_output
             SCREAMING_SNAKE_CASE_							=			self.unet(_lowerCAmelCase				,				_lowerCAmelCase						).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             SCREAMING_SNAKE_CASE_							=			self.scheduler.step(
                 _lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				eta=_lowerCAmelCase				,				use_clipped_model_output=_lowerCAmelCase				,				generator=_lowerCAmelCase				,				).prev_sample

       SCREAMING_SNAKE_CASE_							=			(image / 2 + 0.5).clamp(0				,				1						)
       SCREAMING_SNAKE_CASE_							=			image.cpu().permute(0				,				2				,				3				,				1						).numpy()
       if output_type == "pil":
             SCREAMING_SNAKE_CASE_							=			self.numpy_to_pil(_lowerCAmelCase						)

       if not return_dict:
             return (image, latent_timestep.item())

       return ImagePipelineOutput(images=_lowerCAmelCase						)





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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


lowerCamelCase__ :   List[str]					=							{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__ :   Union[str, Any]					=							{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__ :   Optional[Any]					=							'</w>'
lowerCamelCase__ :   Union[str, Any]					=							'@@ '

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]							)					-> Dict:
      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


# Speech2Text2 has no max input length
lowerCamelCase__ :   Any					=							{'facebook/s2t-wav2vec2-large-en-de': 1_024}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]="<s>"				,				_lowerCAmelCase  :		Any="<pad>"				,				_lowerCAmelCase  :		List[str]="</s>"				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Optional[Any]=False				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		Tuple				,				):
       super().__init__(
           unk_token=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			do_lower_case

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_							=			json.load(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{v: k for k, v in self.encoder.items()}

       if merges_file is None:
             logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding."						)

             SCREAMING_SNAKE_CASE_							=			None
             SCREAMING_SNAKE_CASE_							=			None
       else:
             with open(_lowerCAmelCase				,				encoding='utf-8'						) as merges_handle:
                   SCREAMING_SNAKE_CASE_							=			merges_handle.read().split('\n'						)[:-1]

             SCREAMING_SNAKE_CASE_							=			[tuple(merge.split()[:2]						) for merge in merges]
             SCREAMING_SNAKE_CASE_							=			dict(zip(_lowerCAmelCase				,				range(len(_lowerCAmelCase						)						)						)						)
             SCREAMING_SNAKE_CASE_							=			{}



 @property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return len(self.decoder						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return dict(self.encoder				,				**self.added_tokens_encoder						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)

       if not pairs:
             return token

       while True:
             SCREAMING_SNAKE_CASE_							=			min(_lowerCAmelCase				,				key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase				,				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(_lowerCAmelCase						):
                   try:
                         SCREAMING_SNAKE_CASE_							=			word.index(_lowerCAmelCase				,				_lowerCAmelCase						)
                   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(_lowerCAmelCase						) - 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(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			new_word
             if len(_lowerCAmelCase						) == 1:
                   break
             else:
                   SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)
       if word == "\n  " + BPE_TOKEN_MERGES:
             SCREAMING_SNAKE_CASE_							=			'\n' + BPE_TOKEN_MERGES

       if word.endswith(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			word.replace(_lowerCAmelCase				,				''						)

       SCREAMING_SNAKE_CASE_							=			word.replace(' '				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			word
       return word



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[int]						):

       if self.bpe_ranks is None:
             raise ValueError(
                 'This tokenizer was instantiated without a `merges.txt` file, so'
                 ' that it can only be used for decoding, not for encoding.'
                 'Make sure to provide `merges.txt` file at instantiation to enable '
                 'encoding.'						)

       if self.do_lower_case:
             SCREAMING_SNAKE_CASE_							=			text.lower()

       SCREAMING_SNAKE_CASE_							=			text.split()

       SCREAMING_SNAKE_CASE_							=			[]
       for token in text:
             if token:
                   split_tokens.extend(list(self.bpe(_lowerCAmelCase						).split(' '						)						)						)

       return split_tokens



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str						):
       return self.encoder.get(_lowerCAmelCase				,				self.encoder.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.decoder.get(_lowerCAmelCase				,				self.unk_token						)
       return result



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)

       # make sure @@ tokens are concatenated
       SCREAMING_SNAKE_CASE_							=			''.join(string.split(_lowerCAmelCase						)						)

       return string




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
             return
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

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

       SCREAMING_SNAKE_CASE_							=			0
       if self.bpe_ranks is None:
             return (vocab_file,)

       with open(_lowerCAmelCase				,				'w'				,				encoding='utf-8'						) as writer:
             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 {merges_file}: BPE merge indices are not consecutive."
                             ' Please check that the tokenizer is not corrupted!'						)
                         SCREAMING_SNAKE_CASE_							=			token_index
                   writer.write(' '.join(_lowerCAmelCase						) + '\n'						)
                   index += 1

       return (vocab_file, merges_file)