


'''simple docstring'''

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

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


a_  :				Tuple						= logging.get_logger(__name__)


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

a_  :				Optional[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"""
        ),
    },
}

a_  :				List[str]						= """</w>"""
a_  :				List[str]						= """@@ """





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  set()
 lowerCamelCase_      =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  lowerCamelCase_      =  char
 return pairs


# Speech2Text2 has no max input length
a_  :				Any						= {"""facebook/s2t-wav2vec2-large-en-de""": 1024}







class      snake_case    (    lowercase			):





  """simple docstring"""




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






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase="<s>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase=False			,  UpperCamelCase=None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(
       unk_token=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  do_lower_case=UpperCamelCase			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  do_lower_case

   with open(UpperCamelCase			,  encoding="utf-8"     ) as vocab_handle:
    lowerCamelCase_      =  json.load(UpperCamelCase     )
   lowerCamelCase_      =  {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.'''     )

    lowerCamelCase_      =  None
    lowerCamelCase_      =  None
   else:
    with open(UpperCamelCase			,  encoding="utf-8"     ) as merges_handle:
     lowerCamelCase_      =  merges_handle.read().split("\n"     )[:-1]

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






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.decoder     )






  def   snake_case		(    self     ):







   """simple docstring"""
   return dict(self.encoder			,  **self.added_tokens_encoder     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  tuple(token[:-1]     ) + (token[-1] + BPE_TOKEN_MERGES,)
   if token in self.cache:
    return self.cache[token]
   lowerCamelCase_      =  get_pairs(UpperCamelCase     )

   if not pairs:
    return token

   while True:
    lowerCamelCase_      =  min(UpperCamelCase			,  key=lambda UpperCamelCase     : self.bpe_ranks.get(UpperCamelCase			,  float("inf"     )     )     )
    if bigram not in self.bpe_ranks:
     break
    lowerCamelCase_ ,lowerCamelCase_      =  bigram
    lowerCamelCase_      =  []
    lowerCamelCase_      =  0
    while i < len(UpperCamelCase     ):
     try:
      lowerCamelCase_      =  word.index(UpperCamelCase			,  UpperCamelCase     )
     except ValueError:
      new_word.extend(word[i:]     )
      break
     else:
      new_word.extend(word[i:j]     )
      lowerCamelCase_      =  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
    lowerCamelCase_      =  tuple(UpperCamelCase     )
    lowerCamelCase_      =  new_word
    if len(UpperCamelCase     ) == 1:
     break
    else:
     lowerCamelCase_      =  get_pairs(UpperCamelCase     )
   lowerCamelCase_      =  " ".join(UpperCamelCase     )
   if word == "\n  " + BPE_TOKEN_MERGES:
    lowerCamelCase_      =  "\n" + BPE_TOKEN_MERGES

   if word.endswith(UpperCamelCase     ):
    lowerCamelCase_      =  word.replace(UpperCamelCase			,  ""     )

   lowerCamelCase_      =  word.replace(" "			,  UpperCamelCase     )
   lowerCamelCase_      =  word
   return word






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""

   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:
    lowerCamelCase_      =  text.lower()

   lowerCamelCase_      =  text.split()

   lowerCamelCase_      =  []
   for token in text:
    if token:
     split_tokens.extend(list(self.bpe(UpperCamelCase     ).split(" "     )     )     )

   return split_tokens






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.encoder.get(UpperCamelCase			,  self.encoder.get(self.unk_token     )     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.decoder.get(UpperCamelCase			,  self.unk_token     )
   return result






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  " ".join(UpperCamelCase     )

   # make sure @@ tokens are concatenated
   lowerCamelCase_      =  "".join(string.split(UpperCamelCase     )     )

   return string







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







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

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

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

   return (vocab_file, merges_file)







'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class      snake_case    (    lowercase			,						lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  StableUnCLIPPipeline
  _lowerCamelCase		    =  TEXT_TO_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  32
   lowerCamelCase_      =  embedder_hidden_size

   # prior components

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=UpperCamelCase			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  PriorTransformer(
       num_attention_heads=2			,  attention_head_dim=12			,  embedding_dim=UpperCamelCase			,  num_layers=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDPMScheduler(
       variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=UpperCamelCase			,  clip_sample_range=5.0			,  beta_schedule="squaredcos_cap_v2"			,  )

   # regular denoising components

   torch.manual_seed(0     )
   lowerCamelCase_      =  StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase     )
   lowerCamelCase_      =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=32			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  UNetaDConditionModel(
       sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,  block_out_channels=(32, 64)			,  attention_head_dim=(2, 4)			,  class_embed_type="projection"			,  projection_class_embeddings_input_dim=embedder_projection_dim * 2			,  cross_attention_dim=UpperCamelCase			,  layers_per_block=1			,  upcast_attention=UpperCamelCase			,  use_linear_projection=UpperCamelCase			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDIMScheduler(
       beta_schedule="scaled_linear"			,  beta_start=0.00_085			,  beta_end=0.012			,  prediction_type="v_prediction"			,  set_alpha_to_one=UpperCamelCase			,  steps_offset=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  AutoencoderKL()

   lowerCamelCase_      =  {
       # prior components
       "prior_tokenizer": prior_tokenizer,
       "prior_text_encoder": prior_text_encoder,
       "prior": prior,
       "prior_scheduler": prior_scheduler,
       # image noising components
       "image_normalizer": image_normalizer,
       "image_noising_scheduler": image_noising_scheduler,
       # regular denoising components
       "tokenizer": tokenizer,
       "text_encoder": text_encoder,
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
   }

   return components






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
   lowerCamelCase_      =  {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "prior_num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device == "cpu"

   self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase     )





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"     )

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  torch.Generator(device="cpu"     ).manual_seed(0     )
   lowerCamelCase_      =  pipe("anime turle"			,  generator=UpperCamelCase			,  output_type="np"     )

   lowerCamelCase_      =  output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(UpperCamelCase			,  UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   lowerCamelCase_      =  pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  pipe(
       "anime turtle"			,  prior_num_inference_steps=2			,  num_inference_steps=2			,  output_type="np"			,  )

   lowerCamelCase_      =  torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9



