
"""simple docstring"""
import json
import os
from typing import Optional, Tuple

import regex as re

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


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

_a      :			str					=							{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_a      :			Tuple					=							{
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

_a      :			Optional[int]					=							{
    """ctrl""": 256,
}

_a      :			Any					=							{
    """Pregnancy""": 168_629,
    """Christianity""": 7_675,
    """Explain""": 106_423,
    """Fitness""": 63_440,
    """Saving""": 63_163,
    """Ask""": 27_171,
    """Ass""": 95_985,
    """Joke""": 163_509,
    """Questions""": 45_622,
    """Thoughts""": 49_605,
    """Retail""": 52_342,
    """Feminism""": 164_338,
    """Writing""": 11_992,
    """Atheism""": 192_263,
    """Netflix""": 48_616,
    """Computing""": 39_639,
    """Opinion""": 43_213,
    """Alone""": 44_967,
    """Funny""": 58_917,
    """Gaming""": 40_358,
    """Human""": 4_088,
    """India""": 1_331,
    """Joker""": 77_138,
    """Diet""": 36_206,
    """Legal""": 11_859,
    """Norman""": 4_939,
    """Tip""": 72_689,
    """Weight""": 52_343,
    """Movies""": 46_273,
    """Running""": 23_425,
    """Science""": 2_090,
    """Horror""": 37_793,
    """Confession""": 60_572,
    """Finance""": 12_250,
    """Politics""": 16_360,
    """Scary""": 191_985,
    """Support""": 12_654,
    """Technologies""": 32_516,
    """Teenage""": 66_160,
    """Event""": 32_769,
    """Learned""": 67_460,
    """Notion""": 182_770,
    """Wikipedia""": 37_583,
    """Books""": 6_665,
    """Extract""": 76_050,
    """Confessions""": 102_701,
    """Conspiracy""": 75_932,
    """Links""": 63_674,
    """Narcissus""": 150_425,
    """Relationship""": 54_766,
    """Relationships""": 134_796,
    """Reviews""": 41_671,
    """News""": 4_256,
    """Translation""": 26_820,
    """multilingual""": 128_406,
}
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						List[str]						=   set()
  _snake_case     :						Dict						=   word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)		)
    _snake_case     :						Union[str, Any]						=   char

  _snake_case     :						Dict						=   set(a		)
  return pairs






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					VOCAB_FILES_NAMES
    __lowercase							: str  =					PRETRAINED_VOCAB_FILES_MAP
    __lowercase							: List[str]  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase							: List[Any]  =					CONTROL_CODES
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_="<unk>"						,    **snake_case_     ):
      super().__init__(unk_token=snake_case_						,    **snake_case_     )

      with open(snake_case_						,    encoding="utf-8"     ) as vocab_handle:
        _snake_case     :						Dict						=   json.load(snake_case_     )
      _snake_case     :						Dict						=   {v: k for k, v in self.encoder.items()}
      with open(snake_case_						,    encoding="utf-8"     ) as merges_handle:
        _snake_case     :						str						=   merges_handle.read().split("\n"     )[1:-1]
      _snake_case     :						int						=   [tuple(merge.split()     ) for merge in merges]
      _snake_case     :						Tuple						=   dict(zip(snake_case_						,    range(len(snake_case_     )     )     )     )
      _snake_case     :						int						=   {}
    @property
    def 					lowerCamelCase__	(    self     ):
      return len(self.encoder     )
    def 					lowerCamelCase__	(    self     ):
      return dict(self.encoder						,    **self.added_tokens_encoder     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      if token in self.cache:
        return self.cache[token]
      _snake_case     :						List[str]						=   tuple(snake_case_     )
      _snake_case     :						Tuple						=   tuple(list(word[:-1]     ) + [word[-1] + "</w>"]     )
      _snake_case     :						List[Any]						=   get_pairs(snake_case_     )

      if not pairs:
        return token

      while True:
        _snake_case     :						Any						=   min(snake_case_						,    key=lambda snake_case_     : self.bpe_ranks.get(snake_case_						,    float("inf"     )     )     )
        if bigram not in self.bpe_ranks:
          break
        _snake_case     :						List[str]						=   bigram
        _snake_case     :						Any						=   []
        _snake_case     :						Dict						=   0
        while i < len(snake_case_     ):
          try:
            _snake_case     :						int						=   word.index(snake_case_						,    snake_case_     )
          except ValueError:
            new_word.extend(word[i:]     )
            break
          else:
            new_word.extend(word[i:j]     )
            _snake_case     :						Optional[Any]						=   j

          if word[i] == first and i < len(snake_case_     ) - 1 and word[i + 1] == second:
            new_word.append(first + second     )
            i += 2
          else:
            new_word.append(word[i]     )
            i += 1
        _snake_case     :						str						=   tuple(snake_case_     )
        _snake_case     :						Tuple						=   new_word
        if len(snake_case_     ) == 1:
          break
        else:
          _snake_case     :						Tuple						=   get_pairs(snake_case_     )
      _snake_case     :						Dict						=   "@@ ".join(snake_case_     )
      _snake_case     :						int						=   word[:-4]
      _snake_case     :						str						=   word
      return word
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   []

      _snake_case     :						List[str]						=   re.findall(r"\S+\n?"						,    snake_case_     )

      for token in words:
        split_tokens.extend(list(self.bpe(snake_case_     ).split(" "     )     )     )
      return split_tokens
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      return self.encoder.get(snake_case_						,    self.encoder.get(self.unk_token     )     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      return self.decoder.get(snake_case_						,    self.unk_token     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   " ".join(snake_case_     ).replace("@@ "						,    ""     ).strip()
      return out_string


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      if not os.path.isdir(snake_case_     ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'     )
        return
      _snake_case     :						Any						=   os.path.join(
          snake_case_						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
      _snake_case     :						List[Any]						=   os.path.join(
          snake_case_						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

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

      _snake_case     :						Any						=   0
      with open(snake_case_						,    "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 snake_case_     : 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!"     )
            _snake_case     :						str						=   token_index
          writer.write(" ".join(snake_case_     ) + "\n"     )
          index += 1

      return vocab_file, merge_file

    # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
    #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
    #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
    #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
    #     return ''.join(tokens_generated_so_far)



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class      _UpperCAmelCase				(					_snake_case		,					unittest.TestCase):
    __lowercase							: Any  =					TextToVideoSDPipeline
    __lowercase							: str  =					TEXT_TO_IMAGE_PARAMS
    __lowercase							: int  =					TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    __lowercase							: Optional[int]  =					frozenset(
        [
            """num_inference_steps""",
            """generator""",
            """latents""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ])
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						str						=   UNetaDConditionModel(
          block_out_channels=(32, 64, 64, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")						,    up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")						,    cross_attention_dim=32						,    attention_head_dim=4						,    )
      _snake_case     :						List[Any]						=   DDIMScheduler(
          beta_start=0.00085						,    beta_end=0.012						,    beta_schedule="scaled_linear"						,    clip_sample=snake_case_						,    set_alpha_to_one=snake_case_						,    )
      torch.manual_seed(0     )
      _snake_case     :						Union[str, Any]						=   AutoencoderKL(
          block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,    latent_channels=4						,    sample_size=1_28						,    )
      torch.manual_seed(0     )
      _snake_case     :						Optional[Any]						=   CLIPTextConfig(
          bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=10_00						,    hidden_act="gelu"						,    projection_dim=5_12						,    )
      _snake_case     :						Tuple						=   CLIPTextModel(snake_case_     )
      _snake_case     :						Optional[int]						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

      _snake_case     :						Any						=   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
      }
      return components
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=0     ):
      if str(snake_case_     ).startswith("mps"     ):
        _snake_case     :						str						=   torch.manual_seed(snake_case_     )
      else:
        _snake_case     :						Union[str, Any]						=   torch.Generator(device=snake_case_     ).manual_seed(snake_case_     )
      _snake_case     :						str						=   {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "pt",
      }
      return inputs
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
      _snake_case     :						Optional[Any]						=   self.get_dummy_components()
      _snake_case     :						Tuple						=   TextToVideoSDPipeline(**snake_case_     )
      _snake_case     :						List[str]						=   sd_pipe.to(snake_case_     )
      sd_pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						int						=   self.get_dummy_inputs(snake_case_     )
      _snake_case     :						Union[str, Any]						=   "np"
      _snake_case     :						Dict						=   sd_pipe(**snake_case_     ).frames
      _snake_case     :						Any						=   frames[0][-3:, -3:, -1]

      assert frames[0].shape == (64, 64, 3)
      _snake_case     :						Dict						=   np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
    def 					lowerCamelCase__	(    self     ):
      self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_						,    expected_max_diff=3E-3     )
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()						,    reason="XFormers attention is only available with CUDA and `xformers` installed"						,    )
    def 					lowerCamelCase__	(    self     ):
      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_						,    expected_max_diff=1E-2     )
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass


    def 					lowerCamelCase__	(    self     ):
      return super().test_progress_bar()






@slow
@skip_mps
class      _UpperCAmelCase				(					unittest.TestCase):
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"     )

      _snake_case     :						int						=   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
      _snake_case     :						str						=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
      _snake_case     :						Tuple						=   pipe.to("cuda"     )

      _snake_case     :						List[Any]						=   "Spiderman is surfing"
      _snake_case     :						Optional[int]						=   torch.Generator(device="cpu"     ).manual_seed(0     )

      _snake_case     :						int						=   pipe(snake_case_						,    generator=snake_case_						,    num_inference_steps=25						,    output_type="pt"     ).frames
      _snake_case     :						int						=   video_frames.cpu().numpy()

      assert np.abs(expected_video - video     ).mean() < 5E-2


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"     )

      _snake_case     :						str						=   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
      _snake_case     :						int						=   pipe.to("cuda"     )

      _snake_case     :						Any						=   "Spiderman is surfing"
      _snake_case     :						str						=   torch.Generator(device="cpu"     ).manual_seed(0     )

      _snake_case     :						Any						=   pipe(snake_case_						,    generator=snake_case_						,    num_inference_steps=2						,    output_type="pt"     ).frames
      _snake_case     :						Optional[int]						=   video_frames.cpu().numpy()

      assert np.abs(expected_video - video     ).mean() < 5E-2

