


"""simple docstring"""







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowercase    					=      logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
__lowercase    					=      {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowercase    					=      {
    """allenai/led-base-16384""": 16384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      lowercase			(				)->      Dict:
  '''simple docstring'''







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


def      lowercase			(				A_       )->      Optional[Any]:
  '''simple docstring'''







  a						:		Any      							=			set()
  a						:		Tuple      							=			word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    a						:		int      							=			char
  return pairs







class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       VOCAB_FILES_NAMES
    UpperCAmelCase       :							List[Any]	=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase       :							Optional[Any]	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase       :							List[Any]	=       ["""input_ids""", """attention_mask"""]






    def __init__(					self				:    Tuple				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any]="replace"				, __UpperCAmelCase				:    Any="<s>"				, __UpperCAmelCase				:    Any="</s>"				, __UpperCAmelCase				:    Tuple="</s>"				, __UpperCAmelCase				:    Dict="<s>"				, __UpperCAmelCase				:    List[str]="<unk>"				, __UpperCAmelCase				:    str="<pad>"				, __UpperCAmelCase				:    Union[str, Any]="<mask>"				, __UpperCAmelCase				:    Any=False				, **__UpperCAmelCase				:    Any				, ):
      a						:		Optional[Any]      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else bos_token
      a						:		Optional[Any]      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else eos_token
      a						:		Dict      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else sep_token
      a						:		Dict      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else cls_token
      a						:		Union[str, Any]      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else unk_token
      a						:		int      							=			AddedToken(__UpperCAmelCase				, lstrip=__UpperCAmelCase				, rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase				, __UpperCAmelCase) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      a						:		List[Any]      							=			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.bart.tokenization_bart.BartTokenizer.vocab_size
    def 	__snake_case							(					self				:    Optional[int]):
      return len(self.encoder)






    def 	__snake_case							(					self				:    Any):
      return dict(self.encoder				, **self.added_tokens_encoder)






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int):
      if token in self.cache:
        return self.cache[token]
      a						:		List[Any]      							=			tuple(__UpperCAmelCase)
      a						:		Any      							=			get_pairs(__UpperCAmelCase)

      if not pairs:
        return token

      while True:
        a						:		Union[str, Any]      							=			min(__UpperCAmelCase				, key=lambda __UpperCAmelCase: self.bpe_ranks.get(__UpperCAmelCase				, float("inf")))
        if bigram not in self.bpe_ranks:
          break
        a							,		a						:		str      							=			bigram
        a						:		Tuple      							=			[]
        a						:		Optional[int]      							=			0
        while i < len(__UpperCAmelCase):
          try:
            a						:		str      							=			word.index(__UpperCAmelCase				, __UpperCAmelCase)
          except ValueError:
            new_word.extend(word[i:])
            break
          else:
            new_word.extend(word[i:j])
            a						:		Tuple      							=			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
        a						:		str      							=			tuple(__UpperCAmelCase)
        a						:		Dict      							=			new_word
        if len(__UpperCAmelCase) == 1:
          break
        else:
          a						:		str      							=			get_pairs(__UpperCAmelCase)
      a						:		Any      							=			" ".join(__UpperCAmelCase)
      a						:		Dict      							=			word
      return word






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    str):
      a						:		int      							=			[]
      for token in re.findall(self.pat				, __UpperCAmelCase):
        a						:		List[str]      							=			"".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(__UpperCAmelCase).split(" "))
      return bpe_tokens






    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    str):
      return self.encoder.get(__UpperCAmelCase				, self.encoder.get(self.unk_token))






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]):
      return self.decoder.get(__UpperCAmelCase)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    List[str]):
      a						:		Union[str, Any]      							=			"".join(__UpperCAmelCase)
      a						:		List[Any]      							=			bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"				, errors=self.errors)
      return text






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[str] = None):
      if not os.path.isdir(__UpperCAmelCase):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
        return
      a						:		List[Any]      							=			os.path.join(
          __UpperCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
      a						:		int      							=			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")

      a						:		Tuple      							=			0
      with open(__UpperCAmelCase				, "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 __UpperCAmelCase: 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						:		List[Any]      							=			token_index
          writer.write(" ".join(__UpperCAmelCase) + "\n")
          index += 1

      return vocab_file, merge_file






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    List[int]				, __UpperCAmelCase				:    Optional[List[int]] = None):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a						:		Optional[int]      							=			[self.cls_token_id]
      a						:		Tuple      							=			[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    List[int]				, __UpperCAmelCase				:    Optional[List[int]] = None				, __UpperCAmelCase				:    bool = False):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__UpperCAmelCase				, token_ids_a=__UpperCAmelCase				, already_has_special_tokens=__UpperCAmelCase)

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






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    List[int]				, __UpperCAmelCase				:    Optional[List[int]] = None):
      a						:		Optional[Any]      							=			[self.sep_token_id]
      a						:		str      							=			[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 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    List[Any]=False				, **__UpperCAmelCase				:    Union[str, Any]):
      a						:		int      							=			kwargs.pop("add_prefix_space"				, self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase) > 0 and not text[0].isspace()):
        a						:		List[Any]      							=			" " + text
      return (text, kwargs)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Union[Dict[str, EncodedInput], BatchEncoding]				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD				, __UpperCAmelCase				:    Optional[int] = None				, __UpperCAmelCase				:    Optional[bool] = None				, ):
      a						:		Union[str, Any]      							=			super()._pad(
          encoded_inputs=__UpperCAmelCase				, max_length=__UpperCAmelCase				, padding_strategy=__UpperCAmelCase				, pad_to_multiple_of=__UpperCAmelCase				, return_attention_mask=__UpperCAmelCase				, )

      # Load from model defaults
      if return_attention_mask is None:
        a						:		List[Any]      							=			"attention_mask" in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
        a						:		Tuple      							=			encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        a						:		Tuple      							=			len(encoded_inputs["global_attention_mask"]) != len(__UpperCAmelCase)

        if needs_to_be_padded:
          a						:		Union[str, Any]      							=			len(__UpperCAmelCase) - len(encoded_inputs["global_attention_mask"])

          if self.padding_side == "right":
            # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
            a						:		str      							=			(
                encoded_inputs["global_attention_mask"] + [-1] * difference
            )
          elif self.padding_side == "left":
            a						:		Optional[Any]      							=			[-1] * difference + encoded_inputs[
                "global_attention_mask"
            ]
          else:
            raise ValueError("Invalid padding strategy:" + str(self.padding_side))

      return encoded_inputs










"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       KandinskyVaaControlnetPipeline
    UpperCAmelCase       :							List[str]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Optional[Any]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Dict	=       [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase       :							Optional[int]	=       False






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return self.time_input_dim






    @property
    def 	__snake_case							(					self				:    Any):
      return self.time_input_dim * 4






    @property
    def 	__snake_case							(					self				:    str):
      return 100






    @property
    def 	__snake_case							(					self				:    str):
      torch.manual_seed(0)

      a						:		str      							=			{
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      a						:		Dict      							=			UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def 	__snake_case							(					self				:    str):
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }






    @property
    def 	__snake_case							(					self				:    Union[str, Any]):
      torch.manual_seed(0)
      a						:		Dict      							=			VQModel(**self.dummy_movq_kwargs)
      return model






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Optional[Any]      							=			self.dummy_unet
      a						:		int      							=			self.dummy_movq

      a						:		str      							=			DDIMScheduler(
          num_train_timesteps=1000				, beta_schedule="linear"				, beta_start=0.00_085				, beta_end=0.012				, clip_sample=__UpperCAmelCase				, set_alpha_to_one=__UpperCAmelCase				, steps_offset=1				, prediction_type="epsilon"				, thresholding=__UpperCAmelCase				, )

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    int=0):
      a						:		Optional[int]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)

      # create hint
      a						:		Union[str, Any]      							=			floats_tensor((1, 3, 64, 64)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[Any]      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		str      							=			{
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			"cpu"

      a						:		Tuple      							=			self.get_dummy_components()

      a						:		Dict      							=			self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[int]      							=			pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]      							=			pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Any      							=			output.images

      a						:		Any      							=			pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)				, return_dict=__UpperCAmelCase				, )[0]

      a						:		Union[str, Any]      							=			image[0, -3:, -3:, -1]
      a						:		int      							=			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      a						:		Tuple      							=			np.array(
          [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])

      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
      ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[int]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")

      a						:		Union[str, Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png")
      a						:		List[Any]      							=			torch.from_numpy(np.array(__UpperCAmelCase)).float() / 255.0
      a						:		str      							=			hint.permute(2				, 0				, 1).unsqueeze(0)

      a						:		Optional[int]      							=			KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"				, torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		List[str]      							=			KandinskyVaaControlnetPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"				, torch_dtype=torch.floataa)
      a						:		int      							=			pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Tuple      							=			"A robot, 4k photo"

      a						:		Any      							=			torch.Generator(device="cuda").manual_seed(0)
      a							,		a						:		int      							=			pipe_prior(
          __UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=5				, negative_prompt=""				, ).to_tuple()

      a						:		str      							=			torch.Generator(device="cuda").manual_seed(0)
      a						:		Union[str, Any]      							=			pipeline(
          image_embeds=__UpperCAmelCase				, negative_image_embeds=__UpperCAmelCase				, hint=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=100				, output_type="np"				, )

      a						:		str      							=			output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(__UpperCAmelCase				, __UpperCAmelCase)






