
"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()

class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   @property
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				1
          lowercase       :							int									=				3
          lowercase       :							Dict									=				(32, 32)

          lowercase       :							Tuple									=				floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0							)							).to(_a							)
          return image
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							List[Any]									=				UNetaDConditionModel(
              block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=_a      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
          return model
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							Dict									=				AutoencoderKL(
              block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       )
          return model
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							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=1_000      ,       hidden_act="gelu"      ,       projection_dim=512      ,       )
          return CLIPTextModel(_a							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Union[str, Any]									=				self.dummy_cond_unet_upscale
          lowercase       :							Tuple									=				DDPMScheduler()
          lowercase       :							Optional[Any]									=				DDIMScheduler(prediction_type="v_prediction"							)
          lowercase       :							Tuple									=				self.dummy_vae
          lowercase       :							Union[str, Any]									=				self.dummy_text_encoder
          lowercase       :							Optional[Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

          lowercase       :							str									=				self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
          lowercase       :							Tuple									=				Image.fromarray(np.uinta(_a							)							).convert("RGB"							).resize((64, 64)							)

          # make sure here that pndm scheduler skips prk
          lowercase       :							Optional[Any]									=				StableDiffusionUpscalePipeline(
              unet=_a      ,       low_res_scheduler=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       max_noise_level=350      ,       )
          lowercase       :							str									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Optional[int]									=				"A painting of a squirrel eating a burger"
          lowercase       :							Any									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							Union[str, Any]									=				sd_pipe(
              [prompt]      ,       image=_a      ,       generator=_a      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Dict									=				output.images

          lowercase       :							Tuple									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							str									=				sd_pipe(
              [prompt]      ,       image=_a      ,       generator=_a      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type="np"      ,       return_dict=_a      ,       )[0]

          lowercase       :							Union[str, Any]									=				image[0, -3:, -3:, -1]
          lowercase       :							List[str]									=				image_from_tuple[0, -3:, -3:, -1]

          lowercase       :							Union[str, Any]									=				low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          lowercase       :							Tuple									=				np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							List[Any]									=				self.dummy_cond_unet_upscale
          lowercase       :							Union[str, Any]									=				DDPMScheduler()
          lowercase       :							Dict									=				DDIMScheduler(prediction_type="v_prediction"							)
          lowercase       :							Union[str, Any]									=				self.dummy_vae
          lowercase       :							int									=				self.dummy_text_encoder
          lowercase       :							str									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

          lowercase       :							Tuple									=				self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
          lowercase       :							Optional[int]									=				Image.fromarray(np.uinta(_a							)							).convert("RGB"							).resize((64, 64)							)

          # make sure here that pndm scheduler skips prk
          lowercase       :							List[Any]									=				StableDiffusionUpscalePipeline(
              unet=_a      ,       low_res_scheduler=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       max_noise_level=350      ,       )
          lowercase       :							Optional[int]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							List[Any]									=				"A painting of a squirrel eating a burger"
          lowercase       :							Optional[Any]									=				sd_pipe(
              2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type="np"      ,       )
          lowercase       :							Any									=				output.images
          assert image.shape[0] == 2

          lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(0							)
          lowercase       :							List[Any]									=				sd_pipe(
              [prompt]      ,       image=_a      ,       generator=_a      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type="np"      ,       )
          lowercase       :							Optional[int]									=				output.images
          assert image.shape[0] == 2

   @unittest.skipIf(torch_device != "cuda"      ,       "This test requires a GPU"							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.dummy_cond_unet_upscale
          lowercase       :							Tuple									=				DDPMScheduler()
          lowercase       :							Dict									=				DDIMScheduler(prediction_type="v_prediction"							)
          lowercase       :							List[Any]									=				self.dummy_vae
          lowercase       :							Any									=				self.dummy_text_encoder
          lowercase       :							List[Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

          lowercase       :							Dict									=				self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
          lowercase       :							Optional[Any]									=				Image.fromarray(np.uinta(_a							)							).convert("RGB"							).resize((64, 64)							)

          # put models in fp16, except vae as it overflows in fp16
          lowercase       :							Dict									=				unet.half()
          lowercase       :							Tuple									=				text_encoder.half()

          # make sure here that pndm scheduler skips prk
          lowercase       :							Optional[int]									=				StableDiffusionUpscalePipeline(
              unet=_a      ,       low_res_scheduler=_a      ,       scheduler=_a      ,       vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       max_noise_level=350      ,       )
          lowercase       :							Optional[Any]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Union[str, Any]									=				"A painting of a squirrel eating a burger"
          lowercase       :							Dict									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				sd_pipe(
              [prompt]      ,       image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       ).images

          lowercase       :							Optional[int]									=				low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-upscale/low_res_cat.png"							)
          lowercase       :							Optional[int]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
              "/upsampled_cat.npy"							)

          lowercase       :							str									=				"stabilityai/stable-diffusion-x4-upscaler"
          lowercase       :							Dict									=				StableDiffusionUpscalePipeline.from_pretrained(_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)
          pipe.enable_attention_slicing()

          lowercase       :							Dict									=				"a cat sitting on a park bench"

          lowercase       :							str									=				torch.manual_seed(0							)
          lowercase       :							str									=				pipe(
              prompt=_a      ,       image=_a      ,       generator=_a      ,       output_type="np"      ,       )
          lowercase       :							List[str]									=				output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image							).max() < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-upscale/low_res_cat.png"							)
          lowercase       :							Optional[Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
              "/upsampled_cat_fp16.npy"							)

          lowercase       :							Any									=				"stabilityai/stable-diffusion-x4-upscaler"
          lowercase       :							str									=				StableDiffusionUpscalePipeline.from_pretrained(
              _a      ,       torch_dtype=torch.floataa      ,       )
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)
          pipe.enable_attention_slicing()

          lowercase       :							Union[str, Any]									=				"a cat sitting on a park bench"

          lowercase       :							Optional[int]									=				torch.manual_seed(0							)
          lowercase       :							Optional[int]									=				pipe(
              prompt=_a      ,       image=_a      ,       generator=_a      ,       output_type="np"      ,       )
          lowercase       :							List[str]									=				output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image							).max() < 5E-1

   def      __magic_name__     (     self							):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          lowercase       :							str									=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-upscale/low_res_cat.png"							)

          lowercase       :							Union[str, Any]									=				"stabilityai/stable-diffusion-x4-upscaler"
          lowercase       :							int									=				StableDiffusionUpscalePipeline.from_pretrained(
              _a      ,       torch_dtype=torch.floataa      ,       )
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)
          pipe.enable_attention_slicing(1							)
          pipe.enable_sequential_cpu_offload()

          lowercase       :							List[str]									=				"a cat sitting on a park bench"

          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(
              prompt=_a      ,       image=_a      ,       generator=_a      ,       num_inference_steps=5      ,       output_type="np"      ,       )

          lowercase       :							str									=				torch.cuda.max_memory_allocated()
          # make sure that less than 2.9 GB is allocated
          assert mem_bytes < 2.9 * 10**9




"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


