


'''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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{"vocab_file": "vocab.txt"}

lowercase__				     =		{
    "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",
    },
}

lowercase__				     =		{
    "facebook/esm2_t6_8M_UR50D": 1_024,
    "facebook/esm2_t12_35M_UR50D": 1_024,
}






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]				)      ->     str:







     '''simple docstring'''






     with open(__lowerCamelCase      ,  "r"				) as f:
          _a						=		f.read().splitlines()
          return [l.strip() for l in lines]






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				['''input_ids''', '''attention_mask''']






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase="<unk>"     ,							__UpperCamelCase="<cls>"     ,							__UpperCamelCase="<pad>"     ,							__UpperCamelCase="<mask>"     ,							__UpperCamelCase="<eos>"     ,							**__UpperCamelCase     ,							)       ->    Tuple:
          super().__init__(**__UpperCamelCase		)
          _a						=		load_vocab_file(__UpperCamelCase		)
          _a						=		dict(enumerate(self.all_tokens		)		)
          _a						=		{tok: ind for ind, tok in enumerate(self.all_tokens		)}
          _a						=		unk_token
          _a						=		cls_token
          _a						=		pad_token
          _a						=		mask_token
          _a						=		eos_token
          _a						=		self.all_tokens
          self._create_trie(self.unique_no_split_tokens		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          return self._id_to_token.get(__UpperCamelCase     ,							self.unk_token		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          return self._token_to_id.get(__UpperCamelCase     ,							self._token_to_id.get(self.unk_token		)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Any:
          return text.split()






     def  a_					(			self     ,							__UpperCamelCase=False		)       ->    Dict:
          return len(self._id_to_token		)






     def  a_					(			self		)       ->    List[str]:
          return {token: i for i, token in enumerate(self.all_tokens		)}






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          return self._token_to_id.get(__UpperCamelCase     ,							self._token_to_id.get(self.unk_token		)		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          return self._id_to_token.get(__UpperCamelCase     ,							self.unk_token		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    List[int]:
          _a						=		[self.cls_token_id]
          _a						=		[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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False		)       ->    List[int]:
          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]
          _a						=		[1] + ([0] * len(__UpperCamelCase		)) + [1]
          if token_ids_a is not None:
               mask += [0] * len(__UpperCamelCase		) + [1]
          return mask






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		os.path.join(__UpperCamelCase     ,							(filename_prefix + "-" if filename_prefix else "") + "vocab.txt"		)
          with open(__UpperCamelCase     ,							"w"		) as f:
               f.write("\n".join(self.all_tokens		)		)
          return (vocab_file,)






     @property
     def  a_					(			self		)       ->    int:
          return self.get_vocab_size(with_added_tokens=__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = False		)       ->    int:
          return super()._add_tokens(__UpperCamelCase     ,							special_tokens=__UpperCamelCase		)





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







@nightly
@require_torch_gpu
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

