"""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


__SCREAMING_SNAKE_CASE     			=logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE     			={
    "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",
    },
}

__SCREAMING_SNAKE_CASE     			={
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     with open(__SCREAMING_SNAKE_CASE  ,    'r'   ) as f:
          lowercase_			:       List[str]									=			f.read().splitlines()
          return [l.strip() for l in lines]







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						['input_ids', 'attention_mask']
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase="<unk>"  ,__UpperCamelCase="<cls>"  ,__UpperCamelCase="<pad>"  ,__UpperCamelCase="<mask>"  ,__UpperCamelCase="<eos>"  ,**__UpperCamelCase  ,)			->   int:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			load_vocab_file(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			dict(enumerate(self.all_tokens    )    )
        lowercase_			:       Any									=			{tok: ind for ind, tok in enumerate(self.all_tokens    )}
        lowercase_			:       Tuple									=			unk_token
        lowercase_			:       int									=			cls_token
        lowercase_			:       int									=			pad_token
        lowercase_			:       Union[str, Any]									=			mask_token
        lowercase_			:       int									=			eos_token
        lowercase_			:       Any									=			self.all_tokens
        self._create_trie(self.unique_no_split_tokens    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self._id_to_token.get(__UpperCamelCase  ,self.unk_token    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self._token_to_id.get(__UpperCamelCase  ,self._token_to_id.get(self.unk_token    )    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        return text.split()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=False    )			->   Tuple:


        '''simple docstring'''


        return len(self._id_to_token    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        return {token: i for i, token in enumerate(self.all_tokens    )}
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self._token_to_id.get(__UpperCamelCase  ,self._token_to_id.get(self.unk_token    )    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self._id_to_token.get(__UpperCamelCase  ,self.unk_token    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Any									=			[self.cls_token_id]
        lowercase_			:       List[str]									=			[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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   List[int]:


        '''simple docstring'''


        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_			:       Dict									=			[1] + ([0] * len(__UpperCamelCase    )) + [1]
        if token_ids_a is not None:
             mask += [0] * len(__UpperCamelCase    ) + [1]
        return mask
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			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     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self.get_vocab_size(with_added_tokens=__UpperCamelCase    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   int:


        '''simple docstring'''


        return super()._add_tokens(__UpperCamelCase  ,special_tokens=__UpperCamelCase    )


"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						IFImgaImgSuperResolutionPipeline
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}						)
   lowercase							=						PipelineTesterMixin.required_optional_params - {'latents'}
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._get_superresolution_dummy_components()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       Any									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       int									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       List[str]									=			floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'original_image': original_image,
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }

        return inputs
   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()  ,reason='XFormers attention is only available with CUDA and `xformers` installed'  ,)
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != 'cuda'  ,reason='float16 requires CUDA'    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        super().test_save_load_floataa(expected_max_diff=1e-1    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_save_load_local()







   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2  ,)

