"""simple docstring"""



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      _A	(						__UpperCAmelCase							, __UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					StableDiffusionDiffEditPipeline
    UpperCamelCase_	:					Optional[Any]							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
    UpperCamelCase_	:					List[str]							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
    UpperCamelCase_	:					Any							=					frozenset(
        []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCamelCase_	:					Optional[int]							=					frozenset([]					)
    def  lowercase					(		self							:     List[str]    )    ->       Tuple:
         torch.manual_seed(0    )
         __snake_case				=							UNetaDConditionModel(
             block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=4	,      out_channels=4	,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,      cross_attention_dim=32	,      attention_head_dim=(2, 4)	,      use_linear_projection=lowerCAmelCase_	,      )
         __snake_case				=							DDIMScheduler(
             beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule='''scaled_linear'''	,      clip_sample=lowerCAmelCase_	,      set_alpha_to_one=lowerCAmelCase_	,      )
         __snake_case				=							DDIMInverseScheduler(
             beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule='''scaled_linear'''	,      clip_sample=lowerCAmelCase_	,      set_alpha_to_zero=lowerCAmelCase_	,      )
         torch.manual_seed(0    )
         __snake_case				=							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=128	,      )
         torch.manual_seed(0    )
         __snake_case				=							CLIPTextConfig(
             bos_token_id=0	,      eos_token_id=2	,      hidden_size=32	,      intermediate_size=37	,      layer_norm_eps=1E-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=1_000	,      hidden_act='''gelu'''	,      projection_dim=512	,      )
         __snake_case				=							CLIPTextModel(lowerCAmelCase_    )
         __snake_case				=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         __snake_case				=							{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''inverse_scheduler''': inverse_scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components
    def  lowercase					(		self							:     int	,      A_							:     List[Any]	,      A_							:     Dict=0    )    ->       Dict:
         __snake_case				=							floats_tensor((1, 16, 16)	,      rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
         __snake_case				=							floats_tensor((1, 2, 4, 16, 16)	,      rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
         if str(lowerCAmelCase_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(lowerCAmelCase_    )
         else:
              __snake_case				=							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
         __snake_case				=							{
             '''prompt''': '''a dog and a newt''',
             '''mask_image''': mask,
             '''image_latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs
    def  lowercase					(		self							:     List[str]	,      A_							:     List[Any]	,      A_							:     str=0    )    ->       Dict:
         __snake_case				=							floats_tensor((1, 3, 32, 32)	,      rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
         __snake_case				=							image.cpu().permute(0	,      2	,      3	,      1    )[0]
         __snake_case				=							Image.fromarray(np.uinta(lowerCAmelCase_    )    ).convert('''RGB'''    )
         if str(lowerCAmelCase_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(lowerCAmelCase_    )
         else:
              __snake_case				=							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
         __snake_case				=							{
             '''image''': image,
             '''source_prompt''': '''a cat and a frog''',
             '''target_prompt''': '''a dog and a newt''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''num_maps_per_mask''': 2,
             '''mask_encode_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs
    def  lowercase					(		self							:     int	,      A_							:     Tuple	,      A_							:     Optional[Any]=0    )    ->       Optional[int]:
         __snake_case				=							floats_tensor((1, 3, 32, 32)	,      rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
         __snake_case				=							image.cpu().permute(0	,      2	,      3	,      1    )[0]
         __snake_case				=							Image.fromarray(np.uinta(lowerCAmelCase_    )    ).convert('''RGB'''    )
         if str(lowerCAmelCase_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(lowerCAmelCase_    )
         else:
              __snake_case				=							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
         __snake_case				=							{
             '''image''': image,
             '''prompt''': '''a cat and a frog''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''decode_latents''': True,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     Dict    )    ->       str:







         if not hasattr(self.pipeline_class	,      '''_optional_components'''    ):
              return

         __snake_case				=							self.get_dummy_components()
         __snake_case				=							self.pipeline_class(**lowerCAmelCase_    )
         pipe.to(lowerCAmelCase_    )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
              setattr(lowerCAmelCase_	,      lowerCAmelCase_	,      lowerCAmelCase_    )
         pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

         __snake_case				=							self.get_dummy_inputs(lowerCAmelCase_    )
         __snake_case				=							pipe(**lowerCAmelCase_    )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(lowerCAmelCase_    )
              __snake_case				=							self.pipeline_class.from_pretrained(lowerCAmelCase_    )
              pipe_loaded.to(lowerCAmelCase_    )
              pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_    )

         for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(lowerCAmelCase_	,      lowerCAmelCase_    ) is None	,      f"`{optional_component}` did not stay set to None after loading."	,      )

         __snake_case				=							self.get_dummy_inputs(lowerCAmelCase_    )
         __snake_case				=							pipe_loaded(**lowerCAmelCase_    )[0]

         __snake_case				=							np.abs(output - output_loaded    ).max()
         self.assertLess(lowerCAmelCase_	,      1E-4    )
    def  lowercase					(		self							:     List[str]    )    ->       List[str]:
         __snake_case				=							'''cpu'''

         __snake_case				=							self.get_dummy_components()
         __snake_case				=							self.pipeline_class(**lowerCAmelCase_    )
         pipe.to(lowerCAmelCase_    )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         __snake_case				=							self.get_dummy_mask_inputs(lowerCAmelCase_    )
         __snake_case				=							pipe.generate_mask(**lowerCAmelCase_    )
         __snake_case				=							mask[0, -3:, -3:]

         self.assertEqual(mask.shape	,      (1, 16, 16)    )
         __snake_case				=							np.array([0] * 9    )
         __snake_case				=							np.abs(mask_slice.flatten() - expected_slice    ).max()
         self.assertLessEqual(lowerCAmelCase_	,      1E-3    )
         self.assertEqual(mask[0, -3, -4]	,      0    )
    def  lowercase					(		self							:     Any    )    ->       List[Any]:
         __snake_case				=							'''cpu'''

         __snake_case				=							self.get_dummy_components()
         __snake_case				=							self.pipeline_class(**lowerCAmelCase_    )
         pipe.to(lowerCAmelCase_    )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         __snake_case				=							self.get_dummy_inversion_inputs(lowerCAmelCase_    )
         __snake_case				=							pipe.invert(**lowerCAmelCase_    ).images
         __snake_case				=							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape	,      (2, 32, 32, 3)    )
         __snake_case				=							np.array(
             [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]	,      )
         __snake_case				=							np.abs(image_slice.flatten() - expected_slice    ).max()
         self.assertLessEqual(lowerCAmelCase_	,      1E-3    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         super().test_inference_batch_single_identical(expected_max_diff=5E-3    )







    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         __snake_case				=							'''cpu'''

         __snake_case				=							self.get_dummy_components()

         __snake_case				=							{'''beta_start''': 0.0_00_85, '''beta_end''': 0.0_12, '''beta_schedule''': '''scaled_linear'''}
         __snake_case				=							DPMSolverMultistepScheduler(**lowerCAmelCase_    )
         __snake_case				=							DPMSolverMultistepInverseScheduler(**lowerCAmelCase_    )

         __snake_case				=							self.pipeline_class(**lowerCAmelCase_    )
         pipe.to(lowerCAmelCase_    )
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         __snake_case				=							self.get_dummy_inversion_inputs(lowerCAmelCase_    )
         __snake_case				=							pipe.invert(**lowerCAmelCase_    ).images
         __snake_case				=							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape	,      (2, 32, 32, 3)    )
         __snake_case				=							np.array(
             [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]	,      )
         __snake_case				=							np.abs(image_slice.flatten() - expected_slice    ).max()
         self.assertLessEqual(lowerCAmelCase_	,      1E-3    )






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




    """simple docstring"""




    def  lowercase					(		self							:     Any    )    ->       Dict:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    @classmethod
    def  lowercase					(		cls							:     int    )    ->       int:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

         __snake_case				=							raw_image.convert('''RGB'''    ).resize((768, 768)    )

         __snake_case				=							raw_image
    def  lowercase					(		self							:     int    )    ->       List[Any]:
         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''	,      safety_checker=lowerCAmelCase_	,      torch_dtype=torch.floataa    )
         __snake_case				=							DDIMScheduler.from_config(pipe.scheduler.config    )
         __snake_case				=							DDIMInverseScheduler.from_config(pipe.scheduler.config    )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         __snake_case				=							'''a bowl of fruit'''
         __snake_case				=							'''a bowl of pears'''

         __snake_case				=							pipe.generate_mask(
             image=self.raw_image	,      source_prompt=lowerCAmelCase_	,      target_prompt=lowerCAmelCase_	,      generator=lowerCAmelCase_	,      )

         __snake_case				=							pipe.invert(
             prompt=lowerCAmelCase_	,      image=self.raw_image	,      inpaint_strength=0.7	,      generator=lowerCAmelCase_    ).latents

         __snake_case				=							pipe(
             prompt=lowerCAmelCase_	,      mask_image=lowerCAmelCase_	,      image_latents=lowerCAmelCase_	,      generator=lowerCAmelCase_	,      negative_prompt=lowerCAmelCase_	,      inpaint_strength=0.7	,      output_type='''numpy'''	,      ).images[0]

         __snake_case				=							(
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
             / 255
         )
         assert np.abs((expected_image - image).max()    ) < 5E-1







    def  lowercase					(		self							:     Any    )    ->       Any:
         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''	,      safety_checker=lowerCAmelCase_	,      torch_dtype=torch.floataa    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         __snake_case				=							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

         __snake_case				=							'''a bowl of fruit'''
         __snake_case				=							'''a bowl of pears'''

         __snake_case				=							pipe.generate_mask(
             image=self.raw_image	,      source_prompt=lowerCAmelCase_	,      target_prompt=lowerCAmelCase_	,      generator=lowerCAmelCase_	,      )

         __snake_case				=							pipe.invert(
             prompt=lowerCAmelCase_	,      image=self.raw_image	,      inpaint_strength=0.7	,      generator=lowerCAmelCase_	,      num_inference_steps=25	,      ).latents

         __snake_case				=							pipe(
             prompt=lowerCAmelCase_	,      mask_image=lowerCAmelCase_	,      image_latents=lowerCAmelCase_	,      generator=lowerCAmelCase_	,      negative_prompt=lowerCAmelCase_	,      inpaint_strength=0.7	,      num_inference_steps=25	,      output_type='''numpy'''	,      ).images[0]

         __snake_case				=							(
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
             / 255
         )
         assert np.abs((expected_image - image).max()    ) < 5E-1
"""simple docstring"""



import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


__lowercase		:		List[str]														=							logging.get_logger(__name__)

__lowercase		:		Union[str, Any]														=							{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__lowercase		:		Optional[int]														=							{
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

__lowercase		:		Dict														=							{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							set()
     __snake_case				=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char))
          __snake_case				=							char

     __snake_case				=							set(snake_case)
     return pairs




class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					Any							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Optional[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    def __init__(		self							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     str="<s>"	,      A_							:     List[str]="</s>"	,      A_							:     List[Any]="</s>"	,      A_							:     List[str]="<s>"	,      A_							:     Optional[int]="<unk>"	,      A_							:     List[str]="<pad>"	,      A_							:     Any="<mask>"	,      **A_							:     int	,      )    ->       Tuple:
         super().__init__(
             bos_token=A_	,      eos_token=A_	,      unk_token=A_	,      sep_token=A_	,      cls_token=A_	,      pad_token=A_	,      mask_token=A_	,      **A_	,      )

         __snake_case				=							vocab_file
         __snake_case				=							merges_file

         __snake_case				=							{}
         __snake_case				=							0
         __snake_case				=							1
         __snake_case				=							2
         __snake_case				=							3

         self.add_from_file(A_    )

         __snake_case				=							{v: k for k, v in self.encoder.items()}

         with open(A_	,      encoding='''utf-8'''    ) as merges_handle:
              __snake_case				=							merges_handle.read().split('''\n'''    )[:-1]
         __snake_case				=							[tuple(merge.split()[:-1]    ) for merge in merges]
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							{}
    def  lowercase					(		self							:     str	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_	,      token_ids_a=A_	,      already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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]
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         return len(self.encoder    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         return dict(self.encoder	,      **self.added_tokens_encoder    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]    )    ->       Optional[int]:
         if token in self.cache:
              return self.cache[token]
         __snake_case				=							tuple(A_    )
         __snake_case				=							tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
         __snake_case				=							get_pairs(A_    )

         if not pairs:
              return token

         while True:
              __snake_case				=							min(A_	,      key=lambda A_    : self.bpe_ranks.get(A_	,      float('''inf'''    )    )    )
              if bigram not in self.bpe_ranks:
                   break
              __snake_case		,	__snake_case				=							bigram
              __snake_case				=							[]
              __snake_case				=							0
              while i < len(A_    ):
                   try:
                        __snake_case				=							word.index(A_	,      A_    )
                   except ValueError:
                        new_word.extend(word[i:]    )
                        break
                   else:
                        new_word.extend(word[i:j]    )
                        __snake_case				=							j

                   if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                        new_word.append(first + second    )
                        i += 2
                   else:
                        new_word.append(word[i]    )
                        i += 1
              __snake_case				=							tuple(A_    )
              __snake_case				=							new_word
              if len(A_    ) == 1:
                   break
              else:
                   __snake_case				=							get_pairs(A_    )
         __snake_case				=							'''@@ '''.join(A_    )
         __snake_case				=							word[:-4]
         __snake_case				=							word
         return word
    def  lowercase					(		self							:     int	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							[]

         __snake_case				=							re.findall(R'''\S+\n?'''	,      A_    )

         for token in words:
              split_tokens.extend(list(self.bpe(A_    ).split(''' '''    )    )    )
         return split_tokens
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Tuple    )    ->       int:
         return self.encoder.get(A_	,      self.encoder.get(self.unk_token    )    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       Optional[Any]:
         return self.decoder.get(A_	,      self.unk_token    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     int    )    ->       Tuple:
         __snake_case				=							''' '''.join(A_    ).replace('''@@ '''	,      ''''''    ).strip()
         return out_string
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

         if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ):
              copyfile(self.vocab_file	,      A_    )

         if os.path.abspath(self.merges_file    ) != os.path.abspath(A_    ):
              copyfile(self.merges_file	,      A_    )

         return out_vocab_file, out_merge_file







    def  lowercase					(		self							:     Optional[int]	,      A_							:     Union[str, Any]    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              try:
                   with open(A_	,      '''r'''	,      encoding='''utf-8'''    ) as fd:
                        self.add_from_file(A_    )
              except FileNotFoundError as fnfe:
                   raise fnfe
              except UnicodeError:
                   raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"    )
              return

         __snake_case				=							f.readlines()
         for lineTmp in lines:
              __snake_case				=							lineTmp.strip()
              __snake_case				=							line.rfind(''' '''    )
              if idx == -1:
                   raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''    )
              __snake_case				=							line[:idx]
              __snake_case				=							len(self.encoder    )