




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 				__a (			__lowerCamelCase							,					unittest.TestCase					):


     """simple docstring"""


     _A    :   int     =  UnCLIPPipeline
     _A    :   int     =  TEXT_TO_IMAGE_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
         "guidance_scale",
         "prompt_embeds",
         "cross_attention_kwargs",
     }
     _A    :   Union[str, Any]     =  TEXT_TO_IMAGE_BATCH_PARAMS
     _A    :   Optional[int]     =  [
         "generator",
         "return_dict",
         "prior_num_inference_steps",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]
     _A    :   List[str]     =  False




     @property
     def   __A    (     self      :			List[Any]						)		->       int:


         '''simple docstring'''





         return 3_2




     @property
     def   __A    (     self      :			Tuple						)		->       List[str]:


         '''simple docstring'''





         return 3_2




     @property
     def   __A    (     self      :			Union[str, Any]						)		->       List[Any]:


         '''simple docstring'''





         return self.time_input_dim




     @property
     def   __A    (     self      :			Optional[Any]						)		->       Union[str, Any]:


         '''simple docstring'''





         return self.time_input_dim * 4




     @property
     def   __A    (     self      :			Any						)		->       List[str]:


         '''simple docstring'''





         return 1_0_0




     @property
     def   __A    (     self      :			int						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
         return tokenizer




     @property
     def   __A    (     self      :			Union[str, Any]						)		->       Optional[Any]:


         '''simple docstring'''





         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =CLIPTextConfig(
             bos_token_id=0    ,eos_token_id=2    ,hidden_size=self.text_embedder_hidden_size    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=3_7    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_0_0_0    ,)
         return CLIPTextModelWithProjection(_UpperCamelCase						)




     @property
     def   __A    (     self      :			Optional[int]						)		->       Optional[int]:


         '''simple docstring'''





         torch.manual_seed(0						)

         SCREAMING_SNAKE_CASE__       ={
             """num_attention_heads""": 2,
             """attention_head_dim""": 1_2,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         SCREAMING_SNAKE_CASE__       =PriorTransformer(**_UpperCamelCase						)
         return model




     @property
     def   __A    (     self      :			List[str]						)		->       Optional[int]:


         '''simple docstring'''





         torch.manual_seed(0						)

         SCREAMING_SNAKE_CASE__       ={
             """clip_embeddings_dim""": self.text_embedder_hidden_size,
             """time_embed_dim""": self.time_embed_dim,
             """cross_attention_dim""": self.cross_attention_dim,
         }

         SCREAMING_SNAKE_CASE__       =UnCLIPTextProjModel(**_UpperCamelCase						)
         return model




     @property
     def   __A    (     self      :			List[Any]						)		->       Union[str, Any]:


         '''simple docstring'''





         torch.manual_seed(0						)

         SCREAMING_SNAKE_CASE__       ={
             """sample_size""": 3_2,
             # RGB in channels
             """in_channels""": 3,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 6,
             """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,
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": """identity""",
         }

         SCREAMING_SNAKE_CASE__       =UNetaDConditionModel(**_UpperCamelCase						)
         return model




     @property
     def   __A    (     self      :			Tuple						)		->       List[str]:


         '''simple docstring'''





         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }




     @property
     def   __A    (     self      :			int						)		->       Tuple:


         '''simple docstring'''





         torch.manual_seed(0						)

         SCREAMING_SNAKE_CASE__       =UNetaDModel(**self.dummy_super_res_kwargs						)
         return model




     @property
     def   __A    (     self      :			Union[str, Any]						)		->       int:


         '''simple docstring'''





         torch.manual_seed(1						)

         SCREAMING_SNAKE_CASE__       =UNetaDModel(**self.dummy_super_res_kwargs						)
         return model




     def   __A    (     self      :			Optional[int]						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.dummy_prior
         SCREAMING_SNAKE_CASE__       =self.dummy_decoder
         SCREAMING_SNAKE_CASE__       =self.dummy_text_proj
         SCREAMING_SNAKE_CASE__       =self.dummy_text_encoder
         SCREAMING_SNAKE_CASE__       =self.dummy_tokenizer
         SCREAMING_SNAKE_CASE__       =self.dummy_super_res_first
         SCREAMING_SNAKE_CASE__       =self.dummy_super_res_last

         SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,prediction_type="""sample"""    ,num_train_timesteps=1_0_0_0    ,clip_sample_range=5.0    ,)

         SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
             variance_type="""learned_range"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_0_0_0    ,)

         SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_0_0_0    ,)

         SCREAMING_SNAKE_CASE__       ={
             """prior""": prior,
             """decoder""": decoder,
             """text_proj""": text_proj,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """super_res_first""": super_res_first,
             """super_res_last""": super_res_last,
             """prior_scheduler""": prior_scheduler,
             """decoder_scheduler""": decoder_scheduler,
             """super_res_scheduler""": super_res_scheduler,
         }

         return components




     def   __A    (     self      :			str    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			int=0						)		->       Optional[int]:


         '''simple docstring'''





         if str(_UpperCamelCase						).startswith("""mps"""						):
             SCREAMING_SNAKE_CASE__       =torch.manual_seed(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       ={
             """prompt""": """horse""",
             """generator""": generator,
             """prior_num_inference_steps""": 2,
             """decoder_num_inference_steps""": 2,
             """super_res_num_inference_steps""": 2,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A    (     self      :			List[Any]						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""cpu"""

         SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

         SCREAMING_SNAKE_CASE__       =self.pipeline_class(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)

         pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =pipe(**self.get_dummy_inputs(_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__       =output.images

         SCREAMING_SNAKE_CASE__       =pipe(
             **self.get_dummy_inputs(_UpperCamelCase						)    ,return_dict=_UpperCamelCase    ,)[0]

         SCREAMING_SNAKE_CASE__       =image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE__       =image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         SCREAMING_SNAKE_CASE__       =np.array(
             [
                 0.9997,
                 0.9988,
                 0.0028,
                 0.9997,
                 0.9984,
                 0.9965,
                 0.0029,
                 0.9986,
                 0.0025,
             ]						)

         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   __A    (     self      :			Any						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =torch.device("""cpu"""						)
         class 				__a :


              """simple docstring"""


              _A    :   int     =  1






         SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

         SCREAMING_SNAKE_CASE__       =self.pipeline_class(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =components["""prior"""]
         SCREAMING_SNAKE_CASE__       =components["""decoder"""]
         SCREAMING_SNAKE_CASE__       =components["""super_res_first"""]
         SCREAMING_SNAKE_CASE__       =components["""tokenizer"""]
         SCREAMING_SNAKE_CASE__       =components["""text_encoder"""]

         SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =prior.dtype
         SCREAMING_SNAKE_CASE__       =1

         SCREAMING_SNAKE_CASE__       =(batch_size, prior.config.embedding_dim)
         SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
             _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)
         SCREAMING_SNAKE_CASE__       =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
         SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
             _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)

         SCREAMING_SNAKE_CASE__       =(
             batch_size,
             super_res_first.config.in_channels // 2,
             super_res_first.config.sample_size,
             super_res_first.config.sample_size,
         )
         SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
             _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)

         pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       ="""this is a prompt example"""

         SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =pipe(
             [prompt]    ,generator=_UpperCamelCase    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=_UpperCamelCase    ,decoder_latents=_UpperCamelCase    ,super_res_latents=_UpperCamelCase    ,output_type="""np"""    ,)
         SCREAMING_SNAKE_CASE__       =output.images

         SCREAMING_SNAKE_CASE__       =tokenizer(
             _UpperCamelCase    ,padding="""max_length"""    ,max_length=tokenizer.model_max_length    ,return_tensors="""pt"""    ,)
         SCREAMING_SNAKE_CASE__       =text_encoder(text_inputs.input_ids						)
         SCREAMING_SNAKE_CASE__       =text_inputs.attention_mask

         SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =pipe(
             generator=_UpperCamelCase    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=_UpperCamelCase    ,decoder_latents=_UpperCamelCase    ,super_res_latents=_UpperCamelCase    ,text_model_output=_UpperCamelCase    ,text_attention_mask=_UpperCamelCase    ,output_type="""np"""    ,)[0]

         # make sure passing text embeddings manually is identical
         assert np.abs(image - image_from_text						).max() < 1e-4




     @skip_mps
     def   __A    (     self      :			List[Any]						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =torch_device == """cpu"""

         self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase    ,expected_max_diff=0.01						)




     @skip_mps
     def   __A    (     self      :			Any						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =torch_device == """cpu"""
         SCREAMING_SNAKE_CASE__       =True
         SCREAMING_SNAKE_CASE__       =[
             """prior_num_inference_steps""",
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=_UpperCamelCase    ,relax_max_difference=_UpperCamelCase    ,additional_params_copy_to_batched_inputs=_UpperCamelCase    ,)




     def   __A    (     self      :			Union[str, Any]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[
             """prior_num_inference_steps""",
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         if torch_device == "mps":
             # TODO: MPS errors with larger batch sizes
             SCREAMING_SNAKE_CASE__       =[2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=_UpperCamelCase    ,additional_params_copy_to_batched_inputs=_UpperCamelCase    ,)
         else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=_UpperCamelCase						)




     @skip_mps
     def   __A    (     self      :			Union[str, Any]						)		->       Union[str, Any]:


         '''simple docstring'''





         return super().test_dict_tuple_outputs_equivalent()




     @skip_mps
     def   __A    (     self      :			int						)		->       Any:


         '''simple docstring'''





         return super().test_save_load_local()




     @skip_mps
     def   __A    (     self      :			List[Any]						)		->       int:


         '''simple docstring'''





         return super().test_save_load_optional_components()






@nightly
class 				__a (			unittest.TestCase					):


     """simple docstring"""


     def   __A    (     self      :			Union[str, Any]						)		->       str:


         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   __A    (     self      :			List[Any]						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/unclip/karlo_v1_alpha_horse_cpu.npy"""						)

         SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""						)
         pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =pipeline(
             """horse"""    ,num_images_per_prompt=1    ,generator=_UpperCamelCase    ,output_type="""np"""    ,)

         SCREAMING_SNAKE_CASE__       =output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)
         assert np.abs(expected_image - image						).max() < 1e-1






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


     """simple docstring"""


     def   __A    (     self      :			List[Any]						)		->       Dict:


         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   __A    (     self      :			Optional[Any]						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/unclip/karlo_v1_alpha_horse_fp16.npy"""						)

         SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__       =pipeline.to(_UpperCamelCase						)
         pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =torch.Generator(device="""cpu"""						).manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =pipeline(
             """horse"""    ,generator=_UpperCamelCase    ,output_type="""np"""    ,)

         SCREAMING_SNAKE_CASE__       =output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(_UpperCamelCase    ,_UpperCamelCase						)




     def   __A    (     self      :			str						)		->       Optional[Any]:


         '''simple docstring'''





         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         SCREAMING_SNAKE_CASE__       =pipe(
             """horse"""    ,num_images_per_prompt=1    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,output_type="""np"""    ,)

         SCREAMING_SNAKE_CASE__       =torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9












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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_deberta_va import DebertaVaTokenizer
else:
    lowerCamelCase_  						=       None

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_  						=       {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  VOCAB_FILES_NAMES
     _A    :   Union[str, Any]     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[int]     =  PRETRAINED_INIT_CONFIGURATION
     _A    :   Any     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   Optional[int]     =  DebertaVaTokenizer




     def __init__(     self      :			List[Any]    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			Any=False    ,_UpperCamelCase      :			List[Any]=False    ,_UpperCamelCase      :			Any="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[SEP]"    ,_UpperCamelCase      :			int="[UNK]"    ,_UpperCamelCase      :			int="[SEP]"    ,_UpperCamelCase      :			Tuple="[PAD]"    ,_UpperCamelCase      :			Optional[Any]="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[MASK]"    ,**_UpperCamelCase      :			int    ,)		->       None:


         '''simple docstring'''





         super().__init__(
             _UpperCamelCase    ,tokenizer_file=_UpperCamelCase    ,do_lower_case=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,sep_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,cls_token=_UpperCamelCase    ,mask_token=_UpperCamelCase    ,split_by_punct=_UpperCamelCase    ,**_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =do_lower_case
         SCREAMING_SNAKE_CASE__       =split_by_punct
         SCREAMING_SNAKE_CASE__       =vocab_file
         SCREAMING_SNAKE_CASE__       =False if not self.vocab_file else True




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			str=None						)		->       Dict:


         '''simple docstring'''





         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[self.cls_token_id]
         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       str:


         '''simple docstring'''





         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 not None:
             return [1] + ([0] * len(_UpperCamelCase						)) + [1] + ([0] * len(_UpperCamelCase						)) + [1]
         return [1] + ([0] * len(_UpperCamelCase						)) + [1]




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			str=None						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         SCREAMING_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						) * [0] + len(token_ids_a + sep						) * [1]




     def   __A    (     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""						)

         if not os.path.isdir(_UpperCamelCase						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
             return
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

         return (out_vocab_file,)






