"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{"vocab_file": "sentencepiece.bpe.model"}

lowerCAmelCase_:					Optional[int]     =					{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

lowerCAmelCase_:					str     =					{
    "camembert-base": 5_1_2,
}

lowerCAmelCase_:					Optional[Any]     =					"▁"






class    a__    (   _a	):
   snake_case_							=      VOCAB_FILES_NAMES
   snake_case_							=      PRETRAINED_VOCAB_FILES_MAP
   snake_case_							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_							=      ["input_ids", "attention_mask"]


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase="<s>",				_UpperCAmelCase="</s>",				_UpperCAmelCase="</s>",				_UpperCAmelCase="<s>",				_UpperCAmelCase="<unk>",				_UpperCAmelCase="<pad>",				_UpperCAmelCase="<mask>",				_UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"],				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							AddedToken(_UpperCAmelCase,				lstrip=_UpperCAmelCase,				rstrip=_UpperCAmelCase  ) if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) else mask_token

        lowercase__				 =							{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_UpperCAmelCase,				eos_token=_UpperCAmelCase,				unk_token=_UpperCAmelCase,				sep_token=_UpperCAmelCase,				cls_token=_UpperCAmelCase,				pad_token=_UpperCAmelCase,				mask_token=_UpperCAmelCase,				additional_special_tokens=_UpperCAmelCase,				sp_model_kwargs=self.sp_model_kwargs,				**_UpperCAmelCase,				)
        lowercase__				 =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(str(_UpperCAmelCase  )  )
        lowercase__				 =							vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        lowercase__				 =							{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
        lowercase__				 =							len(self.fairseq_tokens_to_ids  )
        lowercase__				 =							len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
        lowercase__				 =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = False  ):
        '''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 None:
             return [1] + ([0] * len(_UpperCAmelCase  )) + [1]
        return [1] + ([0] * len(_UpperCAmelCase  )) + [1, 1] + ([0] * len(_UpperCAmelCase  )) + [1]


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        lowercase__				 =							[self.sep_token_id]
        lowercase__				 =							[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 	snake_case__	(     self  ):
        '''simple docstring'''



        return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{self.convert_ids_to_tokens(_UpperCAmelCase  ): i for i in range(self.vocab_size  )}
        vocab.update(self.added_tokens_encoder  )
        return vocab


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return self.sp_model.encode(_UpperCAmelCase,				out_type=_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(_UpperCAmelCase  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        lowercase__				 =							""
        lowercase__				 =							False
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special:
                       out_string += " "
                  out_string += self.sp_model.decode(_UpperCAmelCase  ) + token
                  lowercase__				 =							True
                  lowercase__				 =							[]
             else:
                  current_sub_tokens.append(_UpperCAmelCase  )
                  lowercase__				 =							False
        out_string += self.sp_model.decode(_UpperCAmelCase  )
        return out_string.strip()


   def __getstate__(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.__dict__.copy()
        lowercase__				 =							None
        return state


   def __setstate__(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							d

        # for backward compatibility
        if not hasattr(self,				"sp_model_kwargs"  ):
             lowercase__				 =							{}

        lowercase__				 =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(self.vocab_file  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        if not os.path.isdir(_UpperCAmelCase  ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
        lowercase__				 =							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  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file,				_UpperCAmelCase  )
        elif not os.path.isfile(self.vocab_file  ):
             with open(_UpperCAmelCase,				"wb"  ) as fi:
                  lowercase__				 =							self.sp_model.serialized_model_proto()
                  fi.write(_UpperCAmelCase  )

        return (out_vocab_file,)





"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_attention_heads": 2,
            "attention_head_dim": 4,
            "num_layers": 2,
            "embedding_dim": 8,
            "num_embeddings": 7,
            "additional_embeddings": 4,
        }
        lowercase__				 =							self.dummy_input
        return init_dict, inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							PriorTransformer.from_pretrained(
            "hf-internal-testing/prior-dummy",				output_loading_info=_UpperCAmelCase  )
        self.assertIsNotNone(_UpperCAmelCase  )
        self.assertEqual(len(loading_info["missing_keys"]  ),				0  )

        model.to(_UpperCAmelCase  )
        lowercase__				 =							model(**self.dummy_input  )[0]

        assert hidden_states is not None, "Make sure output is not None"


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.prepare_init_args_and_inputs_for_common()

        lowercase__				 =							self.model_class(**_UpperCAmelCase  )
        lowercase__				 =							inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__				 =							[*signature.parameters.keys()]

        lowercase__				 =							["hidden_states", "timestep"]
        self.assertListEqual(arg_names[:2],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
        lowercase__				 =							model.to(_UpperCAmelCase  )

        if hasattr(_UpperCAmelCase,				"set_default_attn_processor"  ):
             model.set_default_attn_processor()

        lowercase__				 =							self.get_dummy_seed_input()

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        lowercase__				 =							output[0, :5].flatten().cpu()
        print(_UpperCAmelCase  )

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        lowercase__				 =							torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							batch_size
        lowercase__				 =							embedding_dim
        lowercase__				 =							num_embeddings

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]  )
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",				subfolder="prior"  )
        model.to(_UpperCAmelCase  )
        lowercase__				 =							self.get_dummy_seed_input(seed=_UpperCAmelCase  )

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        assert list(sample.shape  ) == [1, 768]

        lowercase__				 =							sample[0, :8].flatten().cpu()
        print(_UpperCAmelCase  )
        lowercase__				 =							torch.tensor(_UpperCAmelCase  )

        assert torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				atol=1E-3  )




