





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							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =PriorTransformer
      _snake_case				  ='''hidden_states'''



      @property
      def 						lowerCAmelCase__     (						self:    str							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =4
         UpperCAmelCase_           =8
         UpperCAmelCase_           =7

         UpperCAmelCase_           =floats_tensor((batch_size, embedding_dim)							).to(_lowerCAmelCase							)

         UpperCAmelCase_           =floats_tensor((batch_size, embedding_dim)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(_lowerCAmelCase							)

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



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    Tuple=0							)    ->     Tuple:
         '''simple docstring'''

         torch.manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           =4
         UpperCAmelCase_           =8
         UpperCAmelCase_           =7

         UpperCAmelCase_           =torch.randn((batch_size, embedding_dim)							).to(_lowerCAmelCase							)

         UpperCAmelCase_           =torch.randn((batch_size, embedding_dim)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_lowerCAmelCase							)

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



      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     str:
         '''simple docstring'''

         return (4, 8)



      @property
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[int]:
         '''simple docstring'''

         return (4, 8)



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Tuple:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Optional[int]:
         '''simple docstring'''

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

         model.to(_lowerCAmelCase							)
         UpperCAmelCase_           =model(**self.dummy_input							)[0]

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



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_    ,							UpperCAmelCase_           =self.prepare_init_args_and_inputs_for_common()

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

         UpperCAmelCase_           =["hidden_states", "timestep"]
         self.assertListEqual(arg_names[:2]		,	_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     int:
         '''simple docstring'''

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

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

         UpperCAmelCase_           =self.get_dummy_seed_input()

         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)[0]

         UpperCAmelCase_           =output[0, :5].flatten().cpu()
         print(_lowerCAmelCase							)

         # 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.
         UpperCAmelCase_           =torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]							)
         self.assertTrue(torch_all_close(_lowerCAmelCase		,	_lowerCAmelCase		,	rtol=1e-2							)							)

@slow
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    str=1		,	_lowerCAmelCase:    Any=768		,	_lowerCAmelCase:    Any=77		,	_lowerCAmelCase:    Optional[int]=0							)    ->     str:
         '''simple docstring'''

         torch.manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           =batch_size
         UpperCAmelCase_           =embedding_dim
         UpperCAmelCase_           =num_embeddings

         UpperCAmelCase_           =torch.randn((batch_size, embedding_dim)							).to(_lowerCAmelCase							)

         UpperCAmelCase_           =torch.randn((batch_size, embedding_dim)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_lowerCAmelCase							)

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



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     str:
         '''simple docstring'''

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






      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
              [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
              # fmt: on
          ]							)
      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    List[str]							)    ->     int:
         '''simple docstring'''

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

         with torch.no_grad():
            UpperCAmelCase_           =model(**_lowerCAmelCase							)[0]

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

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

         assert torch_all_close(_lowerCAmelCase		,	_lowerCAmelCase		,	atol=1e-3							)











'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


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

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


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

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              _UpperCAmelCase		,      tokenizer_file=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_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]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					List[str]						=			[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]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''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
          __A		:					List[Any]						=			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,)