




'''simple docstring'''


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class    __lowercase						:







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return self.get_dummy_input()







   @property
   def 		UpperCamelCase__			(	self      )				->    Any:
     if self.block_type == "down":
       return (4, 32, 16, 16)
     elif self.block_type == "mid":
       return (4, 32, 32, 32)
     elif self.block_type == "up":
       return (4, 32, 64, 64)

     raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=True      ,							UpperCamelCase=False      ,							UpperCamelCase=False      ,							UpperCamelCase=False      ,							)				->    Optional[int]:
     __a			=					4
     __a			=					32
     __a			=					(32, 32)

     __a			=					torch.manual_seed(0      )
     __a			=					torch.device(UpperCamelCase      )
     __a			=					(batch_size, num_channels) + sizes
     __a			=					randn_tensor(UpperCamelCase      ,							generator=UpperCamelCase      ,							device=UpperCamelCase      )
     __a			=					{'hidden_states': hidden_states}

     if include_temb:
       __a			=					128
       __a			=					randn_tensor((batch_size, temb_channels)      ,							generator=UpperCamelCase      ,							device=UpperCamelCase      )

     if include_res_hidden_states_tuple:
       __a			=					torch.manual_seed(1      )
       __a			=					(randn_tensor(UpperCamelCase      ,							generator=UpperCamelCase      ,							device=UpperCamelCase      ),)

     if include_encoder_hidden_states:
       __a			=					floats_tensor((batch_size, 32, 32)      ).to(UpperCamelCase      )

     if include_skip_sample:
       __a			=					randn_tensor(((batch_size, 3) + sizes)      ,							generator=UpperCamelCase      ,							device=UpperCamelCase      )

     return dummy_input







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					{
         'in_channels': 32,
         'out_channels': 32,
         'temb_channels': 128,
     }
     if self.block_type == "up":
       __a			=					32

     if self.block_type == "mid":
       init_dict.pop('out_channels'      )

     __a			=					self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     __a	,       __a			=					self.prepare_init_args_and_inputs_for_common()
     __a			=					self.block_class(**UpperCamelCase      )
     unet_block.to(UpperCamelCase      )
     unet_block.eval()

     with torch.no_grad():
       __a			=					unet_block(**UpperCamelCase      )

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					output[0]

     self.assertEqual(output.shape      ,							self.output_shape      )

     __a			=					output[0, -1, -3:, -3:]
     __a			=					torch.tensor(UpperCamelCase      ).to(UpperCamelCase      )
     assert torch_all_close(output_slice.flatten()      ,							UpperCamelCase      ,							atol=5e-3      )







   @unittest.skipIf(torch_device == 'mps'      ,							'Training is not supported in mps'      )
   def 		UpperCamelCase__			(	self      )				->    Any:
     __a	,       __a			=					self.prepare_init_args_and_inputs_for_common()
     __a			=					self.block_class(**UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.train()
     __a			=					model(**UpperCamelCase      )

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					output[0]

     __a			=					torch.device(UpperCamelCase      )
     __a			=					randn_tensor(output.shape      ,							device=UpperCamelCase      )
     __a			=					torch.nn.functional.mse_loss(UpperCamelCase      ,							UpperCamelCase      )
     loss.backward()









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase_ 		=      {"mobilebert-uncased": 5_12}


UpperCAmelCase_ 		=      {}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      MobileBertTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    List[Any]:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         normalizer_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or normalizer_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'      ,							UpperCamelCase      ) != tokenize_chinese_chars
     ):
       __a			=					getattr(UpperCamelCase      ,							normalizer_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					tokenize_chinese_chars
       __a			=					normalizer_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )



