
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	UNetaDModel
     lowerCAmelCase_     =	'''sample'''



     @property
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						4
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						14
       __SCREAMING_SNAKE_CASE     :					Dict			=						16

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([10] * batch_size				).to(_A				)

       return {"sample": noise, "timestep": time_step}



     @property
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       return (4, 14, 16)



     @property
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       return (4, 14, 16)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       pass



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       super().test_determinism()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       super().test_outputs_equivalence()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       super().test_from_save_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       super().test_from_save_pretrained_variant()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       super().test_model_from_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       super().test_output()



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						{
           '''block_out_channels''': (32, 64, 128, 256),
           '''in_channels''': 14,
           '''out_channels''': 14,
           '''time_embedding_type''': '''positional''',
           '''use_timestep_embedding''': True,
           '''flip_sin_to_cos''': False,
           '''freq_shift''': 1.0,
           '''out_block_type''': '''OutConv1DBlock''',
           '''mid_block_type''': '''MidResTemporalBlock1D''',
           '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
           '''up_block_types''': ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D'''),
           '''act_fn''': '''mish''',
       }
       __SCREAMING_SNAKE_CASE     :					int			=						self.dummy_input
       return init_dict, inputs_dict



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''unet'''				)
       self.assertIsNotNone(_A				)
       self.assertEqual(len(loading_info['''missing_keys''']				)      ,		0				)

       model.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(**self.dummy_input				)

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



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						UNetaDModel.from_pretrained('''bglick13/hopper-medium-v2-value-function-hor32'''      ,		subfolder='''unet'''				)
       torch.manual_seed(0				)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0				)

       __SCREAMING_SNAKE_CASE     :					Any			=						model.config.in_channels
       __SCREAMING_SNAKE_CASE     :					Dict			=						16
       __SCREAMING_SNAKE_CASE     :					int			=						torch.randn((1, seq_len, num_features)				).permute(
           0      ,		2      ,		1				)  # match original, we can update values and remove
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.full((num_features,)      ,		0				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					int			=						model(_A      ,		_A				).sample.permute(0      ,		2      ,		1				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						output[0, -3:, -3:].flatten()
       # fmt: off
       __SCREAMING_SNAKE_CASE     :					int			=						torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
       # fmt: on
       self.assertTrue(torch.allclose(_A      ,		_A      ,		rtol=1e-3				)				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       pass




     @slow
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						'''harmonai/maestro-150k'''
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						UNetaDModel.from_pretrained(_A      ,		subfolder='''unet'''				)
       model.to(_A				)

       __SCREAMING_SNAKE_CASE     :					Any			=						6_5536
       __SCREAMING_SNAKE_CASE     :					Any			=						torch.sin(torch.arange(_A				)[None, None, :].repeat(1      ,		2      ,		1				)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						torch.tensor([1]				).to(_A				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(_A      ,		_A				).sample

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						output.abs().sum()
       __SCREAMING_SNAKE_CASE     :					Any			=						output.abs().max()

       assert (output_sum - 224.0896).abs() < 0.5
       assert (output_max - 0.06_07).abs() < 4e-4






class   __UpperCamelCase				(    lowerCAmelCase__				,		lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	UNetaDModel
     lowerCAmelCase_     =	'''sample'''



     @property
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						4
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						14
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						16

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						floats_tensor((batch_size, num_features, seq_len)				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						torch.tensor([10] * batch_size				).to(_A				)

       return {"sample": noise, "timestep": time_step}



     @property
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       return (4, 14, 16)



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       return (4, 14, 1)



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       super().test_determinism()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       super().test_outputs_equivalence()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       super().test_from_save_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       super().test_from_save_pretrained_variant()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       super().test_model_from_pretrained()



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_class(**_A				)
       model.to(_A				)
       model.eval()

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						model(**_A				)

         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					str			=						output.sample

       self.assertIsNotNone(_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.Size((inputs_dict['''sample'''].shape[0], 1)				)
       self.assertEqual(output.shape      ,		_A      ,		'''Input and output shapes do not match'''				)



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{
           '''in_channels''': 14,
           '''out_channels''': 14,
           '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
           '''up_block_types''': [],
           '''out_block_type''': '''ValueFunction''',
           '''mid_block_type''': '''ValueFunctionMidBlock1D''',
           '''block_out_channels''': [32, 64, 128, 256],
           '''layers_per_block''': 1,
           '''downsample_each_block''': True,
           '''use_timestep_embedding''': True,
           '''freq_shift''': 1.0,
           '''flip_sin_to_cos''': False,
           '''time_embedding_type''': '''positional''',
           '''act_fn''': '''mish''',
       }
       __SCREAMING_SNAKE_CASE     :					int			=						self.dummy_input
       return init_dict, inputs_dict



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''value_function'''				)
       self.assertIsNotNone(_A				)
       self.assertEqual(len(vf_loading_info['''missing_keys''']				)      ,		0				)

       value_function.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						value_function(**self.dummy_input				)

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



     @unittest.skipIf(torch_device == '''mps'''      ,		'''mish op not supported in MPS'''				)
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						UNetaDModel.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32'''      ,		output_loading_info=_A      ,		subfolder='''value_function'''				)
       torch.manual_seed(0				)
       if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0				)

       __SCREAMING_SNAKE_CASE     :					Any			=						value_function.config.in_channels
       __SCREAMING_SNAKE_CASE     :					int			=						14
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.randn((1, seq_len, num_features)				).permute(
           0      ,		2      ,		1				)  # match original, we can update values and remove
       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.full((num_features,)      ,		0				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						value_function(_A      ,		_A				).sample

       # fmt: off
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([165.25] * seq_len				)
       # fmt: on
       self.assertTrue(torch.allclose(_A      ,		_A      ,		rtol=1e-3				)				)




     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       pass


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					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				) * [0] + len(token_ids_a + sep				) * [1]



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
