import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCAmelCase_           =				logging.get_logger(__name__)


UpperCAmelCase_           =				R'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   @add_start_docstrings(_lowercase				)
   def __call__(						self       :	Tuple		,	_lowercase       :	torch.LongTensor		,	_lowercase       :	torch.FloatTensor		,	**_lowercase       :	Optional[int]				):



    """simple docstring"""



    raise NotImplementedError('''StoppingCriteria needs to be subclassed'''				)




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	int		,	_lowercase       :	Optional[int] = None				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			max_length
    _UpperCamelCase:  Optional[int]    						=			max_position_embeddings



   @add_start_docstrings(_lowercase				)
   def __call__(						self       :	Any		,	_lowercase       :	torch.LongTensor		,	_lowercase       :	torch.FloatTensor		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			input_ids.shape[-1]
    _UpperCamelCase:  int    						=			cur_len >= self.max_length
    if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
     logger.warning_once(
         '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
         f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
         '''exceptions, performance degradation, or nothing at all.'''				)
    return is_done




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	int				):



    """simple docstring"""



    warnings.warn(
        '''The class `MaxNewTokensCriteria` is deprecated. '''
        f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
        '''with `max_length = start_length + max_new_tokens` instead.'''		,	_lowercase		,	)
    _UpperCamelCase:  Optional[int]    						=			start_length
    _UpperCamelCase:  Dict    						=			max_new_tokens
    _UpperCamelCase:  List[Any]    						=			start_length + max_new_tokens



   @add_start_docstrings(_lowercase				)
   def __call__(						self       :	str		,	_lowercase       :	torch.LongTensor		,	_lowercase       :	torch.FloatTensor		,	**_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    return input_ids.shape[-1] >= self.max_length




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Optional[int]		,	_lowercase       :	float		,	_lowercase       :	Optional[float] = None				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			max_time
    _UpperCamelCase:  Any    						=			time.time() if initial_timestamp is None else initial_timestamp



   @add_start_docstrings(_lowercase				)
   def __call__(						self       :	Dict		,	_lowercase       :	torch.LongTensor		,	_lowercase       :	torch.FloatTensor		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    return time.time() - self.initial_timestamp > self.max_time




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   @add_start_docstrings(_lowercase				)
   def __call__(						self       :	List[str]		,	_lowercase       :	torch.LongTensor		,	_lowercase       :	torch.FloatTensor		,	**_lowercase       :	Dict				):



    """simple docstring"""



    return any(criteria(_lowercase		,	_lowercase				) for criteria in self				)



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



    """simple docstring"""



    for stopping_criterium in self:
     if isinstance(_lowercase		,	_lowercase				):
      return stopping_criterium.max_length
     elif isinstance(_lowercase		,	_lowercase				):
      return stopping_criterium.max_length
    return None




def    lowerCAmelCase_  (		lowercase:				StoppingCriteriaList	,       lowercase:				int		)     ->					StoppingCriteriaList:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			stopping_criteria.max_length
 _UpperCamelCase:  Dict    						=			deepcopy(lowercase		)
 if stopping_max_length is not None and stopping_max_length != max_length:
  warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''	,       lowercase		)
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=lowercase		)		)
 return new_stopping_criteria
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

    _UpperCamelCase:  List[str]    						=			floats_tensor((batch_size, num_channels) + sizes				).to(_lowercase				)

    return {"sample": image}



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



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			{
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _UpperCamelCase:  Optional[Any]    						=			self.dummy_input
    return init_dict, inputs_dict



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  int    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''		,	output_loading_info=_lowercase				)
    self.assertIsNotNone(_lowercase				)
    self.assertEqual(len(loading_info['''missing_keys''']				)		,	0				)

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

    torch.manual_seed(0				)
    if torch.cuda.is_available():
     torch.cuda.manual_seed_all(0				)

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]				)
    # fmt: on
    self.assertTrue(torch.allclose(_lowercase		,	_lowercase		,	atol=1E-3				)				)