



"""simple docstring"""






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__snake_case							:     Optional[Any]   =					logging.get_logger(__name__)

__snake_case							:     List[Any]   =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case							:     Optional[int]   =					{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__snake_case							:     List[str]   =					{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__snake_case							:     int   =					{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__snake_case							:     Union[str, Any]   =					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__snake_case							:     Tuple   =					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__snake_case							:     Optional[Any]   =					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__snake_case							:     Any   =					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__snake_case							:     Optional[Any]   =					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__snake_case							:     Any   =					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE					   =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE					   =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






__snake_case							:     Optional[int]   =					collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__snake_case							:     Any   =					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__snake_case							:     Any   =					R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(__SCREAMING_SNAKE_CASE							)
class     A__     :




	'''simple docstring'''





	def __call__(  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None ,    _SCREAMING_SNAKE_CASE:	Union[bool, str] = False ,    _SCREAMING_SNAKE_CASE:	Union[bool, str] = False ,    _SCREAMING_SNAKE_CASE:	Optional[int] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, TensorType]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    **_SCREAMING_SNAKE_CASE:	List[Any] ,    )       -> BatchEncoding:

				"""simple docstring"""

				if titles is None and texts is None:
							return super().__call__(
							    _SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_tensors=_SCREAMING_SNAKE_CASE ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )
				elif titles is None or texts is None:
							__lowerCAmelCase   :						List[Any]				= titles if texts is None else texts
							return super().__call__(
							    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_tensors=_SCREAMING_SNAKE_CASE ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						Union[str, Any]				= titles if not isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) else [titles]
				__lowerCAmelCase   :						Union[str, Any]				= texts if not isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) else [texts]
				__lowerCAmelCase   :						Dict				= len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= questions if not isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) else [questions] * n_passages
				if len(_SCREAMING_SNAKE_CASE) != len(_SCREAMING_SNAKE_CASE):
							raise ValueError(
							    F"""There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE)} titles and {len(_SCREAMING_SNAKE_CASE)} texts.""")
				__lowerCAmelCase   :						Any				= super().__call__(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE)["input_ids"]
				__lowerCAmelCase   :						str				= super().__call__(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE)["input_ids"]
				__lowerCAmelCase   :						str				= {
				    "input_ids": [
				        (encoded_question_and_title + encoded_text)[:max_length]
				        if max_length is not None and truncation
				        else encoded_question_and_title + encoded_text
				        for encoded_question_and_title, encoded_text in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				    ]
				}
				if return_attention_mask is not False:
							__lowerCAmelCase   :						int				= []
							for input_ids in encoded_inputs["input_ids"]:
										attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
							__lowerCAmelCase   :						List[str]				= attention_mask
				return self.pad(_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_tensors=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	BatchEncoding ,    _SCREAMING_SNAKE_CASE:	DPRReaderOutput ,    _SCREAMING_SNAKE_CASE:	int = 16 ,    _SCREAMING_SNAKE_CASE:	int = 64 ,    _SCREAMING_SNAKE_CASE:	int = 4 ,    )       -> List[DPRSpanPrediction]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= reader_input["input_ids"]
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= reader_output[:3]
				__lowerCAmelCase   :						Dict				= len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= sorted(range(_SCREAMING_SNAKE_CASE) ,    reverse=_SCREAMING_SNAKE_CASE ,    key=relevance_logits.__getitem__)
				__lowerCAmelCase   :						List[DPRReaderOutput]				= []
				for doc_id in sorted_docs:
							__lowerCAmelCase   :						List[Any]				= list(input_ids[doc_id])
							# assuming question & title information is at the beginning of the sequence
							__lowerCAmelCase   :						str				= sequence_ids.index(self.sep_token_id ,    2) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
										__lowerCAmelCase   :						Optional[int]				= sequence_ids.index(self.pad_token_id)
							else:
										__lowerCAmelCase   :						Optional[int]				= len(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase   :						Any				= self._get_best_spans(
							    start_logits=start_logits[doc_id][passage_offset:sequence_len] ,    end_logits=end_logits[doc_id][passage_offset:sequence_len] ,    max_answer_length=_SCREAMING_SNAKE_CASE ,    top_spans=_SCREAMING_SNAKE_CASE ,    )
							for start_index, end_index in best_spans:
										start_index += passage_offset
										end_index += passage_offset
										nbest_spans_predictions.append(
										    DPRSpanPrediction(
										        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,    relevance_score=relevance_logits[doc_id] ,    doc_id=_SCREAMING_SNAKE_CASE ,    start_index=_SCREAMING_SNAKE_CASE ,    end_index=_SCREAMING_SNAKE_CASE ,    text=self.decode(sequence_ids[start_index : end_index + 1]) ,    ))
							if len(_SCREAMING_SNAKE_CASE) >= num_spans:
										break
				return nbest_spans_predictions[:num_spans]







	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	int ,    )       -> List[DPRSpanPrediction]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= []
				for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE):
							for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
										scores.append(((start_index, start_index + answer_length), start_score + end_score))
				__lowerCAmelCase   :						Union[str, Any]				= sorted(_SCREAMING_SNAKE_CASE ,    key=lambda _SCREAMING_SNAKE_CASE: x[1] ,    reverse=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Union[str, Any]				= []
				for (start_index, end_index), score in scores:
							if start_index > end_index:
										raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
							__lowerCAmelCase   :						Any				= end_index - start_index + 1
							if length > max_answer_length:
										raise ValueError(F"""Span is too long: {length} > {max_answer_length}""")
							if any(
							    start_index <= prev_start_index <= prev_end_index <= end_index
							    or prev_start_index <= start_index <= end_index <= prev_end_index
							    for (prev_start_index, prev_end_index) in chosen_span_intervals):
										continue
							chosen_span_intervals.append((start_index, end_index))

							if len(_SCREAMING_SNAKE_CASE) == top_spans:
										break
				return chosen_span_intervals






@add_end_docstrings(__SCREAMING_SNAKE_CASE							)
class     A__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			READER_PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE					   =			READER_PRETRAINED_INIT_CONFIGURATION
	SCREAMING_SNAKE_CASE					   =			['input_ids', 'attention_mask']




"""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     A__     :




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.get_dummy_input()

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				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 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

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

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

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

				__lowerCAmelCase   :						str				= output[0, -1, -3:, -3:]
				__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				assert torch_all_close(output_slice.flatten() ,    _SCREAMING_SNAKE_CASE ,    atol=5e-3)







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()