
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class      A__							:






				def __init__(						self					:      str    ,     a					:      Optional[int]    ,     a					:      Optional[Any]=99    ,     a					:      List[str]=13    ,     a					:      str=7    ,     a					:      List[Any]=9    ,     a					:      List[Any]=True    ,     a					:      Any=True    ,     a					:      Union[str, Any]=False    ,     a					:      str=32    ,     a					:      Optional[int]=5    ,     a					:      Tuple=4    ,     a					:      Optional[Any]=37    ,     a					:      Dict=8    ,     a					:      str=0.1    ,     a					:      List[str]=0.0_0_2    ,     a					:      Union[str, Any]=1    ,     a					:      Union[str, Any]=0    ,     a					:      Dict=0    ,     a					:      Tuple=None    ,     a					:      Any=None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		parent
							lowerCAmelCase__      :      Any         =		batch_size
							lowerCAmelCase__      :      Optional[Any]         =		encoder_seq_length
							lowerCAmelCase__      :      str         =		decoder_seq_length
							# For common tests
							lowerCAmelCase__      :      Union[str, Any]         =		self.decoder_seq_length
							lowerCAmelCase__      :      Optional[Any]         =		is_training
							lowerCAmelCase__      :      Dict         =		use_attention_mask
							lowerCAmelCase__      :      Any         =		use_labels
							lowerCAmelCase__      :      Tuple         =		vocab_size
							lowerCAmelCase__      :      Tuple         =		hidden_size
							lowerCAmelCase__      :      Optional[int]         =		num_hidden_layers
							lowerCAmelCase__      :      Tuple         =		num_attention_heads
							lowerCAmelCase__      :      Tuple         =		d_ff
							lowerCAmelCase__      :      Optional[int]         =		relative_attention_num_buckets
							lowerCAmelCase__      :      Optional[Any]         =		dropout_rate
							lowerCAmelCase__      :      Any         =		initializer_factor
							lowerCAmelCase__      :      Any         =		eos_token_id
							lowerCAmelCase__      :      int         =		pad_token_id
							lowerCAmelCase__      :      str         =		decoder_start_token_id
							lowerCAmelCase__      :      Union[str, Any]         =		None
							lowerCAmelCase__      :      List[Any]         =		decoder_layers






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return TaConfig.from_pretrained('google/umt5-base'   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Union[str, Any]    ,     a					:      List[Any]    ,     a					:      int    ,     a					:      Optional[int]=None    ,     a					:      int=None    ,     a					:      Optional[Any]=None    ,     a					:      Optional[Any]=None    ,     a					:      int=None    ,     ):






							'''simple docstring'''







							if attention_mask is None:
										lowerCAmelCase__      :      Tuple         =		input_ids.ne(config.pad_token_id   )
							if decoder_attention_mask is None:
										lowerCAmelCase__      :      Dict         =		decoder_input_ids.ne(config.pad_token_id   )
							if head_mask is None:
										lowerCAmelCase__      :      Optional[int]         =		torch.ones(config.num_hidden_layers    ,     config.num_attention_heads    ,     device=a   )
							if decoder_head_mask is None:
										lowerCAmelCase__      :      Any         =		torch.ones(config.num_decoder_layers    ,     config.num_attention_heads    ,     device=a   )
							if cross_attn_head_mask is None:
										lowerCAmelCase__      :      int         =		torch.ones(
										    config.num_decoder_layers    ,     config.num_attention_heads    ,     device=a   )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		ids_tensor([self.batch_size, self.encoder_seq_length]    ,     self.vocab_size   )
							lowerCAmelCase__      :      Union[str, Any]         =		ids_tensor([self.batch_size, self.decoder_seq_length]    ,     self.vocab_size   )

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for NllbMoe the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							lowerCAmelCase__      :      Dict         =		input_ids.clamp(self.pad_token_id + 1   )
							lowerCAmelCase__      :      Dict         =		decoder_input_ids.clamp(self.pad_token_id + 1   )

							lowerCAmelCase__      :      Dict         =		self.get_config()
							lowerCAmelCase__      :      str         =		config.num_attention_heads
							lowerCAmelCase__      :      Optional[int]         =		self.prepare_inputs_dict(a    ,     a    ,     a   )
							return config, input_dict






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Tuple         =		self.prepare_config_and_inputs()
							return config, inputs_dict






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return TaConfig(
							    vocab_size=166    ,     d_model=self.hidden_size    ,     d_ff=self.d_ff    ,     d_kv=self.hidden_size // self.num_attention_heads    ,     num_layers=self.num_hidden_layers    ,     num_decoder_layers=self.decoder_layers    ,     num_heads=self.num_attention_heads    ,     relative_attention_num_buckets=self.relative_attention_num_buckets    ,     dropout_rate=self.dropout_rate    ,     initializer_factor=self.initializer_factor    ,     eos_token_id=self.eos_token_id    ,     bos_token_id=self.pad_token_id    ,     pad_token_id=self.pad_token_id    ,     decoder_start_token_id=self.decoder_start_token_id    ,     )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							return TaConfig(
							    vocab_size=self.vocab_size    ,     d_model=self.hidden_size    ,     d_ff=self.d_ff    ,     d_kv=self.hidden_size // self.num_attention_heads    ,     num_layers=self.num_hidden_layers    ,     num_decoder_layers=self.decoder_layers    ,     num_heads=self.num_attention_heads    ,     relative_attention_num_buckets=self.relative_attention_num_buckets    ,     dropout_rate=self.dropout_rate    ,     initializer_factor=self.initializer_factor    ,     eos_token_id=self.eos_token_id    ,     bos_token_id=self.pad_token_id    ,     pad_token_id=self.pad_token_id    ,     decoder_start_token_id=self.decoder_start_token_id    ,     )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      str    ,     a					:      Any    ,     a					:      Union[str, Any]    ,     a					:      Optional[Any]    ,     a					:      List[str]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		UMTaModel(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Optional[Any]         =		model(
							    input_ids=a    ,     decoder_input_ids=a    ,     attention_mask=a    ,     decoder_attention_mask=a    ,     )
							lowerCAmelCase__      :      Tuple         =		model(input_ids=a    ,     decoder_input_ids=a   )
							lowerCAmelCase__      :      Dict         =		result.last_hidden_state
							lowerCAmelCase__      :      str         =		result.past_key_values
							lowerCAmelCase__      :      Dict         =		result.encoder_last_hidden_state

							self.parent.assertEqual(encoder_output.size()    ,     (self.batch_size, self.encoder_seq_length, self.hidden_size)   )
							self.parent.assertEqual(decoder_output.size()    ,     (self.batch_size, self.decoder_seq_length, self.hidden_size)   )
							# There should be `num_layers` key value embeddings stored in decoder_past
							self.parent.assertEqual(len(a   )    ,     config.num_layers   )
							# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
							self.parent.assertEqual(len(decoder_past[0]   )    ,     4   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      Union[str, Any]    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]    ,     a					:      Optional[int]    ,     a					:      Union[str, Any]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		UMTaModel(config=a   ).get_decoder().to(a   ).eval()
							# first forward pass
							lowerCAmelCase__      :      Optional[Any]         =		model(a    ,     use_cache=a   )
							lowerCAmelCase__      :      Optional[Any]         =		model(a   )
							lowerCAmelCase__      :      Union[str, Any]         =		model(a    ,     use_cache=a   )

							self.parent.assertTrue(len(a   ) == len(a   )   )
							self.parent.assertTrue(len(a   ) == len(a   ) + 1   )

							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__      :      List[Any]         =		ids_tensor((self.batch_size, 1)    ,     config.vocab_size   )

							# append to next input_ids and
							lowerCAmelCase__      :      int         =		torch.cat([input_ids, next_tokens]    ,     dim=-1   )

							lowerCAmelCase__      :      Union[str, Any]         =		model(a   )['last_hidden_state']
							lowerCAmelCase__      :      Union[str, Any]         =		model(a    ,     past_key_values=a   )['last_hidden_state']

							# select random slice
							lowerCAmelCase__      :      int         =		ids_tensor((1,)    ,     output_from_past.shape[-1]   ).item()
							lowerCAmelCase__      :      int         =		output_from_no_past[:, -1, random_slice_idx].detach()
							lowerCAmelCase__      :      Union[str, Any]         =		output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(a    ,     a    ,     atol=1E-3   )   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict    ,     a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		UMTaModel(config=a   ).to(a   ).half().eval()
							lowerCAmelCase__      :      int         =		model(**a   )['last_hidden_state']
							self.parent.assertFalse(torch.isnan(a   ).any().item()   )







@require_torch
class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   unittest.TestCase	):
				lowercase			    =  (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				lowercase			    =  (UMTaForConditionalGeneration,) if is_torch_available() else ()
				lowercase			    =  (
				    {
				        'conversational': UMTaForConditionalGeneration,
				        'feature-extraction': UMTaModel,
				        'summarization': UMTaForConditionalGeneration,
				        'text2text-generation': UMTaForConditionalGeneration,
				        'translation': UMTaForConditionalGeneration,
				        'question-answering': UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase			    =  True
				lowercase			    =  False
				lowercase			    =  False
				lowercase			    =  True
				lowercase			    =  True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				lowercase			    =  [0.8, 0.9]






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		UMTaModelTester(self   )






				@unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.model_tester.prepare_config_and_inputs()
							lowerCAmelCase__      :      Union[str, Any]         =		UMTaModel(config_and_inputs[0]   ).to(a   )
							with tempfile.TemporaryDirectory() as tmpdirname:
										torch.onnx.export(
										    a    ,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,     f'''{tmpdirname}/t5_test.onnx'''    ,     export_params=a    ,     opset_version=9    ,     input_names=['input_ids', 'decoder_input_ids']    ,     )






				@unittest.skipIf(torch_device == 'cpu'    ,     'Cant do half precision'   )
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*a   )






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
							lowerCAmelCase__      :      Any         =		self.model_tester.prepare_config_and_inputs()
							lowerCAmelCase__      :      Optional[int]         =		config_and_inputs[0]
							lowerCAmelCase__      :      Dict         =		UMTaForConditionalGeneration(a   ).eval()
							model.to(a   )

							lowerCAmelCase__      :      Union[str, Any]         =		{
							    'head_mask': torch.zeros(config.num_layers    ,     config.num_heads    ,     device=a   ),
							    'decoder_head_mask': torch.zeros(config.num_decoder_layers    ,     config.num_heads    ,     device=a   ),
							    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers    ,     config.num_heads    ,     device=a   ),
							}

							for attn_name, (name, mask) in zip(a    ,     head_masking.items()   ):
										lowerCAmelCase__      :      Dict         =		{name: mask}
										# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
										if name == "head_mask":
													lowerCAmelCase__      :      str         =		torch.ones(
													    config.num_decoder_layers    ,     config.num_heads    ,     device=a   )

										lowerCAmelCase__      :      str         =		model.generate(
										    config_and_inputs[1]['input_ids']    ,     num_beams=1    ,     max_length=3    ,     output_attentions=a    ,     return_dict_in_generate=a    ,     **a    ,     )
										# We check the state of decoder_attentions and cross_attentions just from the last step
										lowerCAmelCase__      :      str         =		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
										self.assertEqual(sum([w.sum().item() for w in attn_weights]   )    ,     0.0   )






				@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'   )
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							pass







@require_torch
@require_sentencepiece
@require_tokenizers
class      A__							(     unittest.TestCase	):






				@slow
				@unittest.skip(
				    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'   )
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,     return_dict=a   ).to(a   )
							lowerCAmelCase__      :      str         =		AutoTokenizer.from_pretrained('google/umt5-small'    ,     use_fast=a    ,     legacy=a   )
							lowerCAmelCase__      :      str         =		[
							    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
							    'No se como puedo <extra_id_0>.',
							    'This is the reason why we <extra_id_0> them.',
							    'The <extra_id_0> walks in <extra_id_1>, seats',
							    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
							]
							lowerCAmelCase__      :      str         =		tokenizer(a    ,     return_tensors='pt'    ,     padding=a   ).input_ids
							# fmt: off
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor(
							    [
							        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
							    ]   )
							# fmt: on
							torch.testing.assert_allclose(a    ,     a   )

							lowerCAmelCase__      :      Union[str, Any]         =		model.generate(input_ids.to(a   )   )
							lowerCAmelCase__      :      List[Any]         =		[
							    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
							    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
							    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
							    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
							    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
							]
							lowerCAmelCase__      :      List[str]         =		tokenizer.batch_decode(a   )
							self.assertEqual(a    ,     a   )

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__							:






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return self.get_dummy_input()






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''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 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]=True    ,     a					:      Any=False    ,     a					:      Dict=False    ,     a					:      Union[str, Any]=False    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		4
							lowerCAmelCase__      :      int         =		32
							lowerCAmelCase__      :      Tuple         =		(32, 32)

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		torch.device(a   )
							lowerCAmelCase__      :      str         =		(batch_size, num_channels) + sizes
							lowerCAmelCase__      :      Tuple         =		randn_tensor(a    ,     generator=a    ,     device=a   )
							lowerCAmelCase__      :      Optional[Any]         =		{'hidden_states': hidden_states}

							if include_temb:
										lowerCAmelCase__      :      int         =		128
										lowerCAmelCase__      :      List[str]         =		randn_tensor((batch_size, temb_channels)    ,     generator=a    ,     device=a   )

							if include_res_hidden_states_tuple:
										lowerCAmelCase__      :      int         =		torch.manual_seed(1   )
										lowerCAmelCase__      :      str         =		(randn_tensor(a    ,     generator=a    ,     device=a   ),)

							if include_encoder_hidden_states:
										lowerCAmelCase__      :      Any         =		floats_tensor((batch_size, 32, 32)   ).to(a   )

							if include_skip_sample:
										lowerCAmelCase__      :      Union[str, Any]         =		randn_tensor(((batch_size, 3) + sizes)    ,     generator=a    ,     device=a   )

							return dummy_input






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{
							    'in_channels': 32,
							    'out_channels': 32,
							    'temb_channels': 128,
							}
							if self.block_type == "up":
										lowerCAmelCase__      :      Union[str, Any]         =		32

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

							lowerCAmelCase__      :      Optional[int]         =		self.dummy_input
							return init_dict, inputs_dict






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      int         =		self.block_class(**a   )
							unet_block.to(a   )
							unet_block.eval()

							with torch.no_grad():
										lowerCAmelCase__      :      int         =		unet_block(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		output[0]

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

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






				@unittest.skipIf(torch_device == 'mps'    ,     'Training is not supported in mps'   )
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      Any         =		self.block_class(**a   )
							model.to(a   )
							model.train()
							lowerCAmelCase__      :      int         =		model(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		output[0]

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