




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     BioGptForCausalLM,
     BioGptForSequenceClassification,
     BioGptForTokenClassification,
     BioGptModel,
     BioGptTokenizer,
 )
 from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						str		,							A_      :						Optional[Any]=13		,							A_      :						List[Any]=7		,							A_      :						int=True		,							A_      :						Union[str, Any]=True		,							A_      :						int=False		,							A_      :						Optional[Any]=True		,							A_      :						List[Any]=99		,							A_      :						str=32		,							A_      :						Union[str, Any]=5		,							A_      :						Optional[Any]=4		,							A_      :						str=37		,							A_      :						str="gelu"		,							A_      :						Dict=0.1		,							A_      :						str=0.1		,							A_      :						Optional[int]=512		,							A_      :						Optional[int]=16		,							A_      :						List[Any]=2		,							A_      :						Dict=0.02		,							A_      :						Dict=3		,							A_      :						Union[str, Any]=4		,							A_      :						List[str]=None		,							)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					seq_length
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					use_input_mask
       lowerCamelCase_	  =					use_token_type_ids
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					type_vocab_size
       lowerCamelCase_	  =					type_sequence_label_size
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					num_labels
       lowerCamelCase_	  =					num_choices
       lowerCamelCase_	  =					scope

 def        a__				(       self      :						Optional[Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size						)

       lowerCamelCase_	  =					None
       if self.use_input_mask:
             lowerCamelCase_	  =					random_attention_mask([self.batch_size, self.seq_length]						)

       lowerCamelCase_	  =					None
       if self.use_token_type_ids:
             lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.type_vocab_size						)

       lowerCamelCase_	  =					None
       lowerCamelCase_	  =					None
       lowerCamelCase_	  =					None
       if self.use_labels:
             lowerCamelCase_	  =					ids_tensor([self.batch_size]		,							self.type_sequence_label_size						)
             lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.num_labels						)
             lowerCamelCase_	  =					ids_tensor([self.batch_size]		,							self.num_choices						)

       lowerCamelCase_	  =					self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def        a__				(       self      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       return BioGptConfig(
           vocab_size=self.vocab_size		,							hidden_size=self.hidden_size		,							num_hidden_layers=self.num_hidden_layers		,							num_attention_heads=self.num_attention_heads		,							intermediate_size=self.intermediate_size		,							hidden_act=self.hidden_act		,							hidden_dropout_prob=self.hidden_dropout_prob		,							attention_probs_dropout_prob=self.attention_probs_dropout_prob		,							max_position_embeddings=self.max_position_embeddings		,							type_vocab_size=self.type_vocab_size		,							is_decoder=A_		,							initializer_range=self.initializer_range		,							)

 def        a__				(       self      :						Optional[Any]		,							A_      :						Union[str, Any]		,							A_      :						Optional[Any]		,							A_      :						Tuple		,							A_      :						Dict		,							A_      :						List[str]		,							A_      :						Union[str, Any]		,							A_      :						Union[str, Any]						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptModel(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_						)
       lowerCamelCase_	  =					model(A_						)
       self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size)						)

 def        a__				(       self      :						Optional[Any]		,							A_      :						int		,							A_      :						List[str]		,							A_      :						Optional[Any]		,							A_      :						Tuple		,							A_      :						List[str]		,							A_      :						List[Any]		,							A_      :						Any		,							A_      :						Dict		,							A_      :						Optional[Any]		,							)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptForCausalLM(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_		,							labels=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.vocab_size)						)

 def        a__				(       self      :						Union[str, Any]		,							A_      :						Optional[int]		,							A_      :						Tuple		,							A_      :						List[Any]		,							A_      :						str		,							A_      :						str		,							*A_      :						List[str]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptModel(config=A_						)
       model.to(A_						)
       model.eval()

       # create attention mask
       lowerCamelCase_	  =					torch.ones(input_ids.shape		,							dtype=torch.long		,							device=A_						)
       lowerCamelCase_	  =					self.seq_length // 2
       lowerCamelCase_	  =					0

       # first forward pass
       lowerCamelCase_  ,       lowerCamelCase_	  =					model(A_		,							attention_mask=A_						).to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowerCamelCase_	  =					ids_tensor((self.batch_size, 1)		,							config.vocab_size						)

       # change a random masked slice from input_ids
       lowerCamelCase_	  =					ids_tensor((1,)		,							A_						).item() + 1
       lowerCamelCase_	  =					ids_tensor((self.batch_size, 1)		,							config.vocab_size						).squeeze(-1						)
       lowerCamelCase_	  =					random_other_next_tokens

       # append to next input_ids and attn_mask
       lowerCamelCase_	  =					torch.cat([input_ids, next_tokens]		,							dim=-1						)
       lowerCamelCase_	  =					torch.cat(
           [attn_mask, torch.ones((attn_mask.shape[0], 1)		,							dtype=torch.long		,							device=A_						)]		,							dim=1		,							)

       # get two different outputs
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_						)['last_hidden_state']
       lowerCamelCase_	  =					model(A_		,							past_key_values=A_		,							attention_mask=A_						)['last_hidden_state']

       # select random slice
       lowerCamelCase_	  =					ids_tensor((1,)		,							output_from_past.shape[-1]						).item()
       lowerCamelCase_	  =					output_from_no_past[:, -1, random_slice_idx].detach()
       lowerCamelCase_	  =					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        a__				(       self      :						Dict		,							A_      :						List[str]		,							A_      :						Any		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						Tuple		,							*A_      :						Tuple						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptModel(config=A_						).to(A_						).eval()

       lowerCamelCase_	  =					torch.ones(input_ids.shape		,							dtype=torch.long		,							device=A_						)

       # first forward pass
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							use_cache=A_						)

       lowerCamelCase_  ,       lowerCamelCase_	  =					outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       lowerCamelCase_	  =					ids_tensor((self.batch_size, 3)		,							config.vocab_size						)
       lowerCamelCase_	  =					ids_tensor((self.batch_size, 3)		,							2						)

       # append to next input_ids and
       lowerCamelCase_	  =					torch.cat([input_ids, next_tokens]		,							dim=-1						)
       lowerCamelCase_	  =					torch.cat([attention_mask, next_attn_mask]		,							dim=-1						)

       lowerCamelCase_	  =					model(A_		,							attention_mask=A_						)['last_hidden_state']
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							past_key_values=A_						)[
           'last_hidden_state'
       ]

       # select random slice
       lowerCamelCase_	  =					ids_tensor((1,)		,							output_from_past.shape[-1]						).item()
       lowerCamelCase_	  =					output_from_no_past[:, -3:, random_slice_idx].detach()
       lowerCamelCase_	  =					output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

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

 def        a__				(       self      :						Union[str, Any]		,							A_      :						List[Any]		,							A_      :						Tuple		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]		,							A_      :						List[str]		,							*A_      :						Tuple		,							A_      :						Tuple=False						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptForCausalLM(A_						)
       model.to(A_						)
       if gradient_checkpointing:
             model.gradient_checkpointing_enable()

       lowerCamelCase_	  =					model(A_		,							labels=A_						)
       self.parent.assertEqual(result.loss.shape		,							()						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.vocab_size)						)
       result.loss.backward()

 def        a__				(       self      :						Union[str, Any]		,							A_      :						Tuple		,							*A_      :						str						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptModel(A_						)
       lowerCamelCase_	  =					model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers						)
       for key in model.state_dict().keys():
             if "c_proj" in key and "weight" in key:
                   self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]						) - model_std						)		,							0.001						)
                   self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]						) - 0.0						)		,							0.01						)

 def        a__				(       self      :						int		,							A_      :						List[Any]		,							A_      :						Optional[int]		,							A_      :						Any		,							A_      :						Tuple		,							A_      :						Tuple		,							*A_      :						Any						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					BioGptForTokenClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.num_labels)						)





 def        a__				(       self      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       (
           lowerCamelCase_
       )  ,       
       )	  =					config_and_inputs
       lowerCamelCase_	  =					{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 						A(		UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 UpperCamelCase		     =		(BioGptForCausalLM,) if is_torch_available() else ()
 UpperCamelCase		     =		(
     {
         '''feature-extraction''': BioGptModel,
         '''text-classification''': BioGptForSequenceClassification,
         '''text-generation''': BioGptForCausalLM,
         '''token-classification''': BioGptForTokenClassification,
         '''zero-shot''': BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCamelCase		     =		False

 def        a__				(       self      :						List[Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_		,							hidden_size=37						)

 def        a__				(       self      :						str						)     ->			Dict:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_						)

 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             lowerCamelCase_	  =					type
             self.model_tester.create_and_check_model(*A_						)

 def        a__				(       self      :						int						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A_						)

 def        a__				(       self      :						Union[str, Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*A_		,							gradient_checkpointing=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A_						)

 def        a__				(       self      :						List[str]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*A_						)

 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*A_						)

 @slow
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       model.to(A_						)
       lowerCamelCase_	  =					BioGptTokenizer.from_pretrained('microsoft/biogpt'						)

       lowerCamelCase_	  =					'left'

       # Define PAD Token = EOS Token = 50256
       lowerCamelCase_	  =					tokenizer.eos_token
       lowerCamelCase_	  =					model.config.eos_token_id

       # use different length sentences to test batching
       lowerCamelCase_	  =					[
           'Hello, my dog is a little',
           'Today, I',
       ]

       lowerCamelCase_	  =					tokenizer(A_		,							return_tensors='pt'		,							padding=A_						)
       lowerCamelCase_	  =					inputs['input_ids'].to(A_						)

       lowerCamelCase_	  =					model.generate(
           input_ids=A_		,							attention_mask=inputs['attention_mask'].to(A_						)		,							)

       lowerCamelCase_	  =					tokenizer(sentences[0]		,							return_tensors='pt'						).input_ids.to(A_						)
       lowerCamelCase_	  =					model.generate(input_ids=A_						)

       lowerCamelCase_	  =					inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
       lowerCamelCase_	  =					tokenizer(sentences[1]		,							return_tensors='pt'						).input_ids.to(A_						)
       lowerCamelCase_	  =					model.generate(input_ids=A_		,							max_length=model.config.max_length - num_paddings						)

       lowerCamelCase_	  =					tokenizer.batch_decode(A_		,							skip_special_tokens=A_						)
       lowerCamelCase_	  =					tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=A_						)
       lowerCamelCase_	  =					tokenizer.decode(output_padded[0]		,							skip_special_tokens=A_						)

       lowerCamelCase_	  =					[
           'Hello, my dog is a little bit bigger than a little bit.',
           'Today, I have a good idea of how to use the information',
       ]
       self.assertListEqual(A_		,							A_						)
       self.assertListEqual(A_		,							[non_padded_sentence, padded_sentence]						)

 @slow
 def        a__				(       self      :						List[str]						)     ->			Optional[int]:

       """simple docstring"""






       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					BioGptModel.from_pretrained(A_						)
             self.assertIsNotNone(A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_	  =					3
       lowerCamelCase_	  =					input_dict['input_ids']
       lowerCamelCase_	  =					input_ids.ne(1						).to(A_						)
       lowerCamelCase_	  =					ids_tensor([self.model_tester.batch_size]		,							self.model_tester.type_sequence_label_size						)
       lowerCamelCase_	  =					BioGptForSequenceClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							labels=A_						)
       self.assertEqual(result.logits.shape		,							(self.model_tester.batch_size, self.model_tester.num_labels)						)





 def        a__				(       self      :						Any						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_	  =					3
       lowerCamelCase_	  =					'multi_label_classification'
       lowerCamelCase_	  =					input_dict['input_ids']
       lowerCamelCase_	  =					input_ids.ne(1						).to(A_						)
       lowerCamelCase_	  =					ids_tensor(
           [self.model_tester.batch_size, config.num_labels]		,							self.model_tester.type_sequence_label_size						).to(torch.float						)
       lowerCamelCase_	  =					BioGptForSequenceClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							labels=A_						)
       self.assertEqual(result.logits.shape		,							(self.model_tester.batch_size, self.model_tester.num_labels)						)







@require_torch
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 @slow
 def        a__				(       self      :						Any						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       lowerCamelCase_	  =					torch.tensor([[2, 4805, 9, 656, 21]]						)
       lowerCamelCase_	  =					model(A_						)[0]

       lowerCamelCase_	  =					42384

       lowerCamelCase_	  =					torch.Size((1, 5, vocab_size)						)
       self.assertEqual(output.shape		,							A_						)

       lowerCamelCase_	  =					torch.tensor(
           [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]						)

       self.assertTrue(torch.allclose(output[:, :3, :3]		,							A_		,							atol=1E-4						)						)





 @slow
 def        a__				(       self      :						int						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					BioGptTokenizer.from_pretrained('microsoft/biogpt'						)
       lowerCamelCase_	  =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'						)
       model.to(A_						)

       torch.manual_seed(0						)
       lowerCamelCase_	  =					tokenizer('COVID-19 is'		,							return_tensors='pt'						).to(A_						)
       lowerCamelCase_	  =					model.generate(
           **A_		,							min_length=100		,							max_length=1024		,							num_beams=5		,							early_stopping=A_		,							)
       lowerCamelCase_	  =					tokenizer.decode(output_ids[0]		,							skip_special_tokens=A_						)

       lowerCamelCase_	  =					(
           'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
           ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
           ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
           ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
           ' more than 800,000 deaths.'
       )
       self.assertEqual(A_		,							A_						)









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



