




import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__UpperCAmelCase							:   List[Any]							=  logging.getLogger(__name__)


class 					_snake_case						(			a__ ):
	_A          =   """summarization"""
	_A          =   ["""loss"""]
	_A          =   ROUGE_KEYS
	_A          =   """rouge2"""






	def __init__(		self					,UpperCamelCase					,**UpperCamelCase       )						->    Tuple:
					if hparams.sortish_sampler and hparams.gpus > 1:
									snake_case__      :Union[str, Any]         =   False
					elif hparams.max_tokens_per_batch is not None:
									if hparams.gpus > 1:
													raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"       )
									if hparams.sortish_sampler:
													raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"       )

					super().__init__(lowerCAmelCase__					,num_labels=lowerCAmelCase__					,mode=self.mode					,**lowerCAmelCase__       )
					use_task_specific_params(self.model					,"summarization"       )
					save_git_info(self.hparams.output_dir       )
					snake_case__      :Optional[Any]         =   Path(self.output_dir       ) / "metrics.json"
					snake_case__      :int         =   Path(self.output_dir       ) / "hparams.pkl"
					pickle_save(self.hparams					,self.hparams_save_path       )
					snake_case__      :List[Any]         =   0
					snake_case__      :Union[str, Any]         =   defaultdict(lowerCAmelCase__       )
					snake_case__      :Any         =   self.config.model_type
					snake_case__      :str         =   self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

					snake_case__      :dict         =   {
					    "data_dir": self.hparams.data_dir,
					    "max_source_length": self.hparams.max_source_length,
					    "prefix": self.model.config.prefix or "",
					}
					snake_case__      :Tuple         =   {
					    "train": self.hparams.n_train,
					    "val": self.hparams.n_val,
					    "test": self.hparams.n_test,
					}
					snake_case__      :Optional[Any]         =   {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

					snake_case__      :str         =   {
					    "train": self.hparams.max_target_length,
					    "val": self.hparams.val_max_target_length,
					    "test": self.hparams.test_max_target_length,
					}
					assert self.target_lens["train"] <= self.target_lens["val"], f'target_lens: {self.target_lens}'
					assert self.target_lens["train"] <= self.target_lens["test"], f'target_lens: {self.target_lens}'
					if self.hparams.freeze_embeds:
									freeze_embeds(self.model       )
					if self.hparams.freeze_encoder:
									freeze_params(self.model.get_encoder()       )
									assert_all_frozen(self.model.get_encoder()       )

					snake_case__      :Optional[Any]         =   get_git_info()["repo_sha"]
					snake_case__      :List[Any]         =   hparams.num_workers
					snake_case__      :Union[str, Any]         =   None  # default to config
					if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer					,lowerCAmelCase__       ):
									snake_case__      :Optional[int]         =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
									snake_case__      :Optional[Any]         =   self.decoder_start_token_id
					snake_case__      :Union[str, Any]         =   (
					    SeqaSeqDataset if hasattr(self.tokenizer					,"prepare_seq2seq_batch"       ) else LegacySeqaSeqDataset
					)
					snake_case__      :Optional[Any]         =   False
					snake_case__      :int         =   self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
					if self.hparams.eval_max_gen_length is not None:
									snake_case__      :Any         =   self.hparams.eval_max_gen_length
					else:
									snake_case__      :Optional[int]         =   self.model.config.max_length
					snake_case__      :Optional[Any]         =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Dict[str, List[str]]:
					snake_case__      :str         =   {
					    k: self.tokenizer.batch_decode(v.tolist()       ) if "mask" not in k else v.shape for k, v in batch.items()
					}
					save_json(lowerCAmelCase__					,Path(self.output_dir       ) / "text_batch.json"       )
					save_json({k: v.tolist() for k, v in batch.items()}					,Path(self.output_dir       ) / "tok_batch.json"       )

					snake_case__      :Any         =   True
					return readable_batch






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,**UpperCamelCase       )						->    int:
					return self.model(lowerCAmelCase__					,**lowerCAmelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.tokenizer.batch_decode(
					    lowerCAmelCase__					,skip_special_tokens=lowerCAmelCase__					,clean_up_tokenization_spaces=lowerCAmelCase__       )
					return lmap(str.strip					,lowerCAmelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Tuple:
					snake_case__      :List[Any]         =   self.tokenizer.pad_token_id
					snake_case__      :Optional[int]         =   batch["input_ids"], batch["attention_mask"]
					snake_case__      :Tuple         =   batch["labels"]
					if isinstance(self.model					,lowerCAmelCase__       ):
									snake_case__      :Optional[int]         =   self.model._shift_right(lowerCAmelCase__       )
					else:
									snake_case__      :Optional[int]         =   shift_tokens_right(lowerCAmelCase__					,lowerCAmelCase__       )
					if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
									snake_case__      :List[str]         =   decoder_input_ids
									self.save_readable_batch(lowerCAmelCase__       )

					snake_case__      :Any         =   self(lowerCAmelCase__					,attention_mask=lowerCAmelCase__					,decoder_input_ids=lowerCAmelCase__					,use_cache=lowerCAmelCase__       )
					snake_case__      :Any         =   outputs["logits"]
					if self.hparams.label_smoothing == 0:
									# Same behavior as modeling_bart.py, besides ignoring pad_token_id
									snake_case__      :List[str]         =   nn.CrossEntropyLoss(ignore_index=lowerCAmelCase__       )

									assert lm_logits.shape[-1] == self.vocab_size
									snake_case__      :Optional[int]         =   ce_loss_fct(lm_logits.view(-1					,lm_logits.shape[-1]       )					,tgt_ids.view(-1       )       )
					else:
									snake_case__      :Tuple         =   nn.functional.log_softmax(lowerCAmelCase__					,dim=-1       )
									snake_case__      :Any         =   label_smoothed_nll_loss(
									    lowerCAmelCase__					,lowerCAmelCase__					,self.hparams.label_smoothing					,ignore_index=lowerCAmelCase__       )
					return (loss,)






	@property
	def    lowerCAmelCase_							(		self       )						->    int:
					return self.tokenizer.pad_token_id






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Dict:
					snake_case__      :Optional[int]         =   self._step(lowerCAmelCase__       )

					snake_case__      :Dict         =   dict(zip(self.loss_names					,lowerCAmelCase__       )       )
					# tokens per batch
					snake_case__      :List[Any]         =   batch["input_ids"].ne(self.pad       ).sum() + batch["labels"].ne(self.pad       ).sum()
					snake_case__      :List[Any]         =   batch["input_ids"].shape[0]
					snake_case__      :Any         =   batch["input_ids"].eq(self.pad       ).sum()
					snake_case__      :str         =   batch["input_ids"].eq(self.pad       ).float().mean()
					# TODO(SS): make a wandb summary metric for this
					return {"loss": loss_tensors[0], "log": logs}






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Dict:
					return self._generative_step(lowerCAmelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase="val"       )						->    Dict:
					self.step_count += 1
					snake_case__      :List[str]         =   {k: torch.stack([x[k] for x in outputs]       ).mean() for k in self.loss_names}
					snake_case__      :int         =   losses["loss"]
					snake_case__      :Any         =   {
					    k: np.array([x[k] for x in outputs]       ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
					}
					snake_case__      :Optional[Any]         =   (
					    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
					)
					snake_case__      :torch.FloatTensor         =   torch.tensor(lowerCAmelCase__       ).type_as(lowerCAmelCase__       )
					generative_metrics.update({k: v.item() for k, v in losses.items()}       )
					losses.update(lowerCAmelCase__       )
					snake_case__      :Optional[int]         =   {f'{prefix}_avg_{k}': x for k, x in losses.items()}
					snake_case__      :List[Any]         =   self.step_count
					self.metrics[prefix].append(lowerCAmelCase__       )  # callback writes this to self.metrics_save_path
					snake_case__      :Optional[int]         =   flatten_list([x["preds"] for x in outputs]       )
					return {
					    "log": all_metrics,
					    "preds": preds,
					    f'{prefix}_loss': loss,
					    f'{prefix}_{self.val_metric}': metric_tensor,
					}






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Dict:
					return calculate_rouge(lowerCAmelCase__					,lowerCAmelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    dict:
					snake_case__      :Any         =   time.time()

					# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
					snake_case__      :Optional[int]         =   self.model.generate(
					    batch["input_ids"]					,attention_mask=batch["attention_mask"]					,use_cache=lowerCAmelCase__					,decoder_start_token_id=self.decoder_start_token_id					,num_beams=self.eval_beams					,max_length=self.eval_max_length					,)
					snake_case__      :str         =   (time.time() - ta) / batch["input_ids"].shape[0]
					snake_case__      :List[str]         =   self.ids_to_clean_text(lowerCAmelCase__       )
					snake_case__      :List[str]         =   self.ids_to_clean_text(batch["labels"]       )
					snake_case__      :int         =   self._step(lowerCAmelCase__       )
					snake_case__      :int         =   dict(zip(self.loss_names					,lowerCAmelCase__       )       )
					snake_case__      :Dict         =   self.calc_generative_metrics(lowerCAmelCase__					,lowerCAmelCase__       )
					snake_case__      :Optional[int]         =   np.mean(lmap(lowerCAmelCase__					,lowerCAmelCase__       )       )
					base_metrics.update(gen_time=lowerCAmelCase__					,gen_len=lowerCAmelCase__					,preds=lowerCAmelCase__					,target=lowerCAmelCase__					,**lowerCAmelCase__       )
					return base_metrics






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    List[Any]:
					return self._generative_step(lowerCAmelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Optional[Any]:
					return self.validation_epoch_end(lowerCAmelCase__					,prefix="test"       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    SeqaSeqDataset:
					snake_case__      :Union[str, Any]         =   self.n_obs[type_path]
					snake_case__      :List[str]         =   self.target_lens[type_path]
					snake_case__      :Any         =   self.dataset_class(
					    self.tokenizer					,type_path=lowerCAmelCase__					,n_obs=lowerCAmelCase__					,max_target_length=lowerCAmelCase__					,**self.dataset_kwargs					,)
					return dataset






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False       )						->    DataLoader:
					snake_case__      :Tuple         =   self.get_dataset(lowerCAmelCase__       )

					if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
									snake_case__      :int         =   dataset.make_sortish_sampler(lowerCAmelCase__					,distributed=self.hparams.gpus > 1       )
									return DataLoader(
									    lowerCAmelCase__					,batch_size=lowerCAmelCase__					,collate_fn=dataset.collate_fn					,shuffle=lowerCAmelCase__					,num_workers=self.num_workers					,sampler=lowerCAmelCase__					,)

					elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
									snake_case__      :Union[str, Any]         =   dataset.make_dynamic_sampler(
									    self.hparams.max_tokens_per_batch					,distributed=self.hparams.gpus > 1       )
									return DataLoader(
									    lowerCAmelCase__					,batch_sampler=lowerCAmelCase__					,collate_fn=dataset.collate_fn					,num_workers=self.num_workers					,)
					else:
									return DataLoader(
									    lowerCAmelCase__					,batch_size=lowerCAmelCase__					,collate_fn=dataset.collate_fn					,shuffle=lowerCAmelCase__					,num_workers=self.num_workers					,sampler=lowerCAmelCase__					,)






	def    lowerCAmelCase_							(		self       )						->    DataLoader:
					snake_case__      :List[str]         =   self.get_dataloader("train"					,batch_size=self.hparams.train_batch_size					,shuffle=lowerCAmelCase__       )
					return dataloader






	def    lowerCAmelCase_							(		self       )						->    DataLoader:
					return self.get_dataloader("val"					,batch_size=self.hparams.eval_batch_size       )






	def    lowerCAmelCase_							(		self       )						->    DataLoader:
					return self.get_dataloader("test"					,batch_size=self.hparams.eval_batch_size       )






	@staticmethod
	def    lowerCAmelCase_							(		UpperCamelCase					,UpperCamelCase       )						->    Optional[int]:
					BaseTransformer.add_model_specific_args(lowerCAmelCase__					,lowerCAmelCase__       )
					add_generic_args(lowerCAmelCase__					,lowerCAmelCase__       )
					parser.add_argument(
					    "--max_source_length"					,default=1_024					,type=lowerCAmelCase__					,help=(
					        "The maximum total input sequence length after tokenization. Sequences longer "
					        "than this will be truncated, sequences shorter will be padded."
					    )					,)
					parser.add_argument(
					    "--max_target_length"					,default=56					,type=lowerCAmelCase__					,help=(
					        "The maximum total input sequence length after tokenization. Sequences longer "
					        "than this will be truncated, sequences shorter will be padded."
					    )					,)
					parser.add_argument(
					    "--val_max_target_length"					,default=142					,type=lowerCAmelCase__					,help=(
					        "The maximum total input sequence length after tokenization. Sequences longer "
					        "than this will be truncated, sequences shorter will be padded."
					    )					,)
					parser.add_argument(
					    "--test_max_target_length"					,default=142					,type=lowerCAmelCase__					,help=(
					        "The maximum total input sequence length after tokenization. Sequences longer "
					        "than this will be truncated, sequences shorter will be padded."
					    )					,)
					parser.add_argument("--freeze_encoder"					,action="store_true"       )
					parser.add_argument("--freeze_embeds"					,action="store_true"       )
					parser.add_argument("--sortish_sampler"					,action="store_true"					,default=lowerCAmelCase__       )
					parser.add_argument("--overwrite_output_dir"					,action="store_true"					,default=lowerCAmelCase__       )
					parser.add_argument("--max_tokens_per_batch"					,type=lowerCAmelCase__					,default=lowerCAmelCase__       )
					parser.add_argument("--logger_name"					,type=lowerCAmelCase__					,choices=["default", "wandb", "wandb_shared"]					,default="default"       )
					parser.add_argument("--n_train"					,type=lowerCAmelCase__					,default=-1					,required=lowerCAmelCase__					,help="# examples. -1 means use all."       )
					parser.add_argument("--n_val"					,type=lowerCAmelCase__					,default=500					,required=lowerCAmelCase__					,help="# examples. -1 means use all."       )
					parser.add_argument("--n_test"					,type=lowerCAmelCase__					,default=-1					,required=lowerCAmelCase__					,help="# examples. -1 means use all."       )
					parser.add_argument(
					    "--task"					,type=lowerCAmelCase__					,default="summarization"					,required=lowerCAmelCase__					,help="# examples. -1 means use all."       )
					parser.add_argument("--label_smoothing"					,type=lowerCAmelCase__					,default=0.0					,required=lowerCAmelCase__       )
					parser.add_argument("--src_lang"					,type=lowerCAmelCase__					,default=""					,required=lowerCAmelCase__       )
					parser.add_argument("--tgt_lang"					,type=lowerCAmelCase__					,default=""					,required=lowerCAmelCase__       )
					parser.add_argument("--eval_beams"					,type=lowerCAmelCase__					,default=lowerCAmelCase__					,required=lowerCAmelCase__       )
					parser.add_argument(
					    "--val_metric"					,type=lowerCAmelCase__					,default=lowerCAmelCase__					,required=lowerCAmelCase__					,choices=["bleu", "rouge2", "loss", None]       )
					parser.add_argument("--eval_max_gen_length"					,type=lowerCAmelCase__					,default=lowerCAmelCase__					,help="never generate more than n tokens"       )
					parser.add_argument("--save_top_k"					,type=lowerCAmelCase__					,default=1					,required=lowerCAmelCase__					,help="How many checkpoints to save"       )
					parser.add_argument(
					    "--early_stopping_patience"					,type=lowerCAmelCase__					,default=-1					,required=lowerCAmelCase__					,help=(
					        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
					        " val_check_interval will effect it."
					    )					,)
					return parser




class 					_snake_case						(			a__ ):
	_A          =   """translation"""
	_A          =   ["""loss"""]
	_A          =   ["""bleu"""]
	_A          =   """bleu"""






	def __init__(		self					,UpperCamelCase					,**UpperCamelCase       )						->    Optional[Any]:
					super().__init__(lowerCAmelCase__					,**lowerCAmelCase__       )
					snake_case__      :Optional[Any]         =   hparams.src_lang
					snake_case__      :Union[str, Any]         =   hparams.tgt_lang






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    dict:
					return calculate_bleu(lowerCAmelCase__					,lowerCAmelCase__       )





def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							Tuple=None      )     -> SummarizationModule:





				'''simple docstring'''


				Path(args.output_dir      ).mkdir(exist_ok=_lowercase      )
				check_output_dir(_lowercase	,	expected_items=3      )

				if model is None:
								if "summarization" in args.task:
												snake_case__      :SummarizationModule         =   SummarizationModule(_lowercase      )
								else:
												snake_case__      :SummarizationModule         =   TranslationModule(_lowercase      )
				snake_case__      :int         =   Path(args.data_dir      ).name
				if (
				    args.logger_name == "default"
				    or args.fast_dev_run
				    or str(args.output_dir      ).startswith("/tmp"      )
				    or str(args.output_dir      ).startswith("/var"      )
				):
								snake_case__      :Tuple         =   True  # don't pollute wandb logs unnecessarily
				elif args.logger_name == "wandb":
								from pytorch_lightning.loggers import WandbLogger

								snake_case__      :Optional[int]         =   os.environ.get("WANDB_PROJECT"	,	_lowercase      )
								snake_case__      :Dict         =   WandbLogger(name=model.output_dir.name	,	project=_lowercase      )

				elif args.logger_name == "wandb_shared":
								from pytorch_lightning.loggers import WandbLogger

								snake_case__      :str         =   WandbLogger(name=model.output_dir.name	,	project=F'hf_{dataset}'      )

				if args.early_stopping_patience >= 0:
								snake_case__      :Tuple         =   get_early_stopping_callback(model.val_metric	,	args.early_stopping_patience      )
				else:
								snake_case__      :Optional[int]         =   False

				snake_case__      :Optional[Any]         =   args.val_metric == "loss"
				snake_case__      :pl.Trainer         =   generic_train(
				    _lowercase	,	_lowercase	,	logging_callback=SeqaSeqLoggingCallback()	,	checkpoint_callback=get_checkpoint_callback(
				        args.output_dir	,	model.val_metric	,	args.save_top_k	,	_lowercase      )	,	early_stopping_callback=_lowercase	,	logger=_lowercase	,	)
				pickle_save(model.hparams	,	model.output_dir / "hparams.pkl"      )
				if not args.do_predict:
								return model

				snake_case__      :Optional[Any]         =   ""
				snake_case__      :Dict         =   sorted(glob.glob(os.path.join(args.output_dir	,	"*.ckpt"      )	,	recursive=_lowercase      )      )
				if checkpoints:
								snake_case__      :Optional[int]         =   checkpoints[-1]
								snake_case__      :int         =   checkpoints[-1]
				trainer.logger.log_hyperparams(model.hparams      )

				# test() without a model tests using the best checkpoint automatically
				trainer.test()
				return model


if __name__ == "__main__":
	__UpperCAmelCase							:   List[str]							=  argparse.ArgumentParser()
	__UpperCAmelCase							:   Optional[int]							=  pl.Trainer.add_argparse_args(parser)
	__UpperCAmelCase							:   int							=  SummarizationModule.add_model_specific_args(parser, os.getcwd())

	__UpperCAmelCase							:   Union[str, Any]							=  parser.parse_args()

	main(args)







import os
import sys
import unittest


__UpperCAmelCase							:   str							=  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_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase							:   Tuple							=  os.path.join(git_repo_path, "src", "diffusers")


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   find_backend("    if not is_torch_available():"       )
					self.assertEqual(UpperCamelCase					,"torch"       )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					snake_case__      :Tuple         =   find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers"       )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					snake_case__      :str         =   find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers_and_onnx"       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn("torch"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers"					,UpperCamelCase       )
					self.assertIn("flax_and_transformers"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers_and_onnx"					,UpperCamelCase       )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn("UNet2DModel"					,objects["torch"]       )
					self.assertIn("FlaxUNet2DConditionModel"					,objects["flax"]       )
					self.assertIn("StableDiffusionPipeline"					,objects["torch_and_transformers"]       )
					self.assertIn("FlaxStableDiffusionPipeline"					,objects["flax_and_transformers"]       )
					self.assertIn("LMSDiscreteScheduler"					,objects["torch_and_scipy"]       )
					self.assertIn("OnnxStableDiffusionPipeline"					,objects["torch_and_transformers_and_onnx"]       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Union[str, Any]         =   create_dummy_object("CONSTANT"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,"\nCONSTANT = None\n"       )

					snake_case__      :Optional[Any]         =   create_dummy_object("function"					,"'torch'"       )
					self.assertEqual(
					    UpperCamelCase					,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					snake_case__      :str         =   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
					snake_case__      :List[str]         =   create_dummy_object("FakeClass"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
					snake_case__      :int         =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
					self.assertEqual(dummy_files["torch"]					,UpperCamelCase       )