
'''simple docstring'''



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A          =    logging.getLogger(__name__)





@dataclass
class 			A							:
			lowerCamelCase							:							str       		=  field(
			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
			lowerCamelCase							:							Optional[str]       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
			lowerCamelCase							:							Optional[str]       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
			lowerCamelCase							:							Optional[str]       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,						)





@dataclass
class 			A							:
			lowerCamelCase							:							str       		=  field(
			    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()				)}				)
			lowerCamelCase							:							str       		=  field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCamelCase							:							int       		=  field(
			    default=128		,						metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }		,						)
			lowerCamelCase							:							bool       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





def  _A		(							lowercase__	):
						lowercase__							 =       default_data_collator(lowercase__	)
						lowercase__							 =       batch.pop("""pairID"""		,		lowercase__	)
						return batch





def  _A		(							):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowercase__							 =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
						lowercase__ ,						lowercase__ ,						lowercase__							 =       parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir	)
						    and os.listdir(training_args.output_dir	)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    """ --overwrite_output_dir to overcome."""	)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,		datefmt="""%m/%d/%Y %H:%M:%S"""		,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,		)
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""		,		training_args.local_rank		,		training_args.device		,		training_args.n_gpu		,		bool(training_args.local_rank != -1	)		,		training_args.fpaa		,		)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank	):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("""Training/evaluation parameters %s"""		,		lowercase__	)

						# Set seed
						set_seed(training_args.seed	)

						try:
												lowercase__							 =       hans_tasks_num_labels[data_args.task_name]
						except KeyError:
												raise ValueError("""Task not found: %s""" % (data_args.task_name)	)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase__							 =       AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,		num_labels=lowercase__		,		finetuning_task=data_args.task_name		,		cache_dir=model_args.cache_dir		,		)
						lowercase__							 =       AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,		cache_dir=model_args.cache_dir		,		)
						lowercase__							 =       AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path		,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)		,		config=lowercase__		,		cache_dir=model_args.cache_dir		,		)

						# Get datasets
						lowercase__							 =       (
						    HansDataset(
						        data_dir=data_args.data_dir		,		tokenizer=lowercase__		,		task=data_args.task_name		,		max_seq_length=data_args.max_seq_length		,		overwrite_cache=data_args.overwrite_cache		,		)
						    if training_args.do_train
						    else None
						)
						lowercase__							 =       (
						    HansDataset(
						        data_dir=data_args.data_dir		,		tokenizer=lowercase__		,		task=data_args.task_name		,		max_seq_length=data_args.max_seq_length		,		overwrite_cache=data_args.overwrite_cache		,		evaluate=lowercase__		,		)
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						lowercase__							 =       Trainer(
						    model=lowercase__		,		args=lowercase__		,		train_dataset=lowercase__		,		eval_dataset=lowercase__		,		data_collator=lowercase__		,		)

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""	)

												lowercase__							 =       trainer.predict(lowercase__	)
												lowercase__							 =       output.predictions
												lowercase__							 =       np.argmax(lowercase__		,		axis=1	)

												lowercase__							 =       [ex.pairID for ex in eval_dataset]
												lowercase__							 =       os.path.join(training_args.output_dir		,		"""hans_predictions.txt"""	)
												lowercase__							 =       eval_dataset.get_labels()
												if trainer.is_world_master():
																		with open(lowercase__		,		"""w"""	) as writer:
																								writer.write("""pairID,gold_label\n"""	)
																								for pid, pred in zip(lowercase__		,		lowercase__	):
																														writer.write("""ex""" + str(lowercase__	) + """,""" + label_list[int(lowercase__	)] + """\n"""	)

												trainer._log(output.metrics	)





def  _A		(							lowercase__	):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
				main()








'''simple docstring'''



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__A          =    logging.get_logger(__name__)





def  _A		(							lowercase__		,		lowercase__ = True	):
						lowercase__							 =       {}
						for name in model.attn_processors.keys():
												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
												)
												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""	):
																		lowercase__							 =       model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""	):
																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
												elif name.startswith("""down_blocks"""	):
																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
																		lowercase__							 =       model.config.block_out_channels[block_id]
												elif name.startswith("""transformer_in"""	):
																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																		lowercase__							 =       8 * model.config.attention_head_dim

												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
												lowercase__							 =       lora_attn_procs[name].to(model.device	)

												if mock_weights:
																		# add 1 to weights to mock trained weights
																		with torch.no_grad():
																								lora_attn_procs[name].to_q_lora.up.weight += 1
																								lora_attn_procs[name].to_k_lora.up.weight += 1
																								lora_attn_procs[name].to_v_lora.up.weight += 1
																								lora_attn_procs[name].to_out_lora.up.weight += 1

						return lora_attn_procs







@skip_mps
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
			lowerCamelCase							:							int       		=  """sample"""



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




									'''simple docstring'''



									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       (32, 32)

									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )

									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



			@property
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									return (4, 4, 32, 32)



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return (4, 4, 32, 32)



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       {
									    """block_out_channels""": (32, 64),
									    """down_block_types""": (
									        """CrossAttnDownBlock3D""",
									        """DownBlock3D""",
									    ),
									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
									    """cross_attention_dim""": 32,
									    """attention_head_dim""": 8,
									    """out_channels""": 4,
									    """in_channels""": 4,
									    """layers_per_block""": 1,
									    """sample_size""": 32,
									}
									lowercase__							 =       self.dummy_input
									return init_dict, inputs_dict



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )

									model.enable_xformers_memory_efficient_attention()

									assert (
									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
									    == "XFormersAttnProcessor"
									), "xformers is not enabled"



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       output.sample

									self.assertIsNotNone(lowerCamelCase__       )
									lowercase__							 =       inputs_dict["""sample"""].shape
									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															# Warmup pass when using mps (see #372)
															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					model(**self.dummy_input       )

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       first.sample

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       second.sample

									lowercase__							 =       first.cpu().numpy()
									lowercase__							 =       second.cpu().numpy()
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									model.set_attention_slice("""auto"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice("""max"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice(2       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )

									# make sure we can set a list of attention processors
									model.set_attn_processor(lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (samplea - samplea).abs().max() < 3e-3
									assert (samplea - samplea).abs().max() < 3e-3

									# sample 2 and sample 3 should be different
									assert (samplea - samplea).abs().max() > 3e-3



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 1e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 3e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															with self.assertRaises(lowerCamelCase__       ) as e:
																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample

									model.set_attn_processor(AttnProcessor()       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - new_sample).abs().max() < 1e-4
									assert (sample - old_sample).abs().max() < 3e-3



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       4

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									# default
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - on_sample).abs().max() < 1e-4
									assert (sample - off_sample).abs().max() < 1e-4



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






