






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase      :      Union[str, Any]     						=						logging.getLogger()

__UpperCamelCase      :      List[str]     						=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Dict			)		-> Dict:
		"""simple docstring"""







		os.makedirs(_lowerCAmelCase       ,				exist_ok=_lowerCAmelCase			)
		__lowercase             =  {"""source""": """What is love ?""", """target""": """life"""}
		__lowercase             =  {"""train""": 12, """val""": 2, """test""": 2}
		for split in ["train", "test", "val"]:
			for field in ["source", "target"]:
				__lowercase             =  """\n""".join([contents[field]] * n_lines[split]			)
				with open(os.path.join(_lowerCAmelCase       ,				F'{split}.{field}'			)       ,				"""w"""			) as f:
					f.write(_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							str = "pytorch"			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.get_auto_remove_tmp_dir()
		__lowercase             =  os.path.join(_lowerCAmelCase       ,				"""output"""			)
		__lowercase             =  os.path.join(_lowerCAmelCase       ,				"""data"""			)
		self._create_dummy_data(data_dir=_lowerCAmelCase			)

		__lowercase             =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

		if gpus > 0:
			testargs.append(F'--gpus={gpus}'			)
			if is_apex_available():
				testargs.append("""--fp16"""			)
		else:
			testargs.append("""--gpus=0"""			)
			testargs.append("""--distributed_backend=ddp_cpu"""			)
			testargs.append("""--num_processes=2"""			)

		__lowercase             =  [sys.executable, str(Path(finetune_rag.__file__			).resolve()			)] + testargs
		execute_subprocess_async(_lowerCAmelCase       ,				env=self.get_env()			)

		__lowercase             =  os.path.join(_lowerCAmelCase       ,				"""metrics.json"""			)
		with open(_lowerCAmelCase			) as f:
			__lowercase             =  json.load(_lowerCAmelCase			)
		return result







	@require_torch_gpu
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self._run_finetune(gpus=1			)
		self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,				0.2			)







	@require_torch_multi_gpu
	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self._run_finetune(gpus=2			)
		self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,				0.2			)







	@require_torch_gpu
	@require_ray
	def  _a   (  self      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  self._run_finetune(gpus=1       ,				distributed_retriever="""ray"""			)
		self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,				0.2			)







	@require_torch_multi_gpu
	@require_ray
	def  _a   (  self      :							Optional[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self._run_finetune(gpus=1       ,				distributed_retriever="""ray"""			)
		self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,				0.2			)









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

