





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(DPMSolverMultistepScheduler,)
	__UpperCamelCase        =		(('''num_inference_steps''', 25),)




	def       UpperCAmelCase (	self       :    Any				,			**__lowerCamelCase       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'num_train_timesteps': 1_000,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			    'solver_order': 2,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			    'sample_max_value': 1.0,
			    'algorithm_type': 'dpmsolver++',
			    'solver_type': 'midpoint',
			    'lower_order_final': False,
			    'lambda_min_clipped': -float('inf'      ),
			    'variance_type': None,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    Tuple=0				,			**__lowerCamelCase       :    Tuple      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    dict(self.forward_default_kwargs      )
			__lowercase					=    kwargs.pop('num_inference_steps'				,			__lowerCamelCase      )
			__lowercase					=    self.dummy_sample
			__lowercase					=    0.1 * sample
			__lowercase					=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					__lowercase					=    self.get_scheduler_config(**__lowerCamelCase      )
					__lowercase					=    scheduler_class(**__lowerCamelCase      )
					scheduler.set_timesteps(__lowerCamelCase      )
					# copy over dummy past residuals
					__lowercase					=    dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(__lowerCamelCase      )
							__lowercase					=    scheduler_class.from_pretrained(__lowerCamelCase      )
							new_scheduler.set_timesteps(__lowerCamelCase      )
							# copy over dummy past residuals
							__lowercase					=    dummy_past_residuals[: new_scheduler.config.solver_order]

					__lowercase	,			__lowercase					=    sample, sample
					for t in range(__lowerCamelCase				,			time_step + scheduler.config.solver_order + 1      ):
							__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample
							__lowercase					=    new_scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample

							assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"




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

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Any=0				,			**__lowerCamelCase       :    List[Any]      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    dict(self.forward_default_kwargs      )
			__lowercase					=    kwargs.pop('num_inference_steps'				,			__lowerCamelCase      )
			__lowercase					=    self.dummy_sample
			__lowercase					=    0.1 * sample
			__lowercase					=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					__lowercase					=    self.get_scheduler_config()
					__lowercase					=    scheduler_class(**__lowerCamelCase      )
					scheduler.set_timesteps(__lowerCamelCase      )

					# copy over dummy past residuals (must be after setting timesteps)
					__lowercase					=    dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(__lowerCamelCase      )
							__lowercase					=    scheduler_class.from_pretrained(__lowerCamelCase      )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(__lowerCamelCase      )

							# copy over dummy past residual (must be after setting timesteps)
							__lowercase					=    dummy_past_residuals[: new_scheduler.config.solver_order]

					__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample
					__lowercase					=    new_scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample

					assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Union[str, Any]=None				,			**__lowerCamelCase       :    int      )					->   List[Any]:

			'''simple docstring'''




			if scheduler is None:
					__lowercase					=    self.scheduler_classes[0]
					__lowercase					=    self.get_scheduler_config(**__lowerCamelCase      )
					__lowercase					=    scheduler_class(**__lowerCamelCase      )

			__lowercase					=    10
			__lowercase					=    self.dummy_model()
			__lowercase					=    self.dummy_sample_deter
			scheduler.set_timesteps(__lowerCamelCase      )

			for i, t in enumerate(scheduler.timesteps      ):
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      )
					__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      ).prev_sample

			return sample




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

			'''simple docstring'''




			__lowercase					=    dict(self.forward_default_kwargs      )

			__lowercase					=    kwargs.pop('num_inference_steps'				,			__lowerCamelCase      )

			for scheduler_class in self.scheduler_classes:
					__lowercase					=    self.get_scheduler_config()
					__lowercase					=    scheduler_class(**__lowerCamelCase      )

					__lowercase					=    self.dummy_sample
					__lowercase					=    0.1 * sample

					if num_inference_steps is not None and hasattr(__lowerCamelCase				,			'set_timesteps'      ):
							scheduler.set_timesteps(__lowerCamelCase      )
					elif num_inference_steps is not None and not hasattr(__lowerCamelCase				,			'set_timesteps'      ):
							__lowercase					=    num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					__lowercase					=    [residual + 0.2, residual + 0.15, residual + 0.10]
					__lowercase					=    dummy_past_residuals[: scheduler.config.solver_order]

					__lowercase					=    scheduler.timesteps[5]
					__lowercase					=    scheduler.timesteps[6]

					__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample
					__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      ).prev_sample

					self.assertEqual(output_a.shape				,			sample.shape      )
					self.assertEqual(output_a.shape				,			output_a.shape      )




	def       UpperCAmelCase (	self       :    Tuple      )					->   Dict:

			'''simple docstring'''




			for timesteps in [25, 50, 100, 999, 1_000]:
					self.check_over_configs(num_train_timesteps=__lowerCamelCase      )




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

			'''simple docstring'''




			self.check_over_configs(thresholding=__lowerCamelCase      )
			for order in [1, 2, 3]:
					for solver_type in ["midpoint", "heun"]:
							for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=__lowerCamelCase				,			prediction_type=__lowerCamelCase				,			sample_max_value=__lowerCamelCase				,			algorithm_type='dpmsolver++'				,			solver_order=__lowerCamelCase				,			solver_type=__lowerCamelCase				,			)




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

			'''simple docstring'''




			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
															continue
											else:
													self.check_over_configs(
													    solver_order=__lowerCamelCase				,			solver_type=__lowerCamelCase				,			prediction_type=__lowerCamelCase				,			algorithm_type=__lowerCamelCase				,			)
											__lowercase					=    self.full_loop(
											    solver_order=__lowerCamelCase				,			solver_type=__lowerCamelCase				,			prediction_type=__lowerCamelCase				,			algorithm_type=__lowerCamelCase				,			)
											assert not torch.isnan(__lowerCamelCase      ).any(), "Samples have nan numbers"




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

			'''simple docstring'''




			self.check_over_configs(lower_order_final=__lowerCamelCase      )
			self.check_over_configs(lower_order_final=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Tuple      )					->   Dict:

			'''simple docstring'''




			self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
			self.check_over_configs(lambda_min_clipped=-5.1      )




	def       UpperCAmelCase (	self       :    Any      )					->   Optional[int]:

			'''simple docstring'''




			self.check_over_configs(variance_type=__lowerCamelCase      )
			self.check_over_configs(variance_type='learned_range'      )




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

			'''simple docstring'''




			for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
					self.check_over_forward(num_inference_steps=__lowerCamelCase				,			time_step=0      )




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

			'''simple docstring'''




			__lowercase					=    self.full_loop()
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 0.3301      ) < 1E-3




	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    self.full_loop(thresholding=__lowerCamelCase				,			dynamic_thresholding_ratio=0.87				,			sample_max_value=0.5      )
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 1.1364      ) < 1E-3




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

			'''simple docstring'''




			__lowercase					=    self.full_loop(prediction_type='v_prediction'      )
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 0.2251      ) < 1E-3




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

			'''simple docstring'''




			__lowercase					=    self.full_loop(prediction_type='v_prediction'				,			use_karras_sigmas=__lowerCamelCase      )
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 0.2096      ) < 1E-3




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    DPMSolverMultistepScheduler(**self.get_scheduler_config()      )
			__lowercase					=    self.full_loop(scheduler=__lowerCamelCase      )
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 0.3301      ) < 1E-3

			__lowercase					=    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
			__lowercase					=    UniPCMultistepScheduler.from_config(scheduler.config      )
			__lowercase					=    DEISMultistepScheduler.from_config(scheduler.config      )
			__lowercase					=    DPMSolverMultistepScheduler.from_config(scheduler.config      )

			__lowercase					=    self.full_loop(scheduler=__lowerCamelCase      )
			__lowercase					=    torch.mean(torch.abs(__lowerCamelCase      )      )

			assert abs(result_mean.item() - 0.3301      ) < 1E-3




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

			'''simple docstring'''




			__lowercase					=    self.scheduler_classes[0]
			__lowercase					=    self.get_scheduler_config(thresholding=__lowerCamelCase				,			dynamic_thresholding_ratio=0      )
			__lowercase					=    scheduler_class(**__lowerCamelCase      )

			__lowercase					=    10
			__lowercase					=    self.dummy_model()
			__lowercase					=    self.dummy_sample_deter.half()
			scheduler.set_timesteps(__lowerCamelCase      )

			for i, t in enumerate(scheduler.timesteps      ):
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      )
					__lowercase					=    scheduler.step(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase      ).prev_sample

			assert sample.dtype == torch.floataa






	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    Optional[int]      )					->   List[str]:

			'''simple docstring'''




			for scheduler_class in self.scheduler_classes:
					__lowercase					=    self.get_scheduler_config(**__lowerCamelCase      )
					__lowercase					=    scheduler_class(**__lowerCamelCase      )

					scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
					assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps











# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    {
		    'en': 'Machine learning is great, isn\'t it?',
		    'ru': 'Машинное обучение - это здорово, не так ли?',
		    'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__lowercase					=    {
		    'wmt19-de-en-6-6-base': [0, 38.37],
		    'wmt19-de-en-6-6-big': [0, 39.90],
		}
		__lowercase					=    F"{src_lang}-{tgt_lang}"

		__lowercase					=    F"\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
		model_card_dir.mkdir(parents=snake_case	,      exist_ok=snake_case       )
		__lowercase					=    os.path.join(snake_case	,      'README.md'       )
		print(F"Generating {path}"       )
		with open(snake_case	,      'w'	,      encoding='utf-8'       ) as f:
				f.write(snake_case       )


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE_      :			Tuple		  =      Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_      :			Any		  =      repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		SCREAMING_SNAKE_CASE_      :			Any		  =      model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)




