


"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class   _UpperCamelCase  (   A_		):
							'''simple docstring'''







							__UpperCAmelCase						:    Tuple				      =(UniPCMultistepScheduler,)
							__UpperCAmelCase						:    Dict				      =(("""num_inference_steps""", 2_5),)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "solver_type": "bh2",
										}

										config.update(**_lowerCamelCase	)
										return config

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   _lowerCamelCase	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

													__lowerCAmelCase    ,	__lowerCAmelCase				    =			sample, sample
													for t in range(_lowerCamelCase    ,   time_step + scheduler.config.solver_order + 1	):
																__lowerCAmelCase				    =			scheduler.step(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   **_lowerCamelCase	).prev_sample
																__lowerCAmelCase				    =			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 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   _lowerCamelCase	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)
													scheduler.set_timesteps(_lowerCamelCase	)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_lowerCamelCase	)
																__lowerCAmelCase				    =			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)
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.step(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   **_lowerCamelCase	).prev_sample
													__lowerCAmelCase				    =			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 			snake_case				(     self    ,   __a=None    ,   **__a	):
										if scheduler is None:
													__lowerCAmelCase				    =			self.scheduler_classes[0]
													__lowerCAmelCase				    =			self.get_scheduler_config(**_lowerCamelCase	)
													__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)

										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(**_lowerCamelCase	)
										__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter
										scheduler.set_timesteps(_lowerCamelCase	)

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

										return sample

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)

										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   _lowerCamelCase	)

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)

													__lowerCAmelCase				    =			self.dummy_sample
													__lowerCAmelCase				    =			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"	):
																__lowerCAmelCase				    =			num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

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

													__lowerCAmelCase				    =			scheduler.step(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   **_lowerCamelCase	).prev_sample
													__lowerCAmelCase				    =			scheduler.step(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   **_lowerCamelCase	).prev_sample

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

							def 			snake_case				(     self	):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowerCAmelCase				    =			UniPCMultistepScheduler(**self.get_scheduler_config()	)
										__lowerCAmelCase				    =			self.full_loop(scheduler=_lowerCamelCase	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(_lowerCamelCase	)	)

										assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3

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

										__lowerCAmelCase				    =			self.full_loop(scheduler=_lowerCamelCase	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(_lowerCamelCase	)	)

										assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3

							def 			snake_case				(     self	):
										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=_lowerCamelCase	)

							def 			snake_case				(     self	):
										self.check_over_configs(thresholding=_lowerCamelCase	)
										for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																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    ,   solver_order=_lowerCamelCase    ,   solver_type=_lowerCamelCase    ,   )

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

							def 			snake_case				(     self	):
										for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																for prediction_type in ["epsilon", "sample"]:
																			self.check_over_configs(
																			    solver_order=_lowerCamelCase    ,   solver_type=_lowerCamelCase    ,   prediction_type=_lowerCamelCase    ,   )
																			__lowerCAmelCase				    =			self.full_loop(
																			    solver_order=_lowerCamelCase    ,   solver_type=_lowerCamelCase    ,   prediction_type=_lowerCamelCase    ,   )
																			assert not torch.isnan(_lowerCamelCase	).any(), "Samples have nan numbers"

							def 			snake_case				(     self	):
										self.check_over_configs(lower_order_final=_lowerCamelCase	)
										self.check_over_configs(lower_order_final=_lowerCamelCase	)

							def 			snake_case				(     self	):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=_lowerCamelCase    ,   time_step=0	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop()
										__lowerCAmelCase				    =			torch.mean(torch.abs(_lowerCamelCase	)	)

										assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(_lowerCamelCase	)	)

										assert abs(result_mean.item() - 0.1_0_1_4	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(thresholding=_lowerCamelCase    ,   dynamic_thresholding_ratio=0	)
										__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter.half()
										scheduler.set_timesteps(_lowerCamelCase	)

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

										assert sample.dtype == torch.floataa



							def 			snake_case				(     self    ,   **__a	):
										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config(**_lowerCamelCase	)
													__lowerCAmelCase				    =			scheduler_class(**_lowerCamelCase	)

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






"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



