




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(DDIMParallelScheduler,)
      UpperCAmelCase_        =		(("""eta""", 0.0), ("""num_inference_steps""", 50))
      def 	UpperCAmelCase_						(			self   :str ,      **lowerCamelCase   :Optional[int]      )     ->			Tuple:
       UpperCAmelCase__							 =						{
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      **lowerCamelCase   :Optional[int]      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						10, 0.0

       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter

       scheduler.set_timesteps(lowerCamelCase      )

       for t in scheduler.timesteps:
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       return sample
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[Any]:
       for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowerCamelCase      )

       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(steps_offset=1      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
       scheduler.set_timesteps(5      )
       assert torch.equal(scheduler.timesteps ,      torch.LongTensor([801, 601, 401, 201, 1]      )      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[Any]:
       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] ,      [0.0_02, 0.02, 0.2, 2]      ):
        self.check_over_configs(beta_start=lowerCamelCase ,      beta_end=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
       for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Union[str, Any]:
       for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       self.check_over_configs(thresholding=lowerCamelCase      )
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=lowerCamelCase ,      prediction_type=lowerCamelCase ,      sample_max_value=lowerCamelCase ,      )
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[Any]:
       for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       for t, num_inference_steps in zip([1, 10, 50] ,      [10, 50, 500]      ):
        self.check_over_forward(time_step=lowerCamelCase ,      num_inference_steps=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       for t, eta in zip([1, 10, 49] ,      [0.0, 0.5, 1.0]      ):
        self.check_over_forward(time_step=lowerCamelCase ,      eta=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       assert torch.sum(torch.abs(scheduler._get_variance(0 ,      0      ) - 0.0      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420 ,      400      ) - 0.1_47_71      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980 ,      960      ) - 0.3_24_60      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0 ,      0      ) - 0.0      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487 ,      486      ) - 0.0_09_79      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999 ,      998      ) - 0.02      )      ) < 1e-5
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config()
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						10, 0.0
       scheduler.set_timesteps(lowerCamelCase      )

       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       UpperCAmelCase__							 =						self.dummy_sample_deter + 0.1
       UpperCAmelCase__							 =						self.dummy_sample_deter - 0.1

       UpperCAmelCase__							 =						samplea.shape[0]
       UpperCAmelCase__							 =						torch.stack([samplea, samplea, samplea] ,      dim=0      )
       UpperCAmelCase__							 =						torch.arange(lowerCamelCase      )[0:3, None].repeat(1 ,      lowerCamelCase      )

       UpperCAmelCase__							 =						model(samples.flatten(0 ,      1      ) ,      timesteps.flatten(0 ,      1      )      )
       UpperCAmelCase__							 =						scheduler.batch_step_no_noise(lowerCamelCase ,      timesteps.flatten(0 ,      1      ) ,      samples.flatten(0 ,      1      ) ,      lowerCamelCase      )

       UpperCAmelCase__							 =						torch.sum(torch.abs(lowerCamelCase      )      )
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_sum.item() - 11_47.79_04      ) < 1e-2
       assert abs(result_mean.item() - 0.49_82      ) < 1e-3
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       UpperCAmelCase__							 =						self.full_loop()

       UpperCAmelCase__							 =						torch.sum(torch.abs(lowerCamelCase      )      )
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_sum.item() - 1_72.00_67      ) < 1e-2
       assert abs(result_mean.item() - 0.22_39_67      ) < 1e-3
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			int:
       UpperCAmelCase__							 =						self.full_loop(prediction_type="v_prediction"      )

       UpperCAmelCase__							 =						torch.sum(torch.abs(lowerCamelCase      )      )
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_sum.item() - 52.53_02      ) < 1e-2
       assert abs(result_mean.item() - 0.06_84      ) < 1e-3
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Any:
       # We specify different beta, so that the first alpha is 0.99
       UpperCAmelCase__							 =						self.full_loop(set_alpha_to_one=lowerCamelCase ,      beta_start=0.01      )
       UpperCAmelCase__							 =						torch.sum(torch.abs(lowerCamelCase      )      )
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_sum.item() - 1_49.82_95      ) < 1e-2
       assert abs(result_mean.item() - 0.19_51      ) < 1e-3







      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       # We specify different beta, so that the first alpha is 0.99
       UpperCAmelCase__							 =						self.full_loop(set_alpha_to_one=lowerCamelCase ,      beta_start=0.01      )
       UpperCAmelCase__							 =						torch.sum(torch.abs(lowerCamelCase      )      )
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_sum.item() - 1_49.07_84      ) < 1e-2
       assert abs(result_mean.item() - 0.19_41      ) < 1e-3











# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

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

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
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-de





