





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class   _lowercase						(							UpperCamelCase_				):
   _UpperCamelCase       		=   (DDPMScheduler,)


   def 					snake_case				( self    ,   **_UpperCAmelCase					):
       A       :       int							=						{
           """num_train_timesteps""": 1_000,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """variance_type""": """fixed_small""",
           """clip_sample""": True,
       }

       config.update(**_a					)
       return config


   def 					snake_case				( self					):


       for timesteps in [1, 5, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=_a					)


   def 					snake_case				( self					):


       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,   [0.002, 0.02, 0.2, 2]					):
           self.check_over_configs(beta_start=_a    ,   beta_end=_a					)


   def 					snake_case				( self					):


       for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_a					)


   def 					snake_case				( self					):


       for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_a					)


   def 					snake_case				( self					):


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


   def 					snake_case				( self					):
       self.check_over_configs(thresholding=_a					)
       for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=_a    ,   prediction_type=_a    ,   sample_max_value=_a    ,   )


   def 					snake_case				( self					):


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


   def 					snake_case				( self					):


       for t in [0, 500, 999]:
           self.check_over_forward(time_step=_a					)


   def 					snake_case				( self					):
       A       :       Tuple							=						self.scheduler_classes[0]
       A       :       int							=						self.get_scheduler_config()
       A       :       Dict							=						scheduler_class(**_a					)

       assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00979					)					) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1E-5


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.scheduler_classes[0]
       A       :       Union[str, Any]							=						self.get_scheduler_config()
       A       :       int							=						scheduler_class(**_a					)

       A       :       Any							=						len(_a					)

       A       :       List[Any]							=						self.dummy_model()
       A       :       str							=						self.dummy_sample_deter
       A       :       str							=						torch.manual_seed(0					)

       for t in reversed(range(_a					)					):
           # 1. predict noise residual
           A       :       Optional[Any]							=						model(_a    ,   _a					)

           # 2. predict previous mean of sample x_t-1
           A       :       int							=						scheduler.step(_a    ,   _a    ,   _a    ,   generator=_a					).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           A       :       str							=						pred_prev_sample

       A       :       str							=						torch.sum(torch.abs(_a					)					)
       A       :       Any							=						torch.mean(torch.abs(_a					)					)

       assert abs(result_sum.item() - 258.9606					) < 1E-2
       assert abs(result_mean.item() - 0.3372					) < 1E-3


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.scheduler_classes[0]
       A       :       Tuple							=						self.get_scheduler_config(prediction_type='''v_prediction'''					)
       A       :       List[str]							=						scheduler_class(**_a					)

       A       :       Dict							=						len(_a					)

       A       :       List[str]							=						self.dummy_model()
       A       :       List[str]							=						self.dummy_sample_deter
       A       :       Optional[Any]							=						torch.manual_seed(0					)

       for t in reversed(range(_a					)					):
           # 1. predict noise residual
           A       :       int							=						model(_a    ,   _a					)

           # 2. predict previous mean of sample x_t-1
           A       :       List[str]							=						scheduler.step(_a    ,   _a    ,   _a    ,   generator=_a					).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           A       :       Tuple							=						pred_prev_sample

       A       :       Any							=						torch.sum(torch.abs(_a					)					)
       A       :       int							=						torch.mean(torch.abs(_a					)					)

       assert abs(result_sum.item() - 202.0296					) < 1E-2
       assert abs(result_mean.item() - 0.2631					) < 1E-3


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.scheduler_classes[0]
       A       :       Union[str, Any]							=						self.get_scheduler_config()
       A       :       Dict							=						scheduler_class(**_a					)

       A       :       Optional[Any]							=						[100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=_a					)

       A       :       Union[str, Any]							=						scheduler.timesteps

       for i, timestep in enumerate(_a					):
           if i == len(_a					) - 1:
               A       :       Optional[Any]							=						-1
           else:
               A       :       Tuple							=						timesteps[i + 1]

           A       :       int							=						scheduler.previous_timestep(_a					)
           A       :       Optional[Any]							=						prev_t.item()

           self.assertEqual(_a    ,   _a					)


   def 					snake_case				( self					):
       A       :       Tuple							=						self.scheduler_classes[0]
       A       :       int							=						self.get_scheduler_config()
       A       :       List[str]							=						scheduler_class(**_a					)

       A       :       Optional[int]							=						[100, 87, 50, 51, 0]

       with self.assertRaises(_a    ,   msg='''`custom_timesteps` must be in descending order.'''					):
           scheduler.set_timesteps(timesteps=_a					)


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						self.scheduler_classes[0]
       A       :       List[Any]							=						self.get_scheduler_config()
       A       :       List[str]							=						scheduler_class(**_a					)

       A       :       int							=						[100, 87, 50, 1, 0]
       A       :       List[str]							=						len(_a					)

       with self.assertRaises(_a    ,   msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''					):
           scheduler.set_timesteps(num_inference_steps=_a    ,   timesteps=_a					)


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.scheduler_classes[0]
       A       :       Union[str, Any]							=						self.get_scheduler_config()
       A       :       Union[str, Any]							=						scheduler_class(**_a					)

       A       :       Optional[int]							=						[scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a    ,   msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,   ):
           scheduler.set_timesteps(timesteps=_a					)







'''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

snake_case_     =	"""tiny-wmt19-en-ru"""

# Build

# borrowed from a test
snake_case_     =	[
    """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>""",
]
snake_case_     =	dict(zip(vocab, range(len(vocab))))
snake_case_     =	["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case_     =	Path(tmpdirname)
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     snake_case_     =	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))

     snake_case_     =	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,
     )

snake_case_     =	FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    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,
)

snake_case_     =	FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
snake_case_     =	tokenizer(["""Making tiny model"""], return_tensors="""pt""")
snake_case_     =	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
