




'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowerCAmelCase_     (    __UpperCAmelCase				):







  '''simple docstring'''

  UpperCamelCase_       :	str				       =							42
  UpperCamelCase_       :	Any				       =							42



  def __init__(      self							:				Tuple ,   SCREAMING_SNAKE_CASE_							:				str ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]     )       ->						List[Any]:







       '''simple docstring'''



       super().__init__()
       self.register_modules(unet=UpperCamelCase__ ,   scheduler=UpperCamelCase__     )




  @torch.no_grad()
  def __call__(      self							:				Optional[Any] ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any] = 1 ,   SCREAMING_SNAKE_CASE_							:				Any = 20_00 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any] = None ,   SCREAMING_SNAKE_CASE_							:				Any = "pil" ,   SCREAMING_SNAKE_CASE_							:				List[str] = True ,   **SCREAMING_SNAKE_CASE_							:				Dict ,   )       ->						Optional[int]:







       '''simple docstring'''



       A:						Optional[int]						=						self.unet.config.sample_size
       A:						List[Any]						=						(batch_size, 3, img_size, img_size)

       A:						Optional[int]						=						self.unet

       A:						Union[str, Any]						=						randn_tensor(UpperCamelCase__ ,   generator=UpperCamelCase__     ) * self.scheduler.init_noise_sigma
       A:						Any						=						sample.to(self.device     )

       self.scheduler.set_timesteps(UpperCamelCase__     )
       self.scheduler.set_sigmas(UpperCamelCase__     )

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
            A:						int						=						self.scheduler.sigmas[i] * torch.ones(shape[0] ,   device=self.device     )

            # correction step
            for _ in range(self.scheduler.config.correct_steps     ):
                 A:						Any						=						self.unet(UpperCamelCase__ ,   UpperCamelCase__     ).sample
                 A:						Optional[Any]						=						self.scheduler.step_correct(UpperCamelCase__ ,   UpperCamelCase__ ,   generator=UpperCamelCase__     ).prev_sample

            # prediction step
            A:						Dict						=						model(UpperCamelCase__ ,   UpperCamelCase__     ).sample
            A:						List[str]						=						self.scheduler.step_pred(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   generator=UpperCamelCase__     )

            A			,  A:						int						=						output.prev_sample, output.prev_sample_mean

       A:						Tuple						=						sample_mean.clamp(0 ,   1     )
       A:						Dict						=						sample.cpu().permute(0 ,   2 ,   3 ,   1     ).numpy()
       if output_type == "pil":
            A:						List[Any]						=						self.numpy_to_pil(UpperCamelCase__     )

       if not return_dict:
            return (sample,)

       return ImagePipelineOutput(images=UpperCamelCase__     )












# 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

_A     =			'''tiny-wmt19-en-ru'''

# Build

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

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

						_A     =			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,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			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




