





'''simple docstring'''






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=0					):
       A       :       Any							=						1.0 if scale is None else scale
       A       :       Union[str, Any]							=						0.0 if loc is None else loc

       super().__init__(_UpperCAmelCase    ,   [AffineTransform(loc=self.loc    ,   scale=self.scale    ,   event_dim=_UpperCAmelCase					)]					)


   @property
   def 					snake_case				( self					):
       return self.base_dist.mean * self.scale + self.loc


   @property
   def 					snake_case				( self					):
       return self.base_dist.variance * self.scale**2


   @property
   def 					snake_case				( self					):
       return self.variance.sqrt()







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   **_UpperCAmelCase					):
       super().__init__(**_UpperCAmelCase					)
       A       :       int							=						args_dim
       A       :       Dict							=						nn.ModuleList([nn.Linear(_UpperCAmelCase    ,   _UpperCAmelCase					) for dim in args_dim.values()]					)
       A       :       List[str]							=						domain_map


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						[proj(_UpperCAmelCase					) for proj in self.proj]

       return self.domain_map(*_UpperCAmelCase					)







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase					):
       super().__init__()
       A       :       List[str]							=						function


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   *_UpperCAmelCase					):
       return self.function(_UpperCAmelCase    ,   *_UpperCAmelCase					)







class   _lowercase						:
   _UpperCamelCase       		=   42
   _UpperCamelCase       		=   42
   _UpperCamelCase       		=   42


   def __init__( self    ,   _UpperCAmelCase = 1					):
       A       :       List[str]							=						dim
       A       :       Tuple							=						{k: dim * self.args_dim[k] for k in self.args_dim}


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if self.dim == 1:
           return self.distribution_class(*_UpperCAmelCase					)
       else:
           return Independent(self.distribution_class(*_UpperCAmelCase					)    ,   1					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   ):
       A       :       str							=						self._base_distribution(_UpperCAmelCase					)
       if loc is None and scale is None:
           return distr
       else:
           return AffineTransformed(_UpperCAmelCase    ,   loc=_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   event_dim=self.event_dim					)


   @property
   def 					snake_case				( self					):
       return () if self.dim == 1 else (self.dim,)


   @property
   def 					snake_case				( self					):
       return len(self.event_shape					)


   @property
   def 					snake_case				( self					):
       return 0.0


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       return ParameterProjection(
           in_features=_UpperCAmelCase    ,   args_dim=self.args_dim    ,   domain_map=LambdaLayer(self.domain_map					)    ,   )


   def 					snake_case				( self    ,   *_UpperCAmelCase					):
       raise NotImplementedError()


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       return (x + torch.sqrt(torch.square(_UpperCAmelCase					) + 4.0					)) / 2.0







class   _lowercase						(							a				):
   _UpperCamelCase       		=   {"df": 1, "loc": 1, "scale": 1}
   _UpperCamelCase       		=   StudentT


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						cls.squareplus(_UpperCAmelCase					).clamp_min(torch.finfo(scale.dtype					).eps					)
       A       :       str							=						2.0 + cls.squareplus(_UpperCAmelCase					)
       return df.squeeze(-1					), loc.squeeze(-1					), scale.squeeze(-1					)







class   _lowercase						(							a				):
   _UpperCamelCase       		=   {"loc": 1, "scale": 1}
   _UpperCamelCase       		=   Normal


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						cls.squareplus(_UpperCAmelCase					).clamp_min(torch.finfo(scale.dtype					).eps					)
       return loc.squeeze(-1					), scale.squeeze(-1					)







class   _lowercase						(							a				):
   _UpperCamelCase       		=   {"total_count": 1, "logits": 1}
   _UpperCamelCase       		=   NegativeBinomial


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Tuple							=						cls.squareplus(_UpperCAmelCase					)
       return total_count.squeeze(-1					), logits.squeeze(-1					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A,	A       :       Any							=						distr_args
       if self.dim == 1:
           return self.distribution_class(total_count=_UpperCAmelCase    ,   logits=_UpperCAmelCase					)
       else:
           return Independent(self.distribution_class(total_count=_UpperCAmelCase    ,   logits=_UpperCAmelCase					)    ,   1					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None					):
       A,	A       :       str							=						distr_args

       if scale is not None:
           # See scaling property of Gamma.
           logits += scale.log()

       return self._base_distribution((total_count, logits)					)







'''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
