


'''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=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,
)

__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



'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  torch.FloatTensor




class 						a__							(				a__							,							a__    ):





							'''simple docstring'''






							@register_to_config
							def __init__(						self   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = ("DownEncoderBlock2D",)   ,       lowerCamelCase_ = ("UpDecoderBlock2D",)   ,       lowerCamelCase_ = (64,)   ,       lowerCamelCase_ = 1   ,       lowerCamelCase_ = "silu"   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = 2_56   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = 0.18_215   ,       lowerCamelCase_ = "group"   ,       )			->				Union[str, Any]:
														super().__init__()

														# pass init params to Encoder
														lowerCAmelCase__			  =      Encoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       down_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       double_z=lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      vq_embed_dim if vq_embed_dim is not None else latent_channels

														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )
														lowerCAmelCase__			  =      VectorQuantizer(lowerCamelCase_   ,       lowerCamelCase_   ,       beta=0.25   ,       remap=lowerCamelCase_   ,       sane_index_shape=lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )

														# pass init params to Decoder
														lowerCAmelCase__			  =      Decoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       up_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       norm_type=lowerCamelCase_   ,       )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				VQEncoderOutput:
														lowerCAmelCase__			  =      self.encoder(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.quant_conv(lowerCamelCase_  )

														if not return_dict:
																					return (h,)

														return VQEncoderOutput(latents=lowerCamelCase_  )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														# also go through quantization layer
														if not force_not_quantize:
																					lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.quantize(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      h
														lowerCAmelCase__			  =      self.post_quant_conv(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.decoder(lowerCamelCase_   ,       quant if self.config.norm_type == '''spatial''' else None  )

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														lowerCAmelCase__			  =      sample
														lowerCAmelCase__			  =      self.encode(lowerCamelCase_  ).latents
														lowerCAmelCase__			  =      self.decode(lowerCamelCase_  ).sample

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )