






"""simple docstring"""
from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

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

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

			lowerCamelCase_       			=   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,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



