






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A	:			str         =logging.get_logger(__name__)

_A	:			List[str]         ={
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}

class _lowercase	(				_lowercase				,      _lowercase		):
							a				      =       """focalnet"""




							def __init__(							self:				Dict			,							UpperCamelCase__:				List[str]=224			,							UpperCamelCase__:				str=4			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				List[Any]=96			,							UpperCamelCase__:				Dict=False			,							UpperCamelCase__:				Union[str, Any]=[192, 384, 768, 768]			,							UpperCamelCase__:				Union[str, Any]=[2, 2, 6, 2]			,							UpperCamelCase__:				Any=[2, 2, 2, 2]			,							UpperCamelCase__:				Union[str, Any]=[3, 3, 3, 3]			,							UpperCamelCase__:				Dict="gelu"			,							UpperCamelCase__:				Dict=4.0			,							UpperCamelCase__:				Optional[int]=0.0			,							UpperCamelCase__:				str=0.1			,							UpperCamelCase__:				Union[str, Any]=False			,							UpperCamelCase__:				str=1e-4			,							UpperCamelCase__:				List[str]=False			,							UpperCamelCase__:				str=False			,							UpperCamelCase__:				List[str]=False			,							UpperCamelCase__:				Optional[Any]=0.02			,							UpperCamelCase__:				str=1e-5			,							UpperCamelCase__:				Union[str, Any]=32			,							UpperCamelCase__:				int=None			,							UpperCamelCase__:				Optional[int]=None			,							**UpperCamelCase__:				int			,							):
														super().__init__(**UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       image_size
														lowerCamelCase__					:	Dict         =       patch_size
														lowerCamelCase__					:	int         =       num_channels
														lowerCamelCase__					:	str         =       embed_dim
														lowerCamelCase__					:	List[str]         =       use_conv_embed
														lowerCamelCase__					:	Dict         =       hidden_sizes
														lowerCamelCase__					:	List[Any]         =       depths
														lowerCamelCase__					:	int         =       focal_levels
														lowerCamelCase__					:	Dict         =       focal_windows
														lowerCamelCase__					:	str         =       hidden_act
														lowerCamelCase__					:	Optional[Any]         =       mlp_ratio
														lowerCamelCase__					:	int         =       hidden_dropout_prob
														lowerCamelCase__					:	Any         =       drop_path_rate
														lowerCamelCase__					:	Dict         =       use_layerscale
														lowerCamelCase__					:	Union[str, Any]         =       layerscale_value
														lowerCamelCase__					:	Optional[Any]         =       use_post_layernorm
														lowerCamelCase__					:	Tuple         =       use_post_layernorm_in_modulation
														lowerCamelCase__					:	Optional[int]         =       normalize_modulator
														lowerCamelCase__					:	Dict         =       initializer_range
														lowerCamelCase__					:	Any         =       layer_norm_eps
														lowerCamelCase__					:	Union[str, Any]         =       encoder_stride
														lowerCamelCase__					:	Union[str, Any]         =       ["""stem"""] + [F'''stage{idx}''' for idx in range(1			,							len(self.depths     ) + 1     )]
														lowerCamelCase__  ,		lowerCamelCase__					:	str         =       get_aligned_output_features_output_indices(
														    out_features=UpperCamelCase__			,							out_indices=UpperCamelCase__			,							stage_names=self.stage_names     )









'''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

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

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =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	:			int         =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	:			List[str]         =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	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

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

