




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A     =			{'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A     =			[
						    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MraForMaskedLM''',
						    '''MraForMultipleChoice''',
						    '''MraForQuestionAnswering''',
						    '''MraForSequenceClassification''',
						    '''MraForTokenClassification''',
						    '''MraLayer''',
						    '''MraModel''',
						    '''MraPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						_A     =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)










# 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




