





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       :     List[Any]   =     """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
__lowerCamelCase       :     int   =     [
    """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       :     Tuple   =     dict(zip(vocab, range(len(vocab))))
__lowerCamelCase       :     Optional[Any]   =     ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
    __lowerCamelCase       :     Tuple   =     Path(tmpdirname)
    __lowerCamelCase       :     int   =     build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
    __lowerCamelCase       :     Any   =     build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
    __lowerCamelCase       :     List[str]   =     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       :     List[str]   =     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       :     Any   =     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       :     List[Any]   =     FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCamelCase       :     Tuple   =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__lowerCamelCase       :     Optional[Any]   =     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'''

# Copyright 2022 The HuggingFace Team and The OpenBMB 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


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




