



"""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

SCREAMING_SNAKE_CASE_				= """tiny-wmt19-en-ru"""

# Build

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

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

					SCREAMING_SNAKE_CASE_				= 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,
					)

SCREAMING_SNAKE_CASE_				= FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    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,
)

SCREAMING_SNAKE_CASE_				= FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
SCREAMING_SNAKE_CASE_				= tokenizer(["""Making tiny model"""], return_tensors="""pt""")
SCREAMING_SNAKE_CASE_				= 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)