




"""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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

A				=							"""facebook/wmt19-en-de"""
A				=							FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
A				=							FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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
A				=							"""tiny-wmt19-en-de"""
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-de



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)







@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     list_field(
							    default=[],  metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    },  )

							UpperCAmelCase				   =     list_field(
							    default=[8],  metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )

							UpperCAmelCase				   =     list_field(
							    default=[8, 32, 128, 512],  metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},  )

							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Use FP16 to accelerate inference."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Benchmark training of model"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Verbose memory tracing"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    },  )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Trace memory line by line"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save result to a CSV file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save all print statements in a log file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Whether to print environment information"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""env_info_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving environment information."},  )
							UpperCAmelCase				   =     field(
							    default=F"""log_{round(time() )}.csv""",  metadata={"help": "Log filename used if print statements are saved in log."},  )
							UpperCAmelCase				   =     field(default=3,  metadata={"help": "Times an experiment will be run."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    },  )







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												warnings.warn(
												    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
												    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
												    ''' to benchmark Transformer models.''' ,			_A ,			)







							def  UpperCamelCase_						(   self      :     str		):
												return json.dumps(dataclasses.asdict(self		) ,			indent=2		)







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												if len(self.models		) <= 0:
																	raise ValueError(
																	    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																	    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''		)
												return self.models







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												if not self.multi_process:
																	return False
												elif self.is_tpu:
																	logger.info('''Multiprocessing is currently not possible on TPU.'''		)
																	return False
												else:
																	return True
