






'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Union[str, Any]									=   inspect.getfile(accelerate.test_utils       )
							a_		:	str									=   os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_script.py''']       )
							a_		:	Any									=   os.path.sep.join(
							    mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']       )
							a_		:	Tuple									=   os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_ops.py''']       )






			@require_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Tuple:
							print(f'Found {torch.cuda.device_count()} devices.'       )
							a_		:	List[str]									=   ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1       ):
											execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    env=os.environ.copy()       )






			@require_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[Any]:
							print(f'Found {torch.cuda.device_count()} devices.'       )
							a_		:	List[Any]									=   ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
							print(f'Command: {cmd}'       )
							with patch_environment(omp_num_threads=1       ):
											execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    env=os.environ.copy()       )






			@require_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[int]:
							a_		:	Optional[int]									=   ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__       )]
							with patch_environment(omp_num_threads=1       ):
											execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    env=os.environ.copy()       )







			@require_multi_gpu
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Optional[Any]:
							print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'       )
							a_		:	Optional[Any]									=   ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
							with patch_environment(omp_num_threads=1	,    cuda_visible_devices='''0,1'''       ):
											execute_subprocess_async(__SCREAMING_SNAKE_CASE	,    env=os.environ.copy()       )


if __name__ == "__main__":
		__lowerCAmelCase									=   Accelerator()
		__lowerCAmelCase									=   (accelerator.state.process_index + 2, 10)
		__lowerCAmelCase									=   torch.randint(0, 10, shape).to(accelerator.device)

		__lowerCAmelCase									=   ''

		__lowerCAmelCase									=   accelerator.pad_across_processes(tensor)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
				error_msg += "Padding was not done with the right value (0)."

		__lowerCAmelCase									=   accelerator.pad_across_processes(tensor, pad_first=True)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		__lowerCAmelCase									=   accelerator.state.num_processes - accelerator.state.process_index - 1
		if not torch.equal(tensora[index:], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[:index] == 0):
				error_msg += "Padding was not done with the right value (0)."

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)












'''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

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   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,
    )
)

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

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   '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




