



"""simple docstring"""







import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase:   Union[str, Any]       			=							namedtuple("""ExampleNamedTuple""", """a b c""")





class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					torch.randn(5							,2					)
			_lowercase							:      List[str]			=					torch.device("""cuda"""					) if torch.cuda.is_available() else torch.device("""cpu"""					)

			_lowercase							:      int			=					send_to_device(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta.cpu()							,UpperCAmelCase_					)					)

			_lowercase							:      str			=					send_to_device((tensor, [tensor, tensor], 1)							,UpperCAmelCase_					)
			self.assertIsInstance(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta[0].cpu()							,UpperCAmelCase_					)					)
			self.assertIsInstance(resulta[1]							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta[1][0].cpu()							,UpperCAmelCase_					)					)
			self.assertTrue(torch.equal(resulta[1][1].cpu()							,UpperCAmelCase_					)					)
			self.assertEqual(resulta[2]							,1					)

			_lowercase							:      Any			=					send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}							,UpperCAmelCase_					)
			self.assertIsInstance(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta["""a"""].cpu()							,UpperCAmelCase_					)					)
			self.assertIsInstance(resulta["""b"""]							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()							,UpperCAmelCase_					)					)
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()							,UpperCAmelCase_					)					)
			self.assertEqual(resulta["""c"""]							,1					)

			_lowercase							:      List[Any]			=					send_to_device(ExampleNamedTuple(a=UpperCAmelCase_							,b=[tensor, tensor]							,c=1					)							,UpperCAmelCase_					)
			self.assertIsInstance(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta.a.cpu()							,UpperCAmelCase_					)					)
			self.assertIsInstance(resulta.b							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta.b[0].cpu()							,UpperCAmelCase_					)					)
			self.assertTrue(torch.equal(resulta.b[1].cpu()							,UpperCAmelCase_					)					)
			self.assertEqual(resulta.c							,1					)

			_lowercase							:      Union[str, Any]			=					send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}					)							,UpperCAmelCase_					)
			self.assertIsInstance(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta["""a"""].cpu()							,UpperCAmelCase_					)					)
			self.assertIsInstance(resulta["""b"""]							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()							,UpperCAmelCase_					)					)
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()							,UpperCAmelCase_					)					)
			self.assertEqual(resulta["""c"""]							,1					)






	def 					lowerCamelCase__ (			self					):
			with self.assertRaises(UpperCAmelCase_					) as cm:
					_lowercase							:      List[str]			=					recursively_apply(torch.tensor							,(torch.tensor(1					), 1)							,error_on_other_type=UpperCAmelCase_					)
			self.assertEqual(
			    str(cm.exception					)							,"""Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."""							,)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					torch.tensor([1, 2, 3, 4, 5]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[1, 2, 3, 4, 5]					)

			_lowercase							:      Any			=					torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

			_lowercase							:      Any			=					torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
			self.assertEqual(
			    listify(UpperCAmelCase_					)							,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






	def 					lowerCamelCase__ (			self					):
			with patch_environment(aa=1							,BB=2					):
					self.assertEqual(os.environ.get("""AA"""					)							,"""1"""					)
					self.assertEqual(os.environ.get("""BB"""					)							,"""2"""					)

			self.assertNotIn("""AA"""							,os.environ					)
			self.assertNotIn("""BB"""							,os.environ					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					RegressionModel()
			_lowercase							:      Optional[Any]			=					model.forward
			_lowercase							:      Tuple			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      List[str]			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					RegressionModel()
			_lowercase							:      int			=					model.forward
			_lowercase							:      List[str]			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Dict			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      Tuple			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Dict			=					RegressionModel()
			_lowercase							:      List[str]			=					model.forward
			_lowercase							:      Any			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Any			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      Union[str, Any]			=					torch.compile(model.forward							,backend="""inductor"""					)
			_lowercase							:      Dict			=					torch.randn(4							,10					).cuda()
			_lowercase							:      str			=					model(UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase							:      int			=					torch.nn.parallel.DataParallel(UpperCAmelCase_					)
			_lowercase							:      List[str]			=					extract_model_from_parallel(UpperCAmelCase_					)

			self.assertEqual(UpperCAmelCase_							,UpperCAmelCase_					)






	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					RegressionModel()
			_lowercase							:      str			=					torch.compile(UpperCAmelCase_					)

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase							:      Optional[int]			=					torch.nn.parallel.DataParallel(UpperCAmelCase_					)
			_lowercase							:      Any			=					torch.compile(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					extract_model_from_parallel(UpperCAmelCase_					)

			self.assertEqual(compiled_model._orig_mod							,compiled_model_unwrapped._orig_mod					)






	def 					lowerCamelCase__ (			self					):
			self.assertEqual(find_device([1, """a""", torch.tensor([1, 2, 3]					)]					)							,torch.device("""cpu"""					)					)
			self.assertEqual(find_device({"""a""": 1, """b""": torch.tensor([1, 2, 3]					)}					)							,torch.device("""cpu"""					)					)
			self.assertIsNone(find_device([1, """a"""]					)					)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






	def __init__(			self							,UpperCAmelCase_=5_02_72							,UpperCAmelCase_=7_68							,UpperCAmelCase_=12							,UpperCAmelCase_=30_72							,UpperCAmelCase_=20_48							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=None							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.0							,UpperCAmelCase_=12							,UpperCAmelCase_="relu"							,UpperCAmelCase_=0.0							,UpperCAmelCase_=0.02							,UpperCAmelCase_=True							,UpperCAmelCase_=1							,UpperCAmelCase_=2							,UpperCAmelCase_=2							,UpperCAmelCase_=True							,UpperCAmelCase_=True							,**UpperCAmelCase_							,):
			super().__init__(
			    pad_token_id=UpperCAmelCase_							,bos_token_id=UpperCAmelCase_							,eos_token_id=UpperCAmelCase_							,**UpperCAmelCase_							,)
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Tuple			=					max_position_embeddings
			_lowercase							:      Union[str, Any]			=					num_attention_heads
			_lowercase							:      List[str]			=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
			_lowercase							:      List[str]			=					ffn_dim
			_lowercase							:      List[str]			=					hidden_size
			_lowercase							:      List[Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					dropout
			_lowercase							:      str			=					attention_dropout
			_lowercase							:      List[Any]			=					activation_function
			_lowercase							:      int			=					init_std
			_lowercase							:      List[Any]			=					layerdrop
			_lowercase							:      Tuple			=					use_cache
			_lowercase							:      Any			=					do_layer_norm_before
			# We keep these variables at `True` for backward compatibility.
			_lowercase							:      int			=					enable_bias
			_lowercase							:      str			=					layer_norm_elementwise_affine

			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
			# with checkpoints that have been fine-tuned before transformers v4.20.1
			# see https://github.com/facebookresearch/metaseq/pull/164
			_lowercase							:      Optional[Any]			=					_remove_final_layer_norm



