from typing import List, Union

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


UpperCamelCase      			=     logging.get_logger(__name__)

UpperCamelCase      			=     {
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	lowerCamelCase_   :      str       =	"""led"""
	lowerCamelCase_   :      Dict       =	{
	    """num_attention_heads""": """encoder_attention_heads""",
	    """hidden_size""": """d_model""",
	    """attention_probs_dropout_prob""": """attention_dropout""",
	    """initializer_range""": """init_std""",
	}

	def __init__(       self							,			__UpperCAmelCase=50_265							,			__UpperCAmelCase=16_384							,			__UpperCAmelCase=1_024							,			__UpperCAmelCase=12							,			__UpperCAmelCase=4_096							,			__UpperCAmelCase=16							,			__UpperCAmelCase=12							,			__UpperCAmelCase=4_096							,			__UpperCAmelCase=16							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase="gelu"							,			__UpperCAmelCase=1_024							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.02							,			__UpperCAmelCase=2							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=1							,			__UpperCAmelCase=0							,			__UpperCAmelCase=2							,			__UpperCAmelCase = 512							,			**__UpperCAmelCase							,			):
			__A							:   List[str]	  =				vocab_size
			__A							:   List[Any]	  =				max_encoder_position_embeddings
			__A							:   Tuple	  =				max_decoder_position_embeddings
			__A							:   List[Any]	  =				d_model
			__A							:   Optional[int]	  =				encoder_ffn_dim
			__A							:   Optional[Any]	  =				encoder_layers
			__A							:   List[str]	  =				encoder_attention_heads
			__A							:   Optional[Any]	  =				decoder_ffn_dim
			__A							:   Tuple	  =				decoder_layers
			__A							:   int	  =				decoder_attention_heads
			__A							:   Tuple	  =				dropout
			__A							:   int	  =				attention_dropout
			__A							:   int	  =				activation_dropout
			__A							:   int	  =				activation_function
			__A							:   Dict	  =				init_std
			__A							:   List[str]	  =				encoder_layerdrop
			__A							:   Union[str, Any]	  =				decoder_layerdrop
			__A							:   List[Any]	  =				classifier_dropout
			__A							:   Dict	  =				use_cache
			__A							:   str	  =				encoder_layers
			__A							:   int	  =				attention_window

			super().__init__(
			    pad_token_id=__UpperCAmelCase							,			bos_token_id=__UpperCAmelCase							,			eos_token_id=__UpperCAmelCase							,			is_encoder_decoder=__UpperCAmelCase							,			decoder_start_token_id=__UpperCAmelCase							,			**__UpperCAmelCase							,			)






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      			=     namedtuple('ExampleNamedTuple', 'a b c')


class _a       (	unittest.TestCase ):

	'''simple docstring'''



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

			__A							:   Union[str, Any]	  =				send_to_device(__UpperCAmelCase							,			__UpperCAmelCase   )
			self.assertTrue(torch.equal(resulta.cpu()							,			__UpperCAmelCase   )   )

			__A							:   Dict	  =				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   )

			__A							:   Optional[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   )

			__A							:   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   )

			__A							:   List[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 					__UpperCAmelCase(       self   ):
			with self.assertRaises(__UpperCAmelCase   ) as cm:
					__A							:   List[Any]	  =				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 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				torch.tensor([1, 2, 3, 4, 5]   )
			self.assertEqual(listify(__UpperCAmelCase   )							,			[1, 2, 3, 4, 5]   )

			__A							:   List[str]	  =				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]]   )

			__A							:   Dict	  =				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 					__UpperCAmelCase(       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 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				RegressionModel()
			__A							:   str	  =				model.forward
			__A							:   List[Any]	  =				convert_outputs_to_fpaa(model.forward   )
			__A							:   Optional[int]	  =				extract_model_from_parallel(__UpperCAmelCase							,			keep_fpaa_wrapper=__UpperCAmelCase   )
			__A							:   Any	  =				pickle.dumps(__UpperCAmelCase   )

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

	@require_cuda
	@require_torch_min_version(version="2.0"   )
	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				RegressionModel()
			__A							:   Optional[int]	  =				model.forward
			__A							:   Union[str, Any]	  =				torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			__A							:   int	  =				convert_outputs_to_fpaa(model.forward   )
			__A							:   Optional[Any]	  =				torch.compile(model.forward							,			backend="inductor"   )
			__A							:   Tuple	  =				torch.randn(4							,			10   ).cuda()
			__A							:   Union[str, Any]	  =				model(__UpperCAmelCase   )

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

			self.assertEqual(__UpperCAmelCase							,			__UpperCAmelCase   )

	@require_torch_min_version(version="2.0"   )
	def 					__UpperCAmelCase(       self   ):
			__A							:   Dict	  =				RegressionModel()
			__A							:   Dict	  =				torch.compile(__UpperCAmelCase   )

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			__A							:   Dict	  =				torch.nn.parallel.DataParallel(__UpperCAmelCase   )
			__A							:   Tuple	  =				torch.compile(__UpperCAmelCase   )
			__A							:   Any	  =				extract_model_from_parallel(__UpperCAmelCase   )

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







	def 					__UpperCAmelCase(       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"]   )   )





