

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 				lowercase       :
				__SCREAMING_SNAKE_CASE      :				str     =    LEDConfig
				__SCREAMING_SNAKE_CASE      :				Union[str, Any]     =    {}
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    '''gelu'''




				def __init__(      self   ,     snake_case   ,     snake_case=13   ,     snake_case=7   ,     snake_case=True   ,     snake_case=False   ,     snake_case=99   ,     snake_case=32   ,     snake_case=2   ,     snake_case=4   ,     snake_case=37   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=20   ,     snake_case=2   ,     snake_case=1   ,     snake_case=0   ,     snake_case=4   ,     ):
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					seq_length
											snake_case_           =					is_training
											snake_case_           =					use_labels
											snake_case_           =					vocab_size
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					intermediate_size

											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					max_position_embeddings
											snake_case_           =					eos_token_id
											snake_case_           =					pad_token_id
											snake_case_           =					bos_token_id
											snake_case_           =					attention_window

											# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
											# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
											# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
											# because its local attention only attends to `self.attention_window` and one before and one after
											snake_case_           =					self.attention_window + 2

											# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
											# the `test_attention_outputs` and `test_hidden_states_output` tests
											snake_case_           =					(
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)




				def 					a							(      self ):
											snake_case_           =					ids_tensor([self.batch_size, self.seq_length - 1]   ,     self.vocab_size )
											snake_case_           =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )   ,     1 )
											snake_case_           =					tf.concat([input_ids, eos_tensor]   ,     axis=1 )

											snake_case_           =					ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size )

											snake_case_           =					self.config_cls(
											    vocab_size=self.vocab_size   ,     d_model=self.hidden_size   ,     encoder_layers=self.num_hidden_layers   ,     decoder_layers=self.num_hidden_layers   ,     encoder_attention_heads=self.num_attention_heads   ,     decoder_attention_heads=self.num_attention_heads   ,     encoder_ffn_dim=self.intermediate_size   ,     decoder_ffn_dim=self.intermediate_size   ,     dropout=self.hidden_dropout_prob   ,     attention_dropout=self.attention_probs_dropout_prob   ,     max_position_embeddings=self.max_position_embeddings   ,     eos_token_ids=[2]   ,     bos_token_id=self.bos_token_id   ,     pad_token_id=self.pad_token_id   ,     decoder_start_token_id=self.pad_token_id   ,     attention_window=self.attention_window   ,     **self.config_updates   ,     )
											snake_case_           =					prepare_led_inputs_dict(snake_case   ,     snake_case   ,     snake_case )
											snake_case_           =					tf.concat(
											    [tf.zeros_like(snake_case )[:, :-1], tf.ones_like(snake_case )[:, -1:]]   ,     axis=-1   ,     )
											snake_case_           =					global_attention_mask
											return config, inputs_dict




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					TFLEDModel(config=snake_case ).get_decoder()
											snake_case_           =					inputs_dict['input_ids']

											snake_case_           =					input_ids[:1, :]
											snake_case_           =					inputs_dict['attention_mask'][:1, :]
											snake_case_           =					1

											# first forward pass
											snake_case_           =					model(snake_case   ,     attention_mask=snake_case   ,     use_cache=snake_case )

											snake_case_       ,					snake_case_           =					outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											snake_case_           =					ids_tensor((self.batch_size, 3)   ,     config.vocab_size )
											snake_case_           =					tf.cast(ids_tensor((self.batch_size, 3)   ,     2 )   ,     tf.inta )

											# append to next input_ids and
											snake_case_           =					tf.concat([input_ids, next_tokens]   ,     axis=-1 )
											snake_case_           =					tf.concat([attention_mask, next_attn_mask]   ,     axis=-1 )

											snake_case_           =					model(snake_case   ,     attention_mask=snake_case )[0]
											snake_case_           =					model(snake_case   ,     attention_mask=snake_case   ,     past_key_values=snake_case )[0]

											self.parent.assertEqual(next_tokens.shape[1]   ,     output_from_past.shape[1] )

											# select random slice
											snake_case_           =					int(ids_tensor((1,)   ,     output_from_past.shape[-1] ) )
											snake_case_           =					output_from_no_past[:, -3:, random_slice_idx]
											snake_case_           =					output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(snake_case   ,     snake_case   ,     rtol=1e-3 )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							):







							'''simple docstring'''






							if attention_mask is None:
														snake_case_           =					tf.cast(tf.math.not_equal(UpperCamelCase__     ,							config.pad_token_id   )     ,							tf.inta   )
							if decoder_attention_mask is None:
														snake_case_           =					tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape     ,							dtype=tf.inta   ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,							config.pad_token_id   )     ,							tf.inta   ),
														    ]     ,							axis=-1     ,							)
							if head_mask is None:
														snake_case_           =					tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
							if decoder_head_mask is None:
														snake_case_           =					tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							    "decoder_input_ids": decoder_input_ids,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							}







@require_tf
class 				lowercase       ( lowercase_			,						lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE      :				int     =    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    (
				    {
				        '''conversational''': TFLEDForConditionalGeneration,
				        '''feature-extraction''': TFLEDModel,
				        '''summarization''': TFLEDForConditionalGeneration,
				        '''text2text-generation''': TFLEDForConditionalGeneration,
				        '''translation''': TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE      :				Tuple     =    True
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    False
				__SCREAMING_SNAKE_CASE      :				Dict     =    False
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    False




				def 					a							(      self ):
											snake_case_           =					TFLEDModelTester(self )
											snake_case_           =					ConfigTester(self   ,     config_class=snake_case )




				def 					a							(      self ):
											self.config_tester.run_common_tests()




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*snake_case )




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_           =					tf.zeros_like(inputs_dict['attention_mask'] )
											snake_case_           =					2
											snake_case_           =					tf.where(
											    tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices   ,     1   ,     inputs_dict['global_attention_mask']   ,     )

											snake_case_           =					True
											snake_case_           =					self.model_tester.seq_length
											snake_case_           =					self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(snake_case ):
																		snake_case_           =					outputs.decoder_attentions
																		self.assertEqual(len(snake_case )   ,     self.model_tester.num_hidden_layers )
																		self.assertListEqual(
																		    list(decoder_attentions[0].shape[-3:] )   ,     [self.model_tester.num_attention_heads, seq_length, seq_length]   ,     )

											def check_encoder_attentions_output(snake_case ):
																		snake_case_           =					[t.numpy() for t in outputs.encoder_attentions]
																		snake_case_           =					[t.numpy() for t in outputs.encoder_global_attentions]
																		self.assertEqual(len(snake_case )   ,     self.model_tester.num_hidden_layers )
																		self.assertEqual(len(snake_case )   ,     self.model_tester.num_hidden_layers )
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:] )   ,     [self.model_tester.num_attention_heads, seq_length, seq_length]   ,     )
																		self.assertListEqual(
																		    list(global_attentions[0].shape[-3:] )   ,     [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]   ,     )

											for model_class in self.all_model_classes:
																		snake_case_           =					True
																		snake_case_           =					False
																		snake_case_           =					False
																		snake_case_           =					model_class(snake_case )
																		snake_case_           =					model(self._prepare_for_class(snake_case   ,     snake_case ) )
																		snake_case_           =					len(snake_case )
																		self.assertEqual(config.output_hidden_states   ,     snake_case )
																		check_encoder_attentions_output(snake_case )

																		if self.is_encoder_decoder:
																									snake_case_           =					model_class(snake_case )
																									snake_case_           =					model(self._prepare_for_class(snake_case   ,     snake_case ) )
																									self.assertEqual(config.output_hidden_states   ,     snake_case )
																									check_decoder_attentions_output(snake_case )

																		# Check that output attentions can also be changed via the config
																		del inputs_dict["output_attentions"]
																		snake_case_           =					True
																		snake_case_           =					model_class(snake_case )
																		snake_case_           =					model(self._prepare_for_class(snake_case   ,     snake_case ) )
																		self.assertEqual(config.output_hidden_states   ,     snake_case )
																		check_encoder_attentions_output(snake_case )

																		# Check attention is always last and order is fine
																		snake_case_           =					True
																		snake_case_           =					True
																		snake_case_           =					model_class(snake_case )
																		snake_case_           =					model(self._prepare_for_class(snake_case   ,     snake_case ) )

																		self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   ,     len(snake_case ) )
																		self.assertEqual(model.config.output_hidden_states   ,     snake_case )
																		check_encoder_attentions_output(snake_case )




				@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
				def 					a							(      self ):
											pass




				def 					a							(      self ):
											# TODO: Head-masking not yet implement
											pass





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return tf.constant(UpperCamelCase__     ,							dtype=tf.intaa   )


_UpperCAmelCase	:     Union[str, Any]		       = 1e-4







@slow
@require_tf
class 				lowercase       ( unittest.TestCase    ):




				def 					a							(      self ):
											snake_case_           =					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led

											# change to intended input here
											snake_case_           =					_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
											snake_case_           =					_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
											snake_case_           =					prepare_led_inputs_dict(model.config   ,     snake_case   ,     snake_case )
											snake_case_           =					model(**snake_case )[0]
											snake_case_           =					(1, 1024, 768)
											self.assertEqual(output.shape   ,     snake_case )
											# change to expected output here
											snake_case_           =					tf.convert_to_tensor(
											    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]   ,     )
											tf.debugging.assert_near(output[:, :3, :3]   ,     snake_case   ,     atol=1e-3 )




				def 					a							(      self ):
											snake_case_           =					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )

											# change to intended input here
											snake_case_           =					_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
											snake_case_           =					_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
											snake_case_           =					prepare_led_inputs_dict(model.config   ,     snake_case   ,     snake_case )
											snake_case_           =					model(**snake_case )[0]
											snake_case_           =					(1, 1024, model.config.vocab_size)
											self.assertEqual(output.shape   ,     snake_case )
											# change to expected output here
											snake_case_           =					tf.convert_to_tensor(
											    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]   ,     )
											tf.debugging.assert_near(output[:, :3, :3]   ,     snake_case   ,     atol=1e-3   ,     rtol=1e-3 )







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							for param, grad_param in zip(model_a.parameters()     ,							model_b.parameters()   ):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad     ,							grad_param.grad   ) is False
																					), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad     ,							grad_param.grad   ) is True
																					), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True   ):







							'''simple docstring'''






							model.train()
							snake_case_           =					model(UpperCamelCase__   )
							snake_case_           =					F.mse_loss(UpperCamelCase__     ,							target.to(output.device   )   )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							set_seed(42   )
							snake_case_           =					RegressionModel()
							snake_case_           =					deepcopy(UpperCamelCase__   )
							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							model.to(accelerator.device   )
							if sched:
														snake_case_           =					AdamW(params=model.parameters()     ,							lr=1E-3   )
														snake_case_           =					AdamW(params=ddp_model.parameters()     ,							lr=1E-3   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
							# Make a copy of `model`
							if sched:
														snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							else:
														snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__   )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__   ):
																												step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad     ,							ddp_param.grad   ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__   ):
																												step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is False
																												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is True
																												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(UpperCamelCase__   ):
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__   ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is True
																												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is False
																												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]
							GradientState._reset_state()





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__     ,							UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__   )):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes   ):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(UpperCamelCase__   ):
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
														snake_case_           =					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__   ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
							GradientState._reset_state()





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()

							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_           =					RegressionDataset(length=96   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__   )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCamelCase__   ):
														assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase__   )
														if iteration < len(UpperCamelCase__   ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCamelCase__   ):
																																			assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase__   )
																																			if batch_num < len(UpperCamelCase__   ) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()
							snake_case_           =					accelerator.state
							if state.local_process_index == 0:
														print('**Test `accumulate` gradient accumulation with dataloader break**'   )
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print('**Test NOOP `no_sync` context manager**'   )
														test_noop_sync(UpperCamelCase__   )
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print('**Test Distributed `no_sync` context manager**'   )
														test_distributed_sync(UpperCamelCase__   )
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    '**Test `accumulate` gradient accumulation, '     ,							F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,							)
																												test_gradient_accumulation(UpperCamelCase__     ,							UpperCamelCase__   )

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version('<'     ,							'2.0'   ) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,							'`split_batches=False`, `dispatch_batches=False`**'     ,							)
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,							F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,							)
																																			test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__     ,							UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()




