




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)
class 		UpperCAmelCase       :







						'''simple docstring'''





						def __init__(						self							:					Union[str, Any]	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					parent
							snake_case_		   =					13
							snake_case_		   =					7
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					True
							snake_case_		   =					99
							snake_case_		   =					32
							snake_case_		   =					4
							snake_case_		   =					2
							snake_case_		   =					4
							snake_case_		   =					37
							snake_case_		   =					"gelu"
							snake_case_		   =					0.1
							snake_case_		   =					0.1
							snake_case_		   =					5_12
							snake_case_		   =					16
							snake_case_		   =					2
							snake_case_		   =					0.02
							snake_case_		   =					3
							snake_case_		   =					4
							snake_case_		   =					None
							snake_case_		   =					self.vocab_size - 1
							snake_case_		   =					self.vocab_size - 1
							snake_case_		   =					self.vocab_size - 1






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






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

							snake_case_		   =					None
							if self.use_input_mask:
								snake_case_		   =					random_attention_mask([self.batch_size, self.seq_length]						)

							snake_case_		   =					None
							if self.use_token_type_ids:
								snake_case_		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.type_vocab_size						)

							snake_case_		   =					None
							if self.use_mc_token_ids:
								snake_case_		   =					ids_tensor([self.batch_size, self.num_choices]	,     self.seq_length						)

							snake_case_		   =					None
							snake_case_		   =					None
							snake_case_		   =					None
							if self.use_labels:
								snake_case_		   =					ids_tensor([self.batch_size]	,     self.type_sequence_label_size						)
								snake_case_		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.num_labels						)
								snake_case_		   =					ids_tensor([self.batch_size]	,     self.num_choices						)

							snake_case_		   =					GPTJConfig(
							    vocab_size=self.vocab_size	,     n_embd=self.hidden_size	,     n_layer=self.num_hidden_layers	,     n_head=self.num_attention_heads	,     intermediate_size=self.intermediate_size	,     hidden_act=self.hidden_act	,     hidden_dropout_prob=self.hidden_dropout_prob	,     attention_probs_dropout_prob=self.attention_probs_dropout_prob	,     n_positions=self.max_position_embeddings	,     type_vocab_size=self.type_vocab_size	,     initializer_range=self.initializer_range	,     bos_token_id=self.bos_token_id	,     eos_token_id=self.eos_token_id	,     pad_token_id=self.pad_token_id	,     rotary_dim=self.rotary_dim	,     return_dict=__lowercase	,     )

							snake_case_		   =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,     2						)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)






						def 	snake_case__						(						self							:					int	,     __lowercase							:					Tuple	,     __lowercase							:					Optional[int]	,     __lowercase							:					Any	,     __lowercase							:					List[Any]	,     __lowercase							:					Any	,     *__lowercase							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)
							snake_case_		   =					{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							snake_case_		   =					model(__lowercase						)

							snake_case_		   =					[input_ids, None, input_mask]  # None is the input for 'past'
							snake_case_		   =					model(__lowercase						)

							snake_case_		   =					model(__lowercase						)

							self.parent.assertEqual(result.last_hidden_state.shape	,     (self.batch_size, self.seq_length, self.hidden_size)						)






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					str	,     __lowercase							:					Any	,     __lowercase							:					Optional[int]	,     __lowercase							:					int	,     *__lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							# first forward pass
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     use_cache=__lowercase						)
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase						)
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     use_cache=__lowercase						)

							self.parent.assertTrue(len(__lowercase						) == len(__lowercase						)						)
							self.parent.assertTrue(len(__lowercase						) == len(__lowercase						) + 1						)

							snake_case_   ,				snake_case_		   =					outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		   =					ids_tensor((self.batch_size, 1)	,     config.vocab_size						)
							snake_case_		   =					ids_tensor([self.batch_size, 1]	,     self.type_vocab_size						)

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

							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(__lowercase	,     token_type_ids=__lowercase	,     past_key_values=__lowercase						)[
							    "last_hidden_state"
							]

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-1]						)						)
							snake_case_		   =					output_from_no_past[:, -1, random_slice_idx]
							snake_case_		   =					output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowercase	,     __lowercase	,     rtol=1E-6						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					List[Any]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Dict	,     __lowercase							:					Optional[Any]	,     __lowercase							:					int	,     *__lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							# create attention mask
							snake_case_		   =					self.seq_length // 2
							snake_case_		   =					tf.ones((self.batch_size, half_seq_length)	,     dtype=tf.intaa						)
							snake_case_		   =					tf.zeros((self.batch_size, self.seq_length - half_seq_length)	,     dtype=tf.intaa						)
							snake_case_		   =					tf.concat([attn_mask_begin, attn_mask_end]	,     axis=1						)

							# first forward pass
							snake_case_   ,				snake_case_		   =					model(__lowercase	,     attention_mask=__lowercase						).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_		   =					ids_tensor((self.batch_size, 1)	,     config.vocab_size						)

							# change a random masked slice from input_ids
							snake_case_		   =					ids_tensor((1,)	,     __lowercase						).numpy() + 1
							snake_case_		   =					ids_tensor((self.batch_size, self.seq_length)	,     config.vocab_size						)
							snake_case_		   =					tf.range(self.seq_length						) == (self.seq_length - random_seq_idx_to_change)
							snake_case_		   =					tf.transpose(
							    tf.broadcast_to(tf.expand_dims(__lowercase	,     -1						)	,     (self.seq_length, self.batch_size)						)						)
							snake_case_		   =					tf.where(__lowercase	,     __lowercase	,     __lowercase						)

							# append to next input_ids and attn_mask
							snake_case_		   =					tf.concat([input_ids, next_tokens]	,     axis=-1						)
							snake_case_		   =					tf.concat([attn_mask, tf.ones((shape_list(__lowercase						)[0], 1)	,     dtype=tf.intaa						)]	,     axis=1						)

							# get two different outputs
							snake_case_		   =					model(__lowercase	,     attention_mask=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(__lowercase	,     past_key_values=__lowercase	,     attention_mask=__lowercase						)[
							    "last_hidden_state"
							]

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-1]						)						)
							snake_case_		   =					output_from_no_past[:, -1, random_slice_idx]
							snake_case_		   =					output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowercase	,     __lowercase	,     rtol=1E-12						)






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					Any	,     __lowercase							:					int	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Tuple	,     *__lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel(config=__lowercase						)

							snake_case_		   =					input_ids[:1, :]
							snake_case_		   =					input_mask[:1, :]
							snake_case_		   =					token_type_ids[:1, :]
							snake_case_		   =					1

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

							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_		   =					ids_tensor((self.batch_size, 3)	,     2						)
							snake_case_		   =					ids_tensor((self.batch_size, 3)	,     self.type_vocab_size						)

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

							snake_case_		   =					model(
							    __lowercase	,     token_type_ids=__lowercase	,     attention_mask=__lowercase						)["last_hidden_state"]
							snake_case_		   =					model(
							    __lowercase	,     token_type_ids=__lowercase	,     attention_mask=__lowercase	,     past_key_values=__lowercase	,     )["last_hidden_state"]
							self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]						)

							# select random slice
							snake_case_		   =					int(ids_tensor((1,)	,     shape_list(__lowercase						)[-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(__lowercase	,     __lowercase	,     rtol=1E-3						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Any	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					int	,     *__lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM(config=__lowercase						)
							snake_case_		   =					{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							snake_case_		   =					model(__lowercase						)
							self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.seq_length, self.vocab_size)						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.prepare_config_and_inputs()

							(
							    (
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,				(
							    snake_case_				
							)   ,								
							)		   =					config_and_inputs

							snake_case_		   =					{
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_tf
class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (
						    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
						    if is_tf_available()
						    else ()
						)

						lowerCAmelCase_							=       (TFGPTJForCausalLM,) if is_tf_available() else ()
						lowerCAmelCase_							=       (
						    {
						        '''feature-extraction''': TFGPTJModel,
						        '''question-answering''': TFGPTJForQuestionAnswering,
						        '''text-classification''': TFGPTJForSequenceClassification,
						        '''text-generation''': TFGPTJForCausalLM,
						        '''zero-shot''': TFGPTJForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					Any	,     __lowercase							:					str	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Dict						):






							"""simple docstring"""






							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"						)
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModelTester(self						)
							snake_case_		   =					ConfigTester(self	,     config_class=__lowercase	,     n_embd=37						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model(*__lowercase						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past(*__lowercase						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_attention_mask_past(*__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past_large_inputs(*__lowercase						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_lm_head_model(*__lowercase						)






						@slow
						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) > 0	,     "skip testing on GPU for now to avoid GPU OOM."	,     )
						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"	,     from_pt=__lowercase						)
							self.assertIsNotNone(__lowercase						)






						@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."						)
						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     from_pt=__lowercase						)
							snake_case_		   =					tf.convert_to_tensor([[4_64, 32_90]]	,     dtype=tf.intaa						)  # The dog
							# fmt: off
							# The dog is a man's best friend. It is a loyal companion, and it is a friend
							snake_case_		   =					[4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
							# fmt: on
							snake_case_		   =					model.generate(__lowercase	,     do_sample=__lowercase						)
							self.assertListEqual(output_ids[0].numpy().tolist()	,     __lowercase						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"						)
							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"	,     from_pt=__lowercase						)

							snake_case_		   =					tokenizer("Today is a nice day and"	,     return_tensors="tf"						)
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"						):
								snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     seed=[42, 0]						)
							snake_case_		   =					tokenizer.decode(output_ids[0]	,     skip_special_tokens=__lowercase						)

							snake_case_		   =					"Today is a nice day and I’m going to go for a walk. I’"
							self.assertEqual(__lowercase	,     __lowercase						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"	,     from_pt=__lowercase						)
							snake_case_		   =					AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,     revision="float16"						)

							snake_case_		   =					"left"

							# Define PAD Token = EOS Token = 50256
							snake_case_		   =					tokenizer.eos_token
							snake_case_		   =					model.config.eos_token_id

							# use different length sentences to test batching
							snake_case_		   =					[
							    "Hello, my dog is a little",
							    "Today, I",
							]
							snake_case_		   =					[
							    "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
							    "Today, I’m going to be talking about a topic that’",
							]
							return model, tokenizer, sentences, expected_output_sentences






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					tokenizer.batch_decode(__lowercase	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     __lowercase						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					tokenizer(sentences[0]	,     return_tensors="tf"						)
							snake_case_		   =					model.generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					(
							    shape_list(inputs_non_padded["input_ids"]						)[-1]
							    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]	,     tf.intaa						)						).numpy()
							)
							snake_case_		   =					tokenizer(sentences[1]	,     return_tensors="tf"						)
							snake_case_		   =					model.generate(
							    **__lowercase	,     do_sample=__lowercase	,     num_beams=2	,     max_length=model.config.max_length - num_paddings						)
							snake_case_		   =					tokenizer.decode(output_non_padded[0]	,     skip_special_tokens=__lowercase						)
							snake_case_		   =					tokenizer.decode(output_padded[0]	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     [non_padded_sentence, padded_sentence]						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					self._get_beam_search_test_objects()

							snake_case_		   =					tokenizer(__lowercase	,     return_tensors="tf"	,     padding=__lowercase						)
							snake_case_		   =					tf.function(model.generate	,     jit_compile=__lowercase						)
							snake_case_		   =					xla_generate(**__lowercase	,     do_sample=__lowercase	,     num_beams=2						)
							snake_case_		   =					tokenizer.batch_decode(__lowercase	,     skip_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase	,     __lowercase						)






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.OneCycleLR(_A					,     max_lr=0.01					,     steps_per_epoch=2					,     epochs=1							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.LambdaLR(_A					,     lr_lambda=lambda _A							: 1 - n / 10							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	snake_case_		   =					False
	scheduler.step()
	snake_case_		   =					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

	# Optimizer has not stepped
	snake_case_		   =					True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case_		   =					1 - 2 / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"


def 						lowerCamelCase__   (		_A = 2							):







	'''simple docstring'''





	from transformers import get_linear_schedule_with_warmup

	snake_case_		   =					[1, 2, 4]
	for num_steps in steps:
		snake_case_		   =					GradientAccumulationPlugin(num_steps=_A					,     adjust_scheduler=num_steps > 1							)
		snake_case_		   =					Accelerator(gradient_accumulation_plugin=_A							)
		snake_case_		   =					torch.nn.Linear(2					,     4							)
		snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=10.0							)
		snake_case_		   =					get_linear_schedule_with_warmup(optimizer=_A					,     num_warmup_steps=0					,     num_training_steps=20							)

		snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

		for i in range(10 * num_steps							):
			with accelerator.accumulate(_A							):
				optimizer.step()
				scheduler.step()

			if i == (10 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), f"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
		assert (
		    scheduler.get_last_lr()[0] == 0
		), f"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
		GradientState._reset_state()






@require_cpu
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						@require_huggingface_suite
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1						)						)
							debug_launcher(__lowercase						)
