

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase							:  str            =						16
lowerCAmelCase							:  Any            =						32







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase = 16   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoTokenizer.from_pretrained("bert-base-cased"   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_dataset("glue"    ,     "mrpc"   )

						def tokenize_function(_UpperCAmelCase   ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer(examples["sentence1"]    ,     examples["sentence2"]    ,     truncation=_UpperCAmelCase    ,     max_length=_UpperCAmelCase   )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_:			int  		=	datasets.map(
												    _UpperCAmelCase    ,     batched=_UpperCAmelCase    ,     remove_columns=["idx", "sentence1", "sentence2"]    ,     )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenized_datasets.rename_column("label"    ,     "labels"   )

						def collate_fn(_UpperCAmelCase   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		SCREAMING_SNAKE_CASE_:			Any  		=	16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_:			Any  		=	8
												else:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	None

												return tokenizer.pad(
												    _UpperCAmelCase    ,     padding="longest"    ,     max_length=_UpperCAmelCase    ,     pad_to_multiple_of=_UpperCAmelCase    ,     return_tensors="pt"    ,     )

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:			Any  		=	DataLoader(
						    tokenized_datasets["train"]    ,     shuffle=_UpperCAmelCase    ,     collate_fn=_UpperCAmelCase    ,     batch_size=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(
						    tokenized_datasets["validation"]    ,     shuffle=_UpperCAmelCase    ,     collate_fn=_UpperCAmelCase    ,     batch_size=_UpperCAmelCase   )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						lowerCAmelCase							:  List[Any]            =						mocked_dataloaders  # noqa: F811







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						# For testing only
						if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,     _UpperCAmelCase   ) == "1":
												SCREAMING_SNAKE_CASE_:			Dict  		=	2
						# New Code #
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	int(args.gradient_accumulation_steps   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	int(args.local_sgd_steps   )
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_:			int  		=	Accelerator(
						    cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     gradient_accumulation_steps=_UpperCAmelCase   )
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"   )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_:			str  		=	config["lr"]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	int(config["num_epochs"]   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	int(config["seed"]   )
						SCREAMING_SNAKE_CASE_:			int  		=	int(config["batch_size"]   )

						SCREAMING_SNAKE_CASE_:			str  		=	evaluate.load("glue"    ,     "mrpc"   )

						set_seed(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_dataloaders(_UpperCAmelCase    ,     _UpperCAmelCase   )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_:			Any  		=	AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,     return_dict=_UpperCAmelCase   )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						SCREAMING_SNAKE_CASE_:			Any  		=	model.to(accelerator.device   )

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	AdamW(params=model.parameters()    ,     lr=_UpperCAmelCase   )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase    ,     num_warmup_steps=1_00    ,     num_training_steps=(len(_UpperCAmelCase   ) * num_epochs)    ,     )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	accelerator.prepare(
						    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

						# Now we train the model
						for epoch in range(_UpperCAmelCase   ):
												model.train()
												with LocalSGD(
												    accelerator=_UpperCAmelCase    ,     model=_UpperCAmelCase    ,     local_sgd_steps=_UpperCAmelCase    ,     enabled=local_sgd_steps is not None   ) as local_sgd:
																		for step, batch in enumerate(_UpperCAmelCase   ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device   )
																								# New code #
																								# We use the new `accumulate` context manager to perform gradient accumulation
																								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																								with accelerator.accumulate(_UpperCAmelCase   ):
																														SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	model(**_UpperCAmelCase   )
																														SCREAMING_SNAKE_CASE_:			Dict  		=	output.loss
																														accelerator.backward(_UpperCAmelCase   )
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												model.eval()
												for step, batch in enumerate(_UpperCAmelCase   ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device   )
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(**_UpperCAmelCase   )
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	outputs.logits.argmax(dim=-1   )
																		SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	accelerator.gather_for_metrics((predictions, batch["labels"])   )
																		metric.add_batch(
																		    predictions=_UpperCAmelCase    ,     references=_UpperCAmelCase    ,     )

												SCREAMING_SNAKE_CASE_:			Any  		=	metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"    ,     _UpperCAmelCase   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	argparse.ArgumentParser(description="Simple example of training script."   )
						parser.add_argument(
						    "--mixed_precision"    ,     type=_UpperCAmelCase    ,     default=_UpperCAmelCase    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU."    ,     )
						# New Code #
						parser.add_argument(
						    "--gradient_accumulation_steps"    ,     type=_UpperCAmelCase    ,     default=1    ,     help="The number of minibatches to be ran before gradients are accumulated."    ,     )
						parser.add_argument(
						    "--local_sgd_steps"    ,     type=_UpperCAmelCase    ,     default=8    ,     help="Number of local SGD steps or None to disable local SGD"   )
						parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU."   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	parser.parse_args()
						SCREAMING_SNAKE_CASE_:			int  		=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase    ,     _UpperCAmelCase   )


if __name__ == "__main__":
						main()









import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
						import torch



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict=14       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					Optional[int]=True       ,						lowerCAmelCase__  :					Optional[int]=True       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					Optional[int]=99       ,						lowerCAmelCase__  :					str=32       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Dict=37       ,						lowerCAmelCase__  :					Tuple="gelu"       ,						lowerCAmelCase__  :					str=0.1       ,						lowerCAmelCase__  :					Optional[Any]=0.1       ,						lowerCAmelCase__  :					Dict=512       ,						lowerCAmelCase__  :					Dict=0.02       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	parent
									SCREAMING_SNAKE_CASE_:			Any  		=	batch_size
									SCREAMING_SNAKE_CASE_:			str  		=	seq_length
									SCREAMING_SNAKE_CASE_:			Dict  		=	is_training
									SCREAMING_SNAKE_CASE_:			str  		=	use_input_mask
									SCREAMING_SNAKE_CASE_:			int  		=	use_token_type_ids
									SCREAMING_SNAKE_CASE_:			Tuple  		=	use_labels
									SCREAMING_SNAKE_CASE_:			int  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	rotary_dim
									SCREAMING_SNAKE_CASE_:			Dict  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Dict  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	None
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	vocab_size - 1
									SCREAMING_SNAKE_CASE_:			Tuple  		=	vocab_size - 1
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	vocab_size - 1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size)

									SCREAMING_SNAKE_CASE_:			Any  		=	None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	GPTJConfig(
									    vocab_size=self.vocab_size       ,						n_embd=self.hidden_size       ,						n_layer=self.num_hidden_layers       ,						n_head=self.num_attention_heads       ,						n_positions=self.max_position_embeddings       ,						use_cache=lowerCAmelCase__       ,						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 (config, input_ids, input_mask)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	config_and_inputs
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	20
									SCREAMING_SNAKE_CASE_:			Any  		=	model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	model.init_cache(input_ids.shape[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	jnp.ones((input_ids.shape[0], max_decoder_length)       ,						dtype="i4")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,						(input_ids.shape[0], input_ids.shape[-1] - 1))
									SCREAMING_SNAKE_CASE_:			int  		=	model(
									    input_ids[:, :-1]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,						dtype="i4")
									SCREAMING_SNAKE_CASE_:			Dict  		=	model(
									    input_ids[:, -1:]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3       ,						msg=F"Max diff is {diff}")




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			int  		=	20
									SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]       ,						axis=-1       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	model.init_cache(input_ids.shape[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,						(input_ids.shape[0], input_ids.shape[-1] - 1))

									SCREAMING_SNAKE_CASE_:			Any  		=	model(
									    input_ids[:, :-1]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			int  		=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,						dtype="i4")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(
									    input_ids[:, -1:]       ,						past_key_values=outputs_cache.past_key_values       ,						attention_mask=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(lowerCAmelCase__       ,						attention_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3       ,						msg=F"Max diff is {diff}")







@require_flax
class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			_UpperCAmelCase       :							Tuple      							=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	FlaxGPTJModelTester(self)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)



			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	GPTaTokenizer.from_pretrained("gpt2"       ,						pad_token="<|endoftext|>"       ,						padding_side="left")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer(["Hello this is a long string", "Hey"]       ,						return_tensors="np"       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
									SCREAMING_SNAKE_CASE_:			int  		=	False
									SCREAMING_SNAKE_CASE_:			str  		=	model.config.eos_token_id

									SCREAMING_SNAKE_CASE_:			int  		=	jax.jit(model.generate)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jit_generate(
									    inputs["input_ids"]       ,						attention_mask=inputs["attention_mask"]       ,						pad_token_id=tokenizer.pad_token_id).sequences

									SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.batch_decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
									    "Hey, I'm a little late to the party. I'm going to",
									]

									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	self._prepare_for_class(lowerCAmelCase__       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:			int  		=	getattr(lowerCAmelCase__       ,						lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.random.randint(0       ,						seq_length - 1       ,						size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:			Dict  		=	0
																											SCREAMING_SNAKE_CASE_:			str  		=	1
																											SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0
																											SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class(lowerCAmelCase__       ,						dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_state

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			int  		=	model_class.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_model_loaded(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(
																					    len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output_loaded, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output_loaded[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)



			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._prepare_for_class(lowerCAmelCase__       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:			Any  		=	getattr(lowerCAmelCase__       ,						lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model_class(lowerCAmelCase__       ,						dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:			int  		=	load_flax_weights_in_pytorch_model(lowerCAmelCase__       ,						fx_model.params)
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:			Any  		=	np.random.randint(0       ,						seq_length - 1       ,						size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:			str  		=	0
																											SCREAMING_SNAKE_CASE_:			Tuple  		=	1
																											SCREAMING_SNAKE_CASE_:			Dict  		=	0
																											SCREAMING_SNAKE_CASE_:			str  		=	1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			Any  		=	pt_model_class.from_pretrained(lowerCAmelCase__       ,						from_flax=lowerCAmelCase__)

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Any  		=	pt_model_loaded(**lowerCAmelCase__).to_tuple()

																					self.assertEqual(
																					    len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)




			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	model_class_name.from_pretrained("EleutherAI/gpt-j-6B")
															SCREAMING_SNAKE_CASE_:			str  		=	model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)






