

'''simple docstring'''





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= fname.split(os.path.sep					)[-1]
						return re.search(R"^(.*)_\d+\.jpg$"       ,			_UpperCAmelCase					).groups()[0]



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def __init__(				self						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  Any=None):
									SCREAMING_SNAKE_CASE_:				List[Any]							= file_names
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= image_transform
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= label_to_id



			def __len__(				self						:  Union[str, Any]):
									return len(self.file_names)




			def __getitem__(				self						:  Tuple     ,		lowerCAmelCase__						:  str):
									SCREAMING_SNAKE_CASE_:				Any							= self.file_names[idx]
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= PIL.Image.open(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= raw_image.convert("RGB")
									if self.image_transform is not None:
															SCREAMING_SNAKE_CASE_:				List[Any]							= self.image_transform(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= extract_label(lowerCAmelCase__)
									if self.label_to_id is not None:
															SCREAMING_SNAKE_CASE_:				List[str]							= self.label_to_id[label]
									return {"image": image, "label": label}







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_:				Dict							= Accelerator(cpu=args.cpu       ,			mixed_precision=args.mixed_precision					)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_:				List[Any]							= config["lr"]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= int(config["num_epochs"]					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= int(config["seed"]					)
						SCREAMING_SNAKE_CASE_:				str							= int(config["batch_size"]					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= config["image_size"]
						if not isinstance(_UpperCAmelCase       ,			(list, tuple)					):
												SCREAMING_SNAKE_CASE_:				Optional[int]							= (image_size, image_size)

						# Grab all the image filenames
						SCREAMING_SNAKE_CASE_:				Optional[int]							= [os.path.join(args.data_dir       ,			_UpperCAmelCase					) for fname in os.listdir(args.data_dir					) if fname.endswith(".jpg"					)]

						# Build the label correspondences
						SCREAMING_SNAKE_CASE_:				str							= [extract_label(_UpperCAmelCase					) for fname in file_names]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= list(set(_UpperCAmelCase					)					)
						id_to_label.sort()
						SCREAMING_SNAKE_CASE_:				Dict							= {lbl: i for i, lbl in enumerate(_UpperCAmelCase					)}

						# Set the seed before splitting the data.
						np.random.seed(_UpperCAmelCase					)
						torch.manual_seed(_UpperCAmelCase					)
						torch.cuda.manual_seed_all(_UpperCAmelCase					)

						# Split our filenames between train and validation
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.random.permutation(len(_UpperCAmelCase					)					)
						SCREAMING_SNAKE_CASE_:				Dict							= int(0.8 * len(_UpperCAmelCase					)					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= random_perm[:cut]
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						SCREAMING_SNAKE_CASE_:				str							= Compose([RandomResizedCrop(_UpperCAmelCase       ,			scale=(0.5, 1.0)					), ToTensor()]					)
						SCREAMING_SNAKE_CASE_:				Dict							= PetsDataset(
						    [file_names[i] for i in train_split]       ,			image_transform=_UpperCAmelCase       ,			label_to_id=_UpperCAmelCase					)

						# For evaluation, we use a deterministic Resize
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= Compose([Resize(_UpperCAmelCase					), ToTensor()]					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= PetsDataset([file_names[i] for i in eval_split]       ,			image_transform=_UpperCAmelCase       ,			label_to_id=_UpperCAmelCase					)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:				List[Any]							= DataLoader(_UpperCAmelCase       ,			shuffle=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase       ,			num_workers=4					)
						SCREAMING_SNAKE_CASE_:				str							= DataLoader(_UpperCAmelCase       ,			shuffle=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase       ,			num_workers=4					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_:				str							= create_model("resnet50d"       ,			pretrained=_UpperCAmelCase       ,			num_classes=len(_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_:				int							= model.to(accelerator.device					)

						# Freezing the base model
						for param in model.parameters():
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= False
						for param in model.get_classifier().parameters():
												SCREAMING_SNAKE_CASE_:				Dict							= True

						# We normalize the batches of images to be a bit faster.
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.tensor(model.default_cfg["mean"]					)[None, :, None, None].to(accelerator.device					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= torch.tensor(model.default_cfg["std"]					)[None, :, None, None].to(accelerator.device					)

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_:				Any							= torch.optim.Adam(params=model.parameters()       ,			lr=lr / 25					)

						# Instantiate learning rate scheduler
						SCREAMING_SNAKE_CASE_:				Tuple							= OneCycleLR(optimizer=_UpperCAmelCase       ,			max_lr=_UpperCAmelCase       ,			epochs=_UpperCAmelCase       ,			steps_per_epoch=len(_UpperCAmelCase					)					)

						# 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_:				Optional[int]							= accelerator.prepare(
						    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)

						# Now we train the model
						for epoch in range(_UpperCAmelCase					):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase					):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		SCREAMING_SNAKE_CASE_:				List[str]							= {k: v.to(accelerator.device					) for k, v in batch.items()}
																		SCREAMING_SNAKE_CASE_:				Dict							= (batch["image"] - mean) / std
																		SCREAMING_SNAKE_CASE_:				int							= model(_UpperCAmelCase					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= torch.nn.functional.cross_entropy(_UpperCAmelCase       ,			batch["label"]					)
																		accelerator.backward(_UpperCAmelCase					)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												SCREAMING_SNAKE_CASE_:				List[Any]							= 0
												SCREAMING_SNAKE_CASE_:				List[Any]							= 0
												for _, batch in enumerate(_UpperCAmelCase					):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		SCREAMING_SNAKE_CASE_:				str							= {k: v.to(accelerator.device					) for k, v in batch.items()}
																		SCREAMING_SNAKE_CASE_:				int							= (batch["image"] - mean) / std
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_:				Tuple							= model(_UpperCAmelCase					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= outputs.argmax(dim=-1					)
																		SCREAMING_SNAKE_CASE_:				Union[str, Any]							= accelerator.gather_for_metrics((predictions, batch["label"])					)
																		SCREAMING_SNAKE_CASE_:				List[str]							= predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												SCREAMING_SNAKE_CASE_:				Dict							= accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}: {1_00 * eval_metric:.2f}"					)







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= argparse.ArgumentParser(description="Simple example of training script."					)
						parser.add_argument("--data_dir"       ,			required=_UpperCAmelCase       ,			help="The data folder on disk."					)
						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."       ,			)
						parser.add_argument(
						    "--checkpointing_steps"       ,			type=_UpperCAmelCase       ,			default=_UpperCAmelCase       ,			help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,			)
						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_:				Any							= {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
						training_function(_UpperCAmelCase       ,			_UpperCAmelCase					)


if __name__ == "__main__":
						main()









import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch



class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[Any]=14     ,		lowerCAmelCase__						:  List[Any]=7     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  int=True     ,		lowerCAmelCase__						:  str=False     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  int=99     ,		lowerCAmelCase__						:  List[str]=32     ,		lowerCAmelCase__						:  Dict=5     ,		lowerCAmelCase__						:  Union[str, Any]=4     ,		lowerCAmelCase__						:  List[Any]=37     ,		lowerCAmelCase__						:  int="gelu"     ,		lowerCAmelCase__						:  List[Any]=0.1     ,		lowerCAmelCase__						:  Optional[int]=0.1     ,		lowerCAmelCase__						:  Optional[int]=512     ,		lowerCAmelCase__						:  Any=0.02     ,		):
									SCREAMING_SNAKE_CASE_:				Tuple							= parent
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= batch_size
									SCREAMING_SNAKE_CASE_:				Tuple							= seq_length
									SCREAMING_SNAKE_CASE_:				Dict							= is_training
									SCREAMING_SNAKE_CASE_:				Dict							= use_input_mask
									SCREAMING_SNAKE_CASE_:				Any							= use_token_type_ids
									SCREAMING_SNAKE_CASE_:				Dict							= use_labels
									SCREAMING_SNAKE_CASE_:				List[str]							= vocab_size
									SCREAMING_SNAKE_CASE_:				str							= hidden_size
									SCREAMING_SNAKE_CASE_:				List[Any]							= num_hidden_layers
									SCREAMING_SNAKE_CASE_:				Optional[int]							= num_attention_heads
									SCREAMING_SNAKE_CASE_:				Any							= intermediate_size
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= hidden_act
									SCREAMING_SNAKE_CASE_:				List[Any]							= hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:				Dict							= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:				Dict							= max_position_embeddings
									SCREAMING_SNAKE_CASE_:				List[str]							= initializer_range
									SCREAMING_SNAKE_CASE_:				Any							= None
									SCREAMING_SNAKE_CASE_:				int							= vocab_size - 1
									SCREAMING_SNAKE_CASE_:				List[Any]							= vocab_size - 1
									SCREAMING_SNAKE_CASE_:				Any							= vocab_size - 1



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

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

									SCREAMING_SNAKE_CASE_:				Any							= GPTaConfig(
									    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     ,		)

									return (config, input_ids, input_mask)



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				str							= self.prepare_config_and_inputs()

									SCREAMING_SNAKE_CASE_:				List[Any]							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
									SCREAMING_SNAKE_CASE_:				List[str]							= ids_tensor([self.batch_size, self.seq_length]     ,		vocab_size=2)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



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

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

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

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

									SCREAMING_SNAKE_CASE_:				Tuple							= model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				str							= 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						:  List[str]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				Any							= 20
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model_class_name(lowerCAmelCase__)

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

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

									SCREAMING_SNAKE_CASE_:				Union[str, 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[Any]							= model(
									    input_ids[:, -1:]     ,		past_key_values=outputs_cache.past_key_values     ,		attention_mask=lowerCAmelCase__     ,		position_ids=lowerCAmelCase__     ,		)

									SCREAMING_SNAKE_CASE_:				Any							= model(lowerCAmelCase__     ,		attention_mask=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}")







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




			"""simple docstring"""







			_UpperCAmelCase     :							str    							=  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			_UpperCAmelCase     :							Optional[Any]    							=  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Tuple							= FlaxGPTaModelTester(self)



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



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



			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= GPTaTokenizer.from_pretrained("gpt2"     ,		pad_token="</s>"     ,		padding_side="left")
									SCREAMING_SNAKE_CASE_:				Any							= tokenizer(["Hello this is a long string", "Hey"]     ,		return_tensors="np"     ,		padding=lowerCAmelCase__     ,		truncation=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				str							= FlaxGPTaLMHeadModel.from_pretrained("gpt2")
									SCREAMING_SNAKE_CASE_:				Dict							= False
									SCREAMING_SNAKE_CASE_:				Dict							= model.config.eos_token_id

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

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= jit_generate(inputs["input_ids"]     ,		attention_mask=inputs["attention_mask"]).sequences

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

									SCREAMING_SNAKE_CASE_:				Any							= [
									    "Hello this is a long string of words. I'm going to start with the first one.\n",
									    "Hey, I'm not sure if I'm going to be able to do",
									]

									self.assertListEqual(lowerCAmelCase__     ,		lowerCAmelCase__)



			@is_pt_flax_cross_test
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				int							= 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_:				List[str]							= self._prepare_for_class(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				List[str]							= {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																					SCREAMING_SNAKE_CASE_:				Dict							= 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_:				Tuple							= pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:				Dict							= 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_:				List[str]							= model_class.from_pretrained(lowerCAmelCase__     ,		from_pt=lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:				int							= 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						:  int):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= 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_:				str							= self._prepare_for_class(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				Optional[Any]							= {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_:				List[str]							= getattr(lowerCAmelCase__     ,		lowerCAmelCase__)

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

																					SCREAMING_SNAKE_CASE_:				Optional[int]							= load_flax_weights_in_pytorch_model(lowerCAmelCase__     ,		fx_model.params)
																					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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_:				Any							= 0
																											SCREAMING_SNAKE_CASE_:				List[Any]							= 1
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 0
																											SCREAMING_SNAKE_CASE_:				Optional[Any]							= 1

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

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:				List[Any]							= pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:				Union[str, 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_:				List[Any]							= pt_model_class.from_pretrained(lowerCAmelCase__     ,		from_flax=lowerCAmelCase__)

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:				Union[str, 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)




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:				List[str]							= model_class_name.from_pretrained("gpt2"     ,		from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				int							= model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)






