

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch






class 			__magic_name__					:





			def __init__(	self      ,						__snake_case      ,						__snake_case=14      ,						__snake_case=7      ,						__snake_case=True      ,						__snake_case=True      ,						__snake_case=False      ,						__snake_case=True      ,						__snake_case=99      ,						__snake_case=32      ,						__snake_case=4      ,						__snake_case=4      ,						__snake_case=[[["global", "local"], 2]]      ,						__snake_case=37      ,						__snake_case="gelu"      ,						__snake_case=0.1      ,						__snake_case=0.1      ,						__snake_case=512      ,						__snake_case=7      ,						__snake_case=0.02      ,						)     ->				Dict:




							'''simple docstring'''
							__a						   =parent
							__a						   =batch_size
							__a						   =seq_length
							__a						   =is_training
							__a						   =use_input_mask
							__a						   =use_token_type_ids
							__a						   =use_labels
							__a						   =vocab_size
							__a						   =hidden_size
							__a						   =num_hidden_layers
							__a						   =num_attention_heads
							__a						   =attention_types
							__a						   =intermediate_size
							__a						   =hidden_act
							__a						   =hidden_dropout_prob
							__a						   =attention_probs_dropout_prob
							__a						   =max_position_embeddings
							__a						   =window_size
							__a						   =initializer_range
							__a						   =None
							__a						   =vocab_size - 1
							__a						   =vocab_size - 1
							__a						   =vocab_size - 1





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




							'''simple docstring'''
							__a						   =ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size      )

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

							__a						   =GPTNeoConfig(
							    vocab_size=self.vocab_size      ,						hidden_size=self.hidden_size      ,						num_layers=self.num_hidden_layers      ,						num_heads=self.num_attention_heads      ,						max_position_embeddings=self.max_position_embeddings      ,						use_cache=__snake_case      ,						bos_token_id=self.bos_token_id      ,						eos_token_id=self.eos_token_id      ,						pad_token_id=self.pad_token_id      ,						window_size=self.window_size      ,						attention_types=self.attention_types      ,						)

							return (config, input_ids, input_mask)





			def 	__magic_name__					(	self      )     ->				Any:




							'''simple docstring'''
							__a						   =self.prepare_config_and_inputs()
							__a   ,					__a   ,					__a						   =config_and_inputs
							__a						   ={'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Tuple:




							'''simple docstring'''
							__a						   =20
							__a						   =model_class_name(__snake_case      )

							__a						   =model.init_cache(input_ids.shape[0]      ,						__snake_case      )
							__a						   =jnp.ones((input_ids.shape[0], max_decoder_length)      ,						dtype='i4'      )

							__a						   =jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1      )[None, :]      ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )
							__a						   =model(
							    input_ids[:, :-1]      ,						attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						position_ids=__snake_case      ,						)

							__a						   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
							__a						   =model(
							    input_ids[:, -1:]      ,						attention_mask=__snake_case      ,						past_key_values=outputs_cache.past_key_values      ,						position_ids=__snake_case      ,						)

							__a						   =model(__snake_case      )

							__a						   =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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Dict:




							'''simple docstring'''
							__a						   =20
							__a						   =model_class_name(__snake_case      )

							__a						   =jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]      ,						axis=-1      ,						)

							__a						   =model.init_cache(input_ids.shape[0]      ,						__snake_case      )
							__a						   =jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1      )[None, :]      ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )

							__a						   =model(
							    input_ids[:, :-1]      ,						attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						position_ids=__snake_case      ,						)
							__a						   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
							__a						   =model(
							    input_ids[:, -1:]      ,						past_key_values=outputs_cache.past_key_values      ,						attention_mask=__snake_case      ,						position_ids=__snake_case      ,						)

							__a						   =model(__snake_case      ,						attention_mask=__snake_case      )

							__a						   =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 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_						,					unittest.TestCase   ):
			SCREAMING_SNAKE_CASE							     =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			SCREAMING_SNAKE_CASE							     =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





			def 	__magic_name__					(	self      )     ->				str:




							'''simple docstring'''
							__a						   =FlaxGPTNeoModelTester(self      )





			def 	__magic_name__					(	self      )     ->				Union[str, Any]:




							'''simple docstring'''
							for model_class_name in self.all_model_classes:
											__a   ,					__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




							'''simple docstring'''
							for model_class_name in self.all_model_classes:
											__a   ,					__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )





			@slow
			def 	__magic_name__					(	self      )     ->				List[Any]:




							'''simple docstring'''
							__a						   =GPTaTokenizer.from_pretrained('gpt2'      ,						pad_token='<|endoftext|>'      ,						padding_side='left'      )
							__a						   =tokenizer(['Hello this is a long string', 'Hey']      ,						return_tensors='np'      ,						padding=__snake_case      ,						truncation=__snake_case      )

							__a						   =FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'      )
							__a						   =False
							__a						   =model.config.eos_token_id

							__a						   =jax.jit(model.generate      )

							__a						   =jit_generate(
							    inputs['input_ids']      ,						attention_mask=inputs['attention_mask']      ,						pad_token_id=tokenizer.pad_token_id      ).sequences

							__a						   =tokenizer.batch_decode(__snake_case      ,						skip_special_tokens=__snake_case      )

							__a						   =[
							    '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(__snake_case      ,						__snake_case      )





			@is_pt_flax_cross_test
			def 	__magic_name__					(	self      )     ->				List[str]:




							'''simple docstring'''
							__a   ,					__a						   =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
															__a						   =self._prepare_for_class(__snake_case      ,						__snake_case      )
															__a						   ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															__a						   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
															__a						   =getattr(__snake_case      ,						__snake_case      )

															__a   ,					__a						   =pt_inputs['input_ids'].shape
															__a						   =np.random.randint(0      ,						seq_length - 1      ,						size=(batch_size,)      )
															for batch_idx, start_index in enumerate(__snake_case      ):
																			__a						   =0
																			__a						   =1
																			__a						   =0
																			__a						   =1
															__a						   =pt_model_class(__snake_case      ).eval()
															__a						   =model_class(__snake_case      ,						dtype=jnp.floataa      )

															__a						   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,						__snake_case      )
															__a						   =fx_state

															with torch.no_grad():
																			__a						   =pt_model(**__snake_case      ).to_tuple()

															__a						   =fx_model(**__snake_case      ).to_tuple()
															self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
															for fx_output, pt_output in zip(__snake_case      ,						__snake_case      ):
																			self.assert_almost_equals(fx_output[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )

															with tempfile.TemporaryDirectory() as tmpdirname:
																			pt_model.save_pretrained(__snake_case      )
																			__a						   =model_class.from_pretrained(__snake_case      ,						from_pt=__snake_case      )

															__a						   =fx_model_loaded(**__snake_case      ).to_tuple()
															self.assertEqual(
															    len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
															for fx_output_loaded, pt_output in zip(__snake_case      ,						__snake_case      ):
																			self.assert_almost_equals(fx_output_loaded[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )





			@is_pt_flax_cross_test
			def 	__magic_name__					(	self      )     ->				Tuple:




							'''simple docstring'''
							__a   ,					__a						   =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
															__a						   =self._prepare_for_class(__snake_case      ,						__snake_case      )
															__a						   ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															__a						   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
															__a						   =getattr(__snake_case      ,						__snake_case      )

															__a						   =pt_model_class(__snake_case      ).eval()
															__a						   =model_class(__snake_case      ,						dtype=jnp.floataa      )

															__a						   =load_flax_weights_in_pytorch_model(__snake_case      ,						fx_model.params      )
															__a   ,					__a						   =pt_inputs['input_ids'].shape
															__a						   =np.random.randint(0      ,						seq_length - 1      ,						size=(batch_size,)      )
															for batch_idx, start_index in enumerate(__snake_case      ):
																			__a						   =0
																			__a						   =1
																			__a						   =0
																			__a						   =1

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

															with torch.no_grad():
																			__a						   =pt_model(**__snake_case      ).to_tuple()

															__a						   =fx_model(**__snake_case      ).to_tuple()
															self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
															for fx_output, pt_output in zip(__snake_case      ,						__snake_case      ):
																			self.assert_almost_equals(fx_output[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )

															with tempfile.TemporaryDirectory() as tmpdirname:
																			fx_model.save_pretrained(__snake_case      )
																			__a						   =pt_model_class.from_pretrained(__snake_case      ,						from_flax=__snake_case      )

															with torch.no_grad():
																			__a						   =pt_model_loaded(**__snake_case      ).to_tuple()

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





			@slow
			def 	__magic_name__					(	self      )     ->				List[str]:




							'''simple docstring'''
							for model_class_name in self.all_model_classes:
											__a						   =model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'      )
											__a						   =model(np.ones((1, 1)      )      )
											self.assertIsNotNone(__snake_case      )









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  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






			__a						   =fname.split(os.path.sep			)[-1]
			return re.search(r'^(.*)_\d+\.jpg$' ,  _snake_case			).groups()[0]






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=None      ,						__snake_case=None      )     ->				Tuple:




						'''simple docstring'''
						__a						   =file_names
						__a						   =image_transform
						__a						   =label_to_id





			def __len__(	self      )     ->				int:




						'''simple docstring'''
						return len(self.file_names      )





			def __getitem__(	self      ,						__snake_case      )     ->				List[str]:




						'''simple docstring'''
						__a						   =self.file_names[idx]
						__a						   =PIL.Image.open(__snake_case      )
						__a						   =raw_image.convert('RGB'      )
						if self.image_transform is not None:
									__a						   =self.image_transform(__snake_case      )
						__a						   =extract_label(__snake_case      )
						if self.label_to_id is not None:
									__a						   =self.label_to_id[label]
						return {"image": image, "label": label}






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Optional[int]			):




			"""simple docstring"""






			__a						   =Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision			)

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						   =config['lr']
			__a						   =int(config['num_epochs']			)
			__a						   =int(config['seed']			)
			__a						   =int(config['batch_size']			)
			__a						   =config['image_size']
			if not isinstance(_snake_case ,  (list, tuple)			):
						__a						   =(image_size, image_size)

			# Grab all the image filenames
			__a						   =[os.path.join(args.data_dir ,  _snake_case			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

			# Build the label correspondences
			__a						   =[extract_label(_snake_case			) for fname in file_names]
			__a						   =list(set(_snake_case			)			)
			id_to_label.sort()
			__a						   ={lbl: i for i, lbl in enumerate(_snake_case			)}

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

			# Split our filenames between train and validation
			__a						   =np.random.permutation(len(_snake_case			)			)
			__a						   =int(0.8 * len(_snake_case			)			)
			__a						   =random_perm[:cut]
			__a						   =random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			__a						   =Compose([RandomResizedCrop(_snake_case ,  scale=(0.5, 1.0)			), ToTensor()]			)
			__a						   =PetsDataset(
			    [file_names[i] for i in train_split] ,  image_transform=_snake_case ,  label_to_id=_snake_case			)

			# For evaluation, we use a deterministic Resize
			__a						   =Compose([Resize(_snake_case			), ToTensor()]			)
			__a						   =PetsDataset([file_names[i] for i in eval_split] ,  image_transform=_snake_case ,  label_to_id=_snake_case			)

			# Instantiate dataloaders.
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						   =create_model('resnet50d' ,  pretrained=_snake_case ,  num_classes=len(_snake_case			)			)

			# 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).
			__a						   =model.to(accelerator.device			)

			# Freezing the base model
			for param in model.parameters():
						__a						   =False
			for param in model.get_classifier().parameters():
						__a						   =True

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

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

			# Instantiate learning rate scheduler
			__a						   =OneCycleLR(optimizer=_snake_case ,  max_lr=_snake_case ,  epochs=_snake_case ,  steps_per_epoch=len(_snake_case			)			)

			# 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.
			__a   ,					__a   ,					__a   ,					__a   ,					__a						   =accelerator.prepare(
			    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			)

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

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

						__a						   =accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'			)






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir' ,  required=_snake_case ,  help='The data folder on disk.'			)
			parser.add_argument(
			    '--mixed_precision' ,  type=_snake_case ,  default=_snake_case ,  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=_snake_case ,  default=_snake_case ,  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.'			)
			__a						   =parser.parse_args()
			__a						   ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_snake_case ,  _snake_case			)


if __name__ == "__main__":
						main()






