
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_			(				snake_case				: Union[str, Any]			)   ->     Optional[Any]:



						'''simple docstring'''

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      file_names
													__UpperCamelCase  	=      image_transform
													__UpperCamelCase  	=      label_to_id



							def __len__(	self      )-> Any:





													'''simple docstring'''






													return len(self.file_names      )



							def __getitem__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






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





def 						A_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     int:



						'''simple docstring'''

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

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

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

						# Build the label correspondences
						__UpperCamelCase  	=      [extract_label(snake_case			) for fname in file_names]
						__UpperCamelCase  	=      list(set(snake_case			)			)
						id_to_label.sort()
						__UpperCamelCase  	=      {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
						__UpperCamelCase  	=      np.random.permutation(len(snake_case			)			)
						__UpperCamelCase  	=      int(0.8 * len(snake_case			)			)
						__UpperCamelCase  	=      random_perm[:cut]
						__UpperCamelCase  	=      random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						__UpperCamelCase  	=      Compose([RandomResizedCrop(snake_case			,   scale=(0.5, 1.0)			), ToTensor()]			)
						__UpperCamelCase  	=      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
						__UpperCamelCase  	=      Compose([Resize(snake_case			), ToTensor()]			)
						__UpperCamelCase  	=      PetsDataset([file_names[i] for i in eval_split]			,   image_transform=snake_case			,   label_to_id=snake_case			)

						# Instantiate dataloaders.
						__UpperCamelCase  	=      DataLoader(snake_case			,   shuffle=snake_case			,   batch_size=snake_case			,   num_workers=4			)
						__UpperCamelCase  	=      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)
						__UpperCamelCase  	=      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).
						__UpperCamelCase  	=      model.to(accelerator.device			)

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

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

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

						# Instantiate learning rate scheduler
						__UpperCamelCase  	=      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.
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      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`.
																		__UpperCamelCase  	=      {k: v.to(accelerator.device			) for k, v in batch.items()}
																		__UpperCamelCase  	=      (batch['''image'''] - mean) / std
																		__UpperCamelCase  	=      model(snake_case			)
																		__UpperCamelCase  	=      torch.nn.functional.cross_entropy(snake_case			,   batch['''label''']			)
																		accelerator.backward(snake_case			)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

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

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




def 						A_			(				)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      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.'''			)
						__UpperCamelCase  	=      parser.parse_args()
						__UpperCamelCase  	=      {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
						training_function(snake_case			,   snake_case			)


if __name__ == "__main__":
				main()






import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=13  ,						SCREAMING_SNAKE_CASE_=(32, 128)  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=27  ,						SCREAMING_SNAKE_CASE_=38  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=4.0  ,						SCREAMING_SNAKE_CASE_=257  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      patch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      max_token_length
													__UpperCamelCase  	=      num_character_labels
													__UpperCamelCase  	=      num_bpe_labels
													__UpperCamelCase  	=      num_wordpiece_labels
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      mlp_ratio
													__UpperCamelCase  	=      patch_embeds_hidden_size
													__UpperCamelCase  	=      output_hidden_states



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )
													__UpperCamelCase  	=      self.get_config()
													return config, pixel_values



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return MgpstrConfig(
													    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						max_token_length=self.max_token_length  ,						num_character_labels=self.num_character_labels  ,						num_bpe_labels=self.num_bpe_labels  ,						num_wordpiece_labels=self.num_wordpiece_labels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						mlp_ratio=self.mlp_ratio  ,						output_hidden_states=self.output_hidden_states  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(
													    generated_ids[0][0].shape  ,						(self.batch_size, self.max_token_length, self.num_character_labels)      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
							_snake_case  		=	{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
							_snake_case  		=	False

							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_  ,						has_text_modality=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''      )
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)      )
																			__UpperCamelCase  	=      model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_  ,						nn.Linear      )      )



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      inspect.signature(model.forward      )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase  	=      [*signature.parameters.keys()]

																			__UpperCamelCase  	=      ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]  ,						SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''      )
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			if not model_class.supports_gradient_checkpointing:
																									continue

																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertTrue(model.is_gradient_checkpointing      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()

																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )

																			__UpperCamelCase  	=      outputs.hidden_states

																			__UpperCamelCase  	=      getattr(
																			    self.model_tester  ,						'''expected_num_hidden_layers'''  ,						self.model_tester.num_hidden_layers + 1      )
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_      )

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]      )  ,						[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]  ,						)

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      True
																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__UpperCamelCase  	=      True

																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase  	=      _config_zero_init(SCREAMING_SNAKE_CASE_      )
													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(config=SCREAMING_SNAKE_CASE_      )
																			for name, param in model.named_parameters():
																									if isinstance(SCREAMING_SNAKE_CASE_  ,						(nn.Linear, nn.Convad, nn.LayerNorm)      ):
																															if param.requires_grad:
																																					self.assertIn(
																																					    ((param.data.mean() * 1E9).round() / 1E9).item()  ,						[0.0, 1.0]  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)



							@unittest.skip(reason='''Retain_grad is tested in individual model tests'''      )
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													pass





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			).convert('''RGB'''			)
						return im






@require_vision
@require_torch
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							@slow
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''alibaba-damo/mgp-str-base'''
													__UpperCamelCase  	=      MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

													# verify the logits
													self.assertEqual(outputs.logits[0].shape  ,						torch.Size((1, 27, 38)      )      )

													__UpperCamelCase  	=      processor.batch_decode(outputs.logits      )
													__UpperCamelCase  	=      '''ticket'''

													self.assertEqual(out_strs['''generated_text'''][0]  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      torch.tensor(
													    [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]  ,						device=SCREAMING_SNAKE_CASE_  ,						)

													self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )




