code: 
  '''simple docstring'''
  
  
  
  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 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=False       ,     lowerCamelCase__=13       ,     lowerCamelCase__=(32, 128)       ,     lowerCamelCase__=4       ,     lowerCamelCase__=3       ,     lowerCamelCase__=27       ,     lowerCamelCase__=38       ,     lowerCamelCase__=99       ,     lowerCamelCase__=99       ,     lowerCamelCase__=32       ,     lowerCamelCase__=5       ,     lowerCamelCase__=4       ,     lowerCamelCase__=4.0       ,     lowerCamelCase__=257       ,     lowerCamelCase__=None       ,     )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       is_training
  									lowercase__							 =       batch_size
  									lowercase__							 =       image_size
  									lowercase__							 =       patch_size
  									lowercase__							 =       num_channels
  									lowercase__							 =       max_token_length
  									lowercase__							 =       num_character_labels
  									lowercase__							 =       num_bpe_labels
  									lowercase__							 =       num_wordpiece_labels
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       mlp_ratio
  									lowercase__							 =       patch_embeds_hidden_size
  									lowercase__							 =       output_hidden_states
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )
  									lowercase__							 =       self.get_config()
  									return config, pixel_values
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''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       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       MgpstrForSceneTextRecognition(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									with torch.no_grad():
  															lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(
  									    generated_ids[0][0].shape       ,     (self.batch_size, self.max_token_length, self.num_character_labels)       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""pixel_values""": pixel_values}
  									return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[str]       		=  (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
  			lowerCamelCase							:							Dict       		=  {"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
  			lowerCamelCase							:							Dict       		=  False
  
  			lowerCamelCase							:							str       		=  False
  			lowerCamelCase							:							List[str]       		=  False
  			lowerCamelCase							:							Union[str, Any]       		=  False
  			lowerCamelCase							:							List[str]       		=  False
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       MgpstrModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     has_text_modality=lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			@unittest.skip(reason="""MgpstrModel does not use inputs_embeds"""       )
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)       )
  															lowercase__							 =       model.get_output_embeddings()
  															self.assertTrue(x is None or isinstance(lowerCamelCase__       ,     nn.Linear       )       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															lowercase__							 =       inspect.signature(model.forward       )
  															# signature.parameters is an OrderedDict => so arg_names order is deterministic
  															lowercase__							 =       [*signature.parameters.keys()]
  
  															lowercase__							 =       ["""pixel_values"""]
  															self.assertListEqual(arg_names[:1]       ,     lowerCamelCase__       )
  
  
  
  			@unittest.skip(reason="""MgpstrModel does not support feedforward chunking"""       )
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       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
  
  															lowercase__							 =       True
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															self.assertTrue(model.is_gradient_checkpointing       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									def check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															model.to(lowerCamelCase__       )
  															model.eval()
  
  															with torch.no_grad():
  																					lowercase__							 =       model(**self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       )       )
  
  															lowercase__							 =       outputs.hidden_states
  
  															lowercase__							 =       getattr(
  															    self.model_tester       ,     """expected_num_hidden_layers"""       ,     self.model_tester.num_hidden_layers + 1       )
  															self.assertEqual(len(lowerCamelCase__       )       ,     lowerCamelCase__       )
  
  															self.assertListEqual(
  															    list(hidden_states[0].shape[-2:]       )       ,     [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]       ,     )
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       True
  															check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  															# check that output_hidden_states also work using config
  															del inputs_dict["output_hidden_states"]
  															lowercase__							 =       True
  
  															check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									lowercase__							 =       _config_zero_init(lowerCamelCase__       )
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(config=lowerCamelCase__       )
  															for name, param in model.named_parameters():
  																					if isinstance(lowerCamelCase__       ,     (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       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  
  
  def  _A    (							):
  						lowercase__							 =       """https://i.postimg.cc/ZKwLg2Gw/367-14.png"""
  						lowercase__							 =       Image.open(requests.get(__snake_case		,		stream=__snake_case	).raw	).convert("""RGB"""	)
  						return im
  
  
  
  
  
  
  
  @require_vision
  @require_torch
  class 			A							(       unittest.TestCase				):
  
  
  
  			@slow
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       """alibaba-damo/mgp-str-base"""
  									lowercase__							 =       MgpstrForSceneTextRecognition.from_pretrained(lowerCamelCase__       ).to(lowerCamelCase__       )
  									lowercase__							 =       MgpstrProcessor.from_pretrained(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).pixel_values.to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(lowerCamelCase__       )
  
  									# verify the logits
  									self.assertEqual(outputs.logits[0].shape       ,     torch.Size((1, 27, 38)       )       )
  
  									lowercase__							 =       processor.batch_decode(outputs.logits       )
  									lowercase__							 =       """ticket"""
  
  									self.assertEqual(out_strs["""generated_text"""][0]       ,     lowerCamelCase__       )
  
  									lowercase__							 =       torch.tensor(
  									    [[[-39.53_97, -44.40_24, -36.18_44], [-61.47_09, -63.86_39, -58.34_54], [-74.02_25, -68.54_94, -71.21_64]]]       ,     device=lowerCamelCase__       ,     )
  
  									self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]       ,     lowerCamelCase__       ,     atol=1e-4       )       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  __A          =    torch.cuda.is_available()
  __A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  __A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __A          =    {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       """\n""".join(lowercase__	)
  						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						for split in ["train", "val", "test"]:
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.source'''	)		,		lowercase__	)
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.target'''	)		,		lowercase__	)
  						return tmp_dir
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls
  
  
  
  			@require_torch_multi_gpu
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """no_teacher""": True,
  									    """freeze_encoder""": True,
  									    """gpus""": 2,
  									    """overwrite_output_dir""": True,
  									    """sortish_sampler""": True,
  									}
  									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """label_smoothing""": 0.0,
  									    """early_stopping_patience""": -1,
  									    """train_batch_size""": 1,
  									    """eval_batch_size""": 2,
  									    """max_epochs""": 2,
  									    """alpha_mlm""": 0.2,
  									    """alpha_ce""": 0.8,
  									    """do_predict""": True,
  									    """model_name_or_path""": """sshleifer/tinier_bart""",
  									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
  									    """val_check_interval""": 0.5,
  									}
  									default_updates.update(lowerCamelCase__       )
  									lowercase__							 =       CHEAP_ARGS.copy()
  									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  									lowercase__							 =       self.get_auto_remove_tmp_dir()
  									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
  															if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
  																					return ""
  															if v is False or v is None:
  																					return ""
  															if v is True:  # or len(str(v))==0:
  																					return F'''--{k}'''
  															return F'''--{k}={v}'''
  
  									lowercase__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
  									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
  									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )
  
  									lowercase__							 =       os.listdir(lowerCamelCase__       )
  									lowercase__							 =       {os.path.basename(lowerCamelCase__       ) for p in contents}
  									lowercase__							 =       [p for p in contents if p.endswith("""ckpt"""       )]
  									assert len(lowerCamelCase__       ) > 0
  
  									self.assertIn("""test_generations.txt"""       ,     lowerCamelCase__       )
  									self.assertIn("""test_results.txt"""       ,     lowerCamelCase__       )
  
  									# get the following from the module, (we don't have access to `model` here)
  									lowercase__							 =       os.path.join(lowerCamelCase__       ,     """metrics.json"""       )
  									lowercase__							 =       """rouge2"""
  
  									lowercase__							 =       load_json(lowerCamelCase__       )
  									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
  									print(lowerCamelCase__       )
  									lowercase__							 =       metrics["""val"""][-1]
  									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
  									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
  									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
  									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
  									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )
  
  
  
  
  
  
  