code: 
  
  
  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	,     __UpperCamelCase	,     __UpperCamelCase=False	,     __UpperCamelCase=13	,     __UpperCamelCase=(32, 1_28)	,     __UpperCamelCase=4	,     __UpperCamelCase=3	,     __UpperCamelCase=27	,     __UpperCamelCase=38	,     __UpperCamelCase=99	,     __UpperCamelCase=99	,     __UpperCamelCase=32	,     __UpperCamelCase=5	,     __UpperCamelCase=4	,     __UpperCamelCase=4.0	,     __UpperCamelCase=2_57	,     __UpperCamelCase=None	,     )->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=parent
  						A__     :							Optional[int]   				=is_training
  						A__     :							Dict   				=batch_size
  						A__     :							Any   				=image_size
  						A__     :							str   				=patch_size
  						A__     :							Optional[Any]   				=num_channels
  						A__     :							Optional[int]   				=max_token_length
  						A__     :							str   				=num_character_labels
  						A__     :							Optional[Any]   				=num_bpe_labels
  						A__     :							Optional[int]   				=num_wordpiece_labels
  						A__     :							Optional[Any]   				=hidden_size
  						A__     :							int   				=num_hidden_layers
  						A__     :							Tuple   				=num_attention_heads
  						A__     :							Union[str, Any]   				=mlp_ratio
  						A__     :							List[Any]   				=patch_embeds_hidden_size
  						A__     :							Tuple   				=output_hidden_states
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)
  						A__     :							Union[str, Any]   				=self.get_config()
  						return config, pixel_values
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=MgpstrForSceneTextRecognition(__UpperCamelCase							)
  						model.to(__UpperCamelCase							)
  						model.eval()
  						with torch.no_grad():
  										A__     :							List[Any]   				=model(__UpperCamelCase							)
  						self.parent.assertEqual(
  						    generated_ids[0][0].shape	,     (self.batch_size, self.max_token_length, self.num_character_labels)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.prepare_config_and_inputs()
  						A__     :							Any   				=config_and_inputs
  						A__     :							Any   				={'''pixel_values''': pixel_values}
  						return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			a      (      UpperCamelCase_      ,UpperCamelCase_      ,unittest.TestCase   ):
  		__lowercase													=		(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
  		__lowercase													=		{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
  		__lowercase													=		False
  
  		__lowercase													=		False
  		__lowercase													=		False
  		__lowercase													=		False
  		__lowercase													=		False
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=MgpstrModelTester(self							)
  						A__     :							Union[str, Any]   				=ConfigTester(self	,     config_class=__UpperCamelCase	,     has_text_modality=__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						self.config_tester.run_common_tests()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*__UpperCamelCase							)
  
  
  
  
  
  
  		@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''							)
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Optional[Any]   				=model_class(__UpperCamelCase							)
  										self.assertIsInstance(model.get_input_embeddings()	,     (nn.Module)							)
  										A__     :							Any   				=model.get_output_embeddings()
  										self.assertTrue(x is None or isinstance(__UpperCamelCase	,     nn.Linear							)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							Union[str, Any]   				=model_class(__UpperCamelCase							)
  										A__     :							Dict   				=inspect.signature(model.forward							)
  										# signature.parameters is an OrderedDict => so arg_names order is deterministic
  										A__     :							List[Any]   				=[*signature.parameters.keys()]
  
  										A__     :							List[str]   				=['''pixel_values''']
  										self.assertListEqual(arg_names[:1]	,     __UpperCamelCase							)
  
  
  
  
  
  
  		@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''							)
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=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
  
  										A__     :							Any   				=True
  										A__     :							Union[str, Any]   				=model_class(__UpperCamelCase							)
  										self.assertTrue(model.is_gradient_checkpointing							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						def check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							):
  										A__     :							List[Any]   				=model_class(__UpperCamelCase							)
  										model.to(__UpperCamelCase							)
  										model.eval()
  
  										with torch.no_grad():
  														A__     :							Any   				=model(**self._prepare_for_class(__UpperCamelCase	,     __UpperCamelCase							)							)
  
  										A__     :							Optional[int]   				=outputs.hidden_states
  
  										A__     :							int   				=getattr(
  										    self.model_tester	,     '''expected_num_hidden_layers'''	,     self.model_tester.num_hidden_layers + 1							)
  										self.assertEqual(len(__UpperCamelCase							)	,     __UpperCamelCase							)
  
  										self.assertListEqual(
  										    list(hidden_states[0].shape[-2:]							)	,     [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]	,     )
  
  						A__     :							Optional[Any]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  										A__     :							List[str]   				=True
  										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)
  
  										# check that output_hidden_states also work using config
  										del inputs_dict["output_hidden_states"]
  										A__     :							Tuple   				=True
  
  										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.model_tester.prepare_config_and_inputs_for_common()
  
  						A__     :							Tuple   				=_config_zero_init(__UpperCamelCase							)
  						for model_class in self.all_model_classes:
  										A__     :							Optional[int]   				=model_class(config=__UpperCamelCase							)
  										for name, param in model.named_parameters():
  														if isinstance(__UpperCamelCase	,     (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       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
  				A__     :							List[Any]   				='''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
  				A__     :							List[str]   				=Image.open(requests.get(snake_case_,	stream=snake_case_	).raw	).convert('''RGB'''	)
  				return im
  
  
  
  
  @require_vision
  @require_torch
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@slow
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							int   				='''alibaba-damo/mgp-str-base'''
  						A__     :							Any   				=MgpstrForSceneTextRecognition.from_pretrained(__UpperCamelCase							).to(__UpperCamelCase							)
  						A__     :							int   				=MgpstrProcessor.from_pretrained(__UpperCamelCase							)
  
  						A__     :							Tuple   				=prepare_img()
  						A__     :							str   				=processor(images=__UpperCamelCase	,     return_tensors='''pt'''							).pixel_values.to(__UpperCamelCase							)
  
  						# forward pass
  						with torch.no_grad():
  										A__     :							Any   				=model(__UpperCamelCase							)
  
  						# verify the logits
  						self.assertEqual(outputs.logits[0].shape	,     torch.Size((1, 27, 38)							)							)
  
  						A__     :							Dict   				=processor.batch_decode(outputs.logits							)
  						A__     :							int   				='''ticket'''
  
  						self.assertEqual(out_strs['''generated_text'''][0]	,     __UpperCamelCase							)
  
  						A__     :							Tuple   				=torch.tensor(
  						    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]	,     device=__UpperCamelCase	,     )
  
  						self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]	,     __UpperCamelCase	,     atol=1E-4							)							)
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
  				A__     :							Optional[int]   				=[0] * len(snake_case_	)
  				for i in range(1,	len(snake_case_	)	):
  								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
  				return waiting_times
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
  				return [
  				    duration_time + waiting_times[i]
  				    for i, duration_time in enumerate(snake_case_	)
  				]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  if __name__ == "__main__":
  						# process id's
  						__lowerCamelCase       :       List[str]				   =			[1, 2, 3]
  
  						# ensure that we actually have processes
  						if len(processes) == 0:
  												print("Zero amount of processes")
  												raise SystemExit(0)
  
  						# duration time of all processes
  						__lowerCamelCase       :       Dict				   =			[19, 8, 9]
  
  						# ensure we can match each id to a duration time
  						if len(duration_times) != len(processes):
  												print("Unable to match all id's with their duration time")
  												raise SystemExit(0)
  
  						# get the waiting times and the turnaround times
  						__lowerCamelCase       :       int				   =			calculate_waiting_times(duration_times)
  						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)
  
  						# get the average times
  						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
  						__lowerCamelCase       :       Dict				   =			calculate_average_turnaround_time(turnaround_times)
  
  						# print all the results
  						print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
  						for i, process in enumerate(processes):
  												print(
  												    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
  												    F"{turnaround_times[i]}"
  												)
  						print(F"Average waiting time = {average_waiting_time}")
  						print(F"Average turn around time = {average_turnaround_time}")
  
  
  
  