

'''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  _snake_case						:






			def __init__(   self       ,_snake_case       ,_snake_case=False       ,_snake_case=13       ,_snake_case=(32, 1_28)       ,_snake_case=4       ,_snake_case=3       ,_snake_case=27       ,_snake_case=38       ,_snake_case=99       ,_snake_case=99       ,_snake_case=32       ,_snake_case=5       ,_snake_case=4       ,_snake_case=4.0       ,_snake_case=2_57       ,_snake_case=None       ,):
				UpperCAmelCase_			:	List[str]      =   parent
				UpperCAmelCase_			:	str      =   is_training
				UpperCAmelCase_			:	Dict      =   batch_size
				UpperCAmelCase_			:	Any      =   image_size
				UpperCAmelCase_			:	Union[str, Any]      =   patch_size
				UpperCAmelCase_			:	List[Any]      =   num_channels
				UpperCAmelCase_			:	Optional[int]      =   max_token_length
				UpperCAmelCase_			:	List[Any]      =   num_character_labels
				UpperCAmelCase_			:	int      =   num_bpe_labels
				UpperCAmelCase_			:	Dict      =   num_wordpiece_labels
				UpperCAmelCase_			:	List[str]      =   hidden_size
				UpperCAmelCase_			:	int      =   num_hidden_layers
				UpperCAmelCase_			:	Optional[int]      =   num_attention_heads
				UpperCAmelCase_			:	List[Any]      =   mlp_ratio
				UpperCAmelCase_			:	List[str]      =   patch_embeds_hidden_size
				UpperCAmelCase_			:	Dict      =   output_hidden_states






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
				UpperCAmelCase_			:	Optional[int]      =   self.get_config()
				return config, pixel_values






			def 						UpperCamelCase__				(   self			):
				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 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	Union[str, Any]      =   MgpstrForSceneTextRecognition(_snake_case			)
				model.to(_snake_case			)
				model.eval()
				with torch.no_grad():
					UpperCAmelCase_			:	Optional[int]      =   model(_snake_case			)
				self.parent.assertEqual(
				    generated_ids[0][0].shape       ,(self.batch_size, self.max_token_length, self.num_character_labels)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[int]      =   self.prepare_config_and_inputs()
				UpperCAmelCase_ , UpperCAmelCase_			:	List[Any]      =   config_and_inputs
				UpperCAmelCase_			:	Tuple      =   {"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     str          =(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
			__A   :     Dict          ={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
			__A   :     int          =False

			__A   :     Optional[Any]          =False
			__A   :     Tuple          =False
			__A   :     List[str]          =False
			__A   :     Any          =False






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Dict      =   MgpstrModelTester(self			)
				UpperCAmelCase_			:	Any      =   ConfigTester(self       ,config_class=_snake_case       ,has_text_modality=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				self.config_tester.run_common_tests()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Tuple      =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case			)






			@unittest.skip(reason="MgpstrModel does not use inputs_embeds"			)
			def 						UpperCamelCase__				(   self			):
				pass






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   self.model_tester.prepare_config_and_inputs_for_common()

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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[Any]      =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	List[Any]      =   model_class(_snake_case			)
					UpperCAmelCase_			:	Union[str, Any]      =   inspect.signature(model.forward			)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					UpperCAmelCase_			:	str      =   [*signature.parameters.keys()]

					UpperCAmelCase_			:	Tuple      =   ["pixel_values"]
					self.assertListEqual(arg_names[:1]       ,_snake_case			)






			@unittest.skip(reason="MgpstrModel does not support feedforward chunking"			)
			def 						UpperCamelCase__				(   self			):
				pass






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	int      =   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_			:	Optional[Any]      =   True
					UpperCAmelCase_			:	List[Any]      =   model_class(_snake_case			)
					self.assertTrue(model.is_gradient_checkpointing			)






			def 						UpperCamelCase__				(   self			):
				def check_hidden_states_output(_snake_case       ,_snake_case       ,_snake_case			):
					UpperCAmelCase_			:	Dict      =   model_class(_snake_case			)
					model.to(_snake_case			)
					model.eval()

					with torch.no_grad():
						UpperCAmelCase_			:	Dict      =   model(**self._prepare_for_class(_snake_case       ,_snake_case			)			)

					UpperCAmelCase_			:	List[Any]      =   outputs.hidden_states

					UpperCAmelCase_			:	Any      =   getattr(
					    self.model_tester       ,"expected_num_hidden_layers"       ,self.model_tester.num_hidden_layers + 1			)
					self.assertEqual(len(_snake_case			)       ,_snake_case			)

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

				UpperCAmelCase_ , UpperCAmelCase_			:	int      =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	Dict      =   True
					check_hidden_states_output(_snake_case       ,_snake_case       ,_snake_case			)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					UpperCAmelCase_			:	Any      =   True

					check_hidden_states_output(_snake_case       ,_snake_case       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Union[str, Any]      =   self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase_			:	Optional[int]      =   _config_zero_init(_snake_case			)
				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	Any      =   model_class(config=_snake_case			)
					for name, param in model.named_parameters():
						if isinstance(_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 						UpperCamelCase__				(   self			):
				pass




def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	int      =   "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
	UpperCAmelCase_			:	str      =   Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,							stream=_SCREAMING_SNAKE_CASE							).raw							).convert("RGB"							)
	return im





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






			@slow
			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   "alibaba-damo/mgp-str-base"
				UpperCAmelCase_			:	Any      =   MgpstrForSceneTextRecognition.from_pretrained(_snake_case			).to(_snake_case			)
				UpperCAmelCase_			:	str      =   MgpstrProcessor.from_pretrained(_snake_case			)

				UpperCAmelCase_			:	List[str]      =   prepare_img()
				UpperCAmelCase_			:	Dict      =   processor(images=_snake_case       ,return_tensors="pt"			).pixel_values.to(_snake_case			)

				# forward pass
				with torch.no_grad():
					UpperCAmelCase_			:	int      =   model(_snake_case			)

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

				UpperCAmelCase_			:	str      =   processor.batch_decode(outputs.logits			)
				UpperCAmelCase_			:	str      =   "ticket"

				self.assertEqual(out_strs["generated_text"][0]       ,_snake_case			)

				UpperCAmelCase_			:	List[str]      =   torch.tensor(
				    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]       ,device=_snake_case       ,)

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







'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






			def 						UpperCamelCase__				(   self			):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




