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__ :

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

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

       def        snake_case__  (				self :			List[Any]       ):
           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        snake_case__  (				self :			int       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			Tuple       ):
           __snake_case    :							Union[str, Any]             =  MgpstrForSceneTextRecognition(_snake_case       )
           model.to(_snake_case       )
           model.eval()
           with torch.no_grad():
               __snake_case    :							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        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							Optional[int]             =  self.prepare_config_and_inputs()
           __snake_case    :							List[Any]             =  config_and_inputs
           __snake_case    :							Tuple             =  {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class 			SCREAMING_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        snake_case__  (				self :			Tuple       ):
           __snake_case    :							Dict             =  MgpstrModelTester(self       )
           __snake_case    :							Any             =  ConfigTester(self       ,       config_class=_snake_case       ,       has_text_modality=_snake_case       )

       def        snake_case__  (				self :			int       ):
           self.config_tester.run_common_tests()

       def        snake_case__  (				self :			int       ):
           __snake_case    :							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        snake_case__  (				self :			List[str]       ):
           pass

       def        snake_case__  (				self :			List[Any]       ):
           __snake_case    :							List[str]             =  self.model_tester.prepare_config_and_inputs_for_common()

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

       def        snake_case__  (				self :			Tuple       ):
           __snake_case    :							Optional[Any]             =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

       def        snake_case__  (				self :			Tuple       ):
           __snake_case    :							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

               __snake_case    :							Optional[Any]             =  True
               __snake_case    :							List[Any]             =  model_class(_snake_case       )
               self.assertTrue(model.is_gradient_checkpointing       )

       def        snake_case__  (				self :			Any       ):
           def check_hidden_states_output(_lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			List[str]       ,       _lowerCAmelCase :			Optional[int]       ):
               __snake_case    :							Dict             =  model_class(_snake_case       )
               model.to(_snake_case       )
               model.eval()

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

               __snake_case    :							List[Any]             =  outputs.hidden_states

               __snake_case    :							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]       ,       )

           __snake_case    :							int             =  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __snake_case    :							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"]
               __snake_case    :							Any             =  True

               check_hidden_states_output(_snake_case       ,       _snake_case       ,       _snake_case       )

       def        snake_case__  (				self :			int       ):
           __snake_case    :							Union[str, Any]             =  self.model_tester.prepare_config_and_inputs_for_common()

           __snake_case    :							Optional[int]             =  _config_zero_init(_snake_case       )
           for model_class in self.all_model_classes:
               __snake_case    :							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        snake_case__  (				self :			int       ):
           pass







def  __lowerCAmelCase							(						):







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







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

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

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

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

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

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

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

           __snake_case    :							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




