




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  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	List[Any]       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	List[Any]=False       ,       _lowerCAmelCase						:	Dict=13       ,       _lowerCAmelCase						:	str=(32, 128)       ,       _lowerCAmelCase						:	Optional[int]=4       ,       _lowerCAmelCase						:	Union[str, Any]=3       ,       _lowerCAmelCase						:	Dict=27       ,       _lowerCAmelCase						:	List[Any]=38       ,       _lowerCAmelCase						:	Union[str, Any]=99       ,       _lowerCAmelCase						:	Optional[int]=99       ,       _lowerCAmelCase						:	Optional[Any]=32       ,       _lowerCAmelCase						:	Optional[Any]=5       ,       _lowerCAmelCase						:	List[Any]=4       ,       _lowerCAmelCase						:	Tuple=4.0       ,       _lowerCAmelCase						:	Tuple=257       ,       _lowerCAmelCase						:	Any=None       ,       ):
       SCREAMING_SNAKE_CASE_					=    parent
       SCREAMING_SNAKE_CASE_					=    is_training
       SCREAMING_SNAKE_CASE_					=    batch_size
       SCREAMING_SNAKE_CASE_					=    image_size
       SCREAMING_SNAKE_CASE_					=    patch_size
       SCREAMING_SNAKE_CASE_					=    num_channels
       SCREAMING_SNAKE_CASE_					=    max_token_length
       SCREAMING_SNAKE_CASE_					=    num_character_labels
       SCREAMING_SNAKE_CASE_					=    num_bpe_labels
       SCREAMING_SNAKE_CASE_					=    num_wordpiece_labels
       SCREAMING_SNAKE_CASE_					=    hidden_size
       SCREAMING_SNAKE_CASE_					=    num_hidden_layers
       SCREAMING_SNAKE_CASE_					=    num_attention_heads
       SCREAMING_SNAKE_CASE_					=    mlp_ratio
       SCREAMING_SNAKE_CASE_					=    patch_embeds_hidden_size
       SCREAMING_SNAKE_CASE_					=    output_hidden_states



 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)
       SCREAMING_SNAKE_CASE_					=    self.get_config()
       return config, pixel_values



 def    lowerCAmelCase_						(							self						:	List[str]		):
       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						:	Dict       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Any		):
       SCREAMING_SNAKE_CASE_					=    MgpstrForSceneTextRecognition(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase		)
       self.parent.assertEqual(
           generated_ids[0][0].shape       ,       (self.batch_size, self.max_token_length, self.num_character_labels)		)




 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    config_and_inputs
       SCREAMING_SNAKE_CASE_					=    {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 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						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    MgpstrModelTester(self		)
       SCREAMING_SNAKE_CASE_					=    ConfigTester(self       ,       config_class=_lowerCAmelCase       ,       has_text_modality=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Tuple		):
       self.config_tester.run_common_tests()



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	Dict		):
       pass



 def    lowerCAmelCase_						(							self						:	int		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs_for_common()

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



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs_for_common()

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

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



 @unittest.skip(reason='MgpstrModel does not support feedforward chunking'		)
 def    lowerCAmelCase_						(							self						:	List[str]		):
       pass



 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    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

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



 def    lowerCAmelCase_						(							self						:	List[str]		):
       def check_hidden_states_output(_lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	Any		):
             SCREAMING_SNAKE_CASE_					=    model_class(_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

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

             SCREAMING_SNAKE_CASE_					=    outputs.hidden_states

             SCREAMING_SNAKE_CASE_					=    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]       ,       )

       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	int		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_					=    _config_zero_init(_lowerCAmelCase		)
       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	int		):
       pass


def       UpperCAmelCase_							(   )      ->				List[str]:
      SCREAMING_SNAKE_CASE_					=    'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
      SCREAMING_SNAKE_CASE_					=    Image.open(requests.get(__UpperCAmelCase    ,    stream=__UpperCAmelCase							).raw							).convert('RGB'							)
      return im





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


 '''simple docstring'''







 @slow
 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    'alibaba-damo/mgp-str-base'
       SCREAMING_SNAKE_CASE_					=    MgpstrForSceneTextRecognition.from_pretrained(_lowerCAmelCase		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    MgpstrProcessor.from_pretrained(_lowerCAmelCase		)

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

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

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

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

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

       SCREAMING_SNAKE_CASE_					=    torch.tensor(
           [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]       ,       device=_lowerCAmelCase       ,       )

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





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    UNetaDModel(
           block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,       up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 3, 32, 32)		)
       SCREAMING_SNAKE_CASE_					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    (image > 0).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





@nightly
@require_torch_gpu
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'		)

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    repaint(
           _lowerCAmelCase       ,       _lowerCAmelCase       ,       num_inference_steps=250       ,       eta=0.0       ,       jump_length=10       ,       jump_n_sample=10       ,       generator=_lowerCAmelCase       ,       output_type='np'       ,       )
       SCREAMING_SNAKE_CASE_					=    output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image		).mean() < 1E-2