





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,							A,							A=False,							A=13,							A=(32, 128),							A=4,							A=3,							A=27,							A=38,							A=99,							A=99,							A=32,							A=5,							A=4,							A=4.0,							A=257,							A=None,							):





            """simple docstring"""

            _UpperCamelCase														=    parent
            _UpperCamelCase														=    is_training
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    image_size
            _UpperCamelCase														=    patch_size
            _UpperCamelCase														=    num_channels
            _UpperCamelCase														=    max_token_length
            _UpperCamelCase														=    num_character_labels
            _UpperCamelCase														=    num_bpe_labels
            _UpperCamelCase														=    num_wordpiece_labels
            _UpperCamelCase														=    hidden_size
            _UpperCamelCase														=    num_hidden_layers
            _UpperCamelCase														=    num_attention_heads
            _UpperCamelCase														=    mlp_ratio
            _UpperCamelCase														=    patch_embeds_hidden_size
            _UpperCamelCase														=    output_hidden_states

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]   )
            _UpperCamelCase														=    self.get_config()
            return config, pixel_values

     def 						_UpperCamelCase  (   self   ):





            """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 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    MgpstrForSceneTextRecognition(_lowercase   )
            model.to(_lowercase   )
            model.eval()
            with torch.no_grad():
                   _UpperCamelCase														=    model(_lowercase   )
            self.parent.assertEqual(
                generated_ids[0][0].shape,							(self.batch_size, self.max_token_length, self.num_character_labels)   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.prepare_config_and_inputs()
            _UpperCamelCase							,		_UpperCamelCase														=    config_and_inputs
            _UpperCamelCase														=    {'''pixel_values''': pixel_values}
            return config, inputs_dict






@require_torch
class 			__A(    lowerCamelCase_    ,      lowerCamelCase_    ,      unittest.TestCase			):
     __A							=							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
     __A							=							{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
     __A							=							False

     __A							=							False
     __A							=							False
     __A							=							False
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    MgpstrModelTester(self   )
            _UpperCamelCase														=    ConfigTester(self,							config_class=_lowercase,							has_text_modality=_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_lowercase   )

     @unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

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

                   _UpperCamelCase														=    ['''pixel_values''']
                   self.assertListEqual(arg_names[:1],							_lowercase   )

     @unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    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														=    True
                   _UpperCamelCase														=    model_class(_lowercase   )
                   self.assertTrue(model.is_gradient_checkpointing   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            def check_hidden_states_output(A,							A,							A   ):
                   _UpperCamelCase														=    model_class(_lowercase   )
                   model.to(_lowercase   )
                   model.eval()

                   with torch.no_grad():
                          _UpperCamelCase														=    model(**self._prepare_for_class(_lowercase,							_lowercase   )   )

                   _UpperCamelCase														=    outputs.hidden_states

                   _UpperCamelCase														=    getattr(
                       self.model_tester,							'''expected_num_hidden_layers''',							self.model_tester.num_hidden_layers + 1   )
                   self.assertEqual(len(_lowercase   ),							_lowercase   )

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

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    True
                   check_hidden_states_output(_lowercase,							_lowercase,							_lowercase   )

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

                   check_hidden_states_output(_lowercase,							_lowercase,							_lowercase   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            _UpperCamelCase														=    _config_zero_init(_lowercase   )
            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    model_class(config=_lowercase   )
                   for name, param in model.named_parameters():
                          if isinstance(_lowercase,							(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   ):





            """simple docstring"""

            pass






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
       _UpperCamelCase														=    Image.open(requests.get(__UpperCamelCase     ,							stream=__UpperCamelCase       ).raw       ).convert('''RGB'''       )
       return im

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

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    '''alibaba-damo/mgp-str-base'''
            _UpperCamelCase														=    MgpstrForSceneTextRecognition.from_pretrained(_lowercase   ).to(_lowercase   )
            _UpperCamelCase														=    MgpstrProcessor.from_pretrained(_lowercase   )

            _UpperCamelCase														=    prepare_img()
            _UpperCamelCase														=    processor(images=_lowercase,							return_tensors='''pt'''   ).pixel_values.to(_lowercase   )

            # forward pass
            with torch.no_grad():
                   _UpperCamelCase														=    model(_lowercase   )

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

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

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

            _UpperCamelCase														=    torch.tensor(
                [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]],							device=_lowercase,							)

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







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				StableDiffusionModelEditingPipeline
   lowerCAmelCase_								=				TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= UNetaDConditionModel(
         block_out_channels=(32, 64)			,      layers_per_block=2			,      sample_size=32			,      in_channels=4			,      out_channels=4			,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,      cross_attention_dim=32			,      )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler()
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= AutoencoderKL(
         block_out_channels=[32, 64]			,      in_channels=3			,      out_channels=3			,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,      latent_channels=4			,      )
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= CLIPTextConfig(
         bos_token_id=0			,      eos_token_id=2			,      hidden_size=32			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=10_00			,      )
     SCREAMING_SNAKE_CASE__     				= CLIPTextModel(_lowercase )
     SCREAMING_SNAKE_CASE__     				= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

     SCREAMING_SNAKE_CASE__     				= {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         # Setting height and width to None to prevent OOMs on CPU.
         """height""": None,
         """width""": None,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]
     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= """french fries"""
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase			,      negative_prompt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= output.images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

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

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= PNDMScheduler()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     # the pipeline does not expect pndm so test if it raises error.
     with self.assertRaises(_lowercase ):
       SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     super().test_inference_batch_single_identical(expected_max_diff=5E-3 )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     super().test_attention_slicing_forward_pass(expected_max_diff=5E-3 )







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							str ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(_lowercase			,      safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__     				= np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58] )

     assert np.abs(expected_slice - image_slice ).max() < 1E-2

     # make sure image changes after editing
     pipe.edit_model("""A pack of roses"""			,      """A pack of blue roses""" )

     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     assert np.abs(expected_slice - image_slice ).max() > 1E-1




   def       __a		(       self     :							Any ):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_pretrained(_lowercase			,      subfolder="""scheduler""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(
         _lowercase			,      scheduler=_lowercase			,      safety_checker=_lowercase )
     SCREAMING_SNAKE_CASE__     				= pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.cuda.max_memory_allocated()
     # make sure that less than 4.4 GB is allocated
     assert mem_bytes < 4.4 * 10**9
