


'''simple docstring'''

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple=12   ,						UpperCamelCase    :    Tuple=7   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    int=True   ,						UpperCamelCase    :    str=99   ,						UpperCamelCase    :    Tuple=32   ,						UpperCamelCase    :    Dict=32   ,						UpperCamelCase    :    List[str]=2   ,						UpperCamelCase    :    int=4   ,						UpperCamelCase    :    Optional[int]=37   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.1   ,						UpperCamelCase    :    List[str]=512   ,						UpperCamelCase    :    Optional[int]=0.02   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    Union[str, Any]=None   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= seq_length
             lowercase__      			= is_training
             lowercase__      			= use_input_mask
             lowercase__      			= use_labels
             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= projection_dim
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= intermediate_size
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= max_position_embeddings
             lowercase__      			= initializer_range
             lowercase__      			= scope
             lowercase__      			= bos_token_id



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

             lowercase__      			= None
             if self.use_input_mask:
                    lowercase__      			= random_attention_mask([self.batch_size, self.seq_length] )

             if input_mask is not None:
                    lowercase__      			= input_mask.numpy()
                    lowercase__			,lowercase__      			= input_mask.shape
                    lowercase__      			= np.random.randint(1   ,						seq_length - 1   ,						size=(batch_size,) )
                    for batch_idx, start_index in enumerate(UpperCamelCase ):
                           lowercase__      			= 1
                           lowercase__      			= 0

             lowercase__      			= self.get_config()

             return config, input_ids, tf.convert_to_tensor(UpperCamelCase )



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             return BlipTextConfig(
                 vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						projection_dim=self.projection_dim   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						dropout=self.dropout   ,						attention_dropout=self.attention_dropout   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						bos_token_id=self.bos_token_id   ,						)



      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= TFBlipTextModel(config=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						attention_mask=UpperCamelCase   ,						training=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						training=UpperCamelCase )
             self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size) )
             self.parent.assertEqual(result.pooler_output.shape   ,						(self.batch_size, self.hidden_size) )






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= self.prepare_config_and_inputs()
             lowercase__			,lowercase__			,lowercase__      			= config_and_inputs
             lowercase__      			= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_tf
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     str  					=							(TFBlipTextModel,) if is_tf_available() else ()
      lowerCAmelCase__					:     Union[str, Any]  					=							False
      lowerCAmelCase__					:     int  					=							False
      lowerCAmelCase__					:     Tuple  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= BlipTextModelTester(self )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase   ,						hidden_size=37 )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''Blip does not use inputs_embeds''' )
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             pass



      @slow
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      			= TFBlipTextModel.from_pretrained(UpperCamelCase )
                    self.assertIsNotNone(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Any=True ):

             '''simple docstring'''







             super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase )








'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							ShapEImgaImgPipeline
      lowerCAmelCase__					:     List[str]  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							[
          """num_images_per_prompt""",
          """num_inference_steps""",
          """generator""",
          """latents""",
          """guidance_scale""",
          """frame_size""",
          """output_type""",
          """return_dict""",
      ]
      lowerCAmelCase__					:     Tuple  					=							False



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return self.time_input_dim * 4



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return 8



      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size   ,						image_size=64   ,						projection_dim=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_channels=3   ,						num_hidden_layers=5   ,						patch_size=1   ,						)

             lowercase__      			= CLIPVisionModel(UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= CLIPImageProcessor(
                 crop_size=224   ,						do_center_crop=UpperCamelCase   ,						do_normalize=UpperCamelCase   ,						do_resize=UpperCamelCase   ,						image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,						image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,						resample=3   ,						size=224   ,						)

             return image_processor



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''num_attention_heads''': 2,
                 '''attention_head_dim''': 16,
                 '''embedding_dim''': self.time_input_dim,
                 '''num_embeddings''': 32,
                 '''embedding_proj_dim''': self.text_embedder_hidden_size,
                 '''time_embed_dim''': self.time_embed_dim,
                 '''num_layers''': 1,
                 '''clip_embed_dim''': self.time_input_dim * 2,
                 '''additional_embeddings''': 0,
                 '''time_embed_act_fn''': '''gelu''',
                 '''norm_in_type''': '''layer''',
                 '''embedding_proj_norm_type''': '''layer''',
                 '''encoder_hid_proj_type''': None,
                 '''added_emb_type''': None,
             }

             lowercase__      			= PriorTransformer(**UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''param_shapes''': (
                     (self.renderer_dim, 93),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                 ),
                 '''d_latent''': self.time_input_dim,
                 '''d_hidden''': self.renderer_dim,
                 '''n_output''': 12,
                 '''background''': (
                     0.1,
                     0.1,
                     0.1,
                 ),
             }
             lowercase__      			= ShapERenderer(**UpperCamelCase )
             return model



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

             '''simple docstring'''







             lowercase__      			= self.dummy_prior
             lowercase__      			= self.dummy_image_encoder
             lowercase__      			= self.dummy_image_processor
             lowercase__      			= self.dummy_renderer

             lowercase__      			= HeunDiscreteScheduler(
                 beta_schedule='''exp'''   ,						num_train_timesteps=1024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase   ,						clip_sample=UpperCamelCase   ,						clip_sample_range=1.0   ,						)
             lowercase__      			= {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''image_processor''': image_processor,
                 '''renderer''': renderer,
                 '''scheduler''': scheduler,
             }

             return components



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str=0 ):

             '''simple docstring'''







             lowercase__      			= floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )

             if str(UpperCamelCase ).startswith('''mps''' ):
                    lowercase__      			= torch.manual_seed(UpperCamelCase )
             else:
                    lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(UpperCamelCase )
             lowercase__      			= {
                 '''image''': input_image,
                 '''generator''': generator,
                 '''num_inference_steps''': 1,
                 '''frame_size''': 32,
                 '''output_type''': '''np''',
             }
             return inputs



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= '''cpu'''

             lowercase__      			= self.get_dummy_components()

             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )

             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= pipe(**self.get_dummy_inputs(UpperCamelCase ) )
             lowercase__      			= output.images[0]
             lowercase__      			= image[0, -3:, -3:, -1]

             assert image.shape == (20, 32, 32, 3)

             lowercase__      			= np.array(
                 [
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                 ] )

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



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             self._test_inference_batch_consistent(batch_sizes=[1, 2] )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= torch_device == '''cpu'''
             lowercase__      			= True
             self._test_inference_batch_single_identical(
                 batch_size=2   ,						test_max_difference=UpperCamelCase   ,						relax_max_difference=UpperCamelCase   ,						)






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.get_dummy_components()
             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= 1
             lowercase__      			= 2

             lowercase__      			= self.get_dummy_inputs(UpperCamelCase )

             for key in inputs.keys():
                    if key in self.batch_params:
                           lowercase__      			= batch_size * [inputs[key]]

             lowercase__      			= pipe(**UpperCamelCase   ,						num_images_per_prompt=UpperCamelCase )[0]

             assert images.shape[0] == batch_size * num_images_per_prompt






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





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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






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

             '''simple docstring'''







             lowercase__      			= load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
             lowercase__      			= load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/shap_e/test_shap_e_img2img_out.npy''' )
             lowercase__      			= ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(0 )

             lowercase__      			= pipe(
                 UpperCamelCase   ,						generator=UpperCamelCase   ,						guidance_scale=3.0   ,						num_inference_steps=64   ,						frame_size=64   ,						output_type='''np'''   ,						).images[0]

             assert images.shape == (20, 64, 64, 3)

             assert_mean_pixel_difference(UpperCamelCase   ,						UpperCamelCase )




