





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 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Optional[Any]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=12    ,      a__	:				Optional[int]=7    ,      a__	:				Optional[int]=True    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=True    ,      a__	:				List[str]=99    ,      a__	:				Optional[int]=32    ,      a__	:				Any=32    ,      a__	:				Union[str, Any]=2    ,      a__	:				Tuple=4    ,      a__	:				Optional[Any]=37    ,      a__	:				str=0.1    ,      a__	:				str=0.1    ,      a__	:				Optional[int]=512    ,      a__	:				List[str]=0.0_2    ,      a__	:				Dict=0    ,      a__	:				Any=None    ,      ):





         """simple docstring"""



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


      def        a			(self	:				Any				):





         """simple docstring"""



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

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

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

         __snake_case								=       self.get_config()

         return config, input_ids, tf.convert_to_tensor(a__				)


      def        a			(self	:				List[str]				):





         """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        a			(self	:				Dict    ,      a__	:				Optional[Any]    ,      a__	:				Union[str, Any]    ,      a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       TFBlipTextModel(config=a__				)
         __snake_case								=       model(a__    ,      attention_mask=a__    ,      training=a__				)
         __snake_case								=       model(a__    ,      training=a__				)
         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        a			(self	:				Dict				):





         """simple docstring"""



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






@require_tf
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   (TFBlipTextModel,) if is_tf_available() else ()
      A_						:    Dict							=   False
      A_						:    Tuple							=   False
      A_						:    List[str]							=   False


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       BlipTextModelTester(self				)
         __snake_case								=       ConfigTester(self    ,      config_class=a__    ,      hidden_size=37				)


      def        a			(self	:				Any				):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         pass


      def        a			(self	:				List[str]				):





         """simple docstring"""



         pass


      @unittest.skip(reason='''Blip does not use inputs_embeds'''				)
      def        a			(self	:				List[str]				):





         """simple docstring"""



         pass


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





         """simple docstring"""



         pass


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





         """simple docstring"""



         pass


      @slow
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



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


      def        a			(self	:				Optional[int]    ,      a__	:				Any=True				):





         """simple docstring"""



         super().test_pt_tf_model_equivalence(allow_missing_keys=a__				)








import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   CycleDiffusionPipeline
      A_						:    List[Any]							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'negative_prompt',
          'height',
          'width',
          'negative_prompt_embeds',
      }
      A_						:    List[str]							=   PipelineTesterMixin.required_optional_params - {'latents'}
      A_						:    List[str]							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
      A_						:    List[str]							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
      A_						:    Any							=   IMAGE_TO_IMAGE_IMAGE_PARAMS


      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)
         __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    ,      )
         __snake_case								=       DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      num_train_timesteps=1000    ,      clip_sample=a__    ,      set_alpha_to_one=a__    ,      )
         torch.manual_seed(0				)
         __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				)
         __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=1000    ,      )
         __snake_case								=       CLIPTextModel(a__				)
         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         __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	:				List[str]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image / 2 + 0.5
         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''prompt''': '''An astronaut riding an elephant''',
             '''source_prompt''': '''An astronaut riding a horse''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''eta''': 0.1,
             '''strength''': 0.8,
             '''guidance_scale''': 3,
             '''source_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case								=       self.get_dummy_components()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

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


      @unittest.skipIf(torch_device != '''cuda'''    ,      '''This test requires a GPU'''				)
      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.get_dummy_components()
         for name, module in components.items():
            if hasattr(a__    ,      '''half'''				):
               __snake_case								=       module.half()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

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


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_save_load_local()


      @unittest.skip('''non-deterministic pipeline'''				)
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_inference_batch_single_identical()


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def        a			(self	:				str				):





         """simple docstring"""



         return super().test_save_load_optional_components()


      @skip_mps
      def        a			(self	:				Dict				):





         """simple docstring"""



         return super().test_attention_slicing_forward_pass()






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


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





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(
             a__    ,      scheduler=a__    ,      safety_checker=a__    ,      torch_dtype=torch.floataa    ,      revision='''fp16'''				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image				).max() < 5E-1


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(a__    ,      scheduler=a__    ,      safety_checker=a__				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         assert np.abs(image - expected_image				).max() < 2E-2

