






'''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  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					ShapEImgaImgPipeline
     a_    	=					["image"]
     a_    	=					["image"]
     a_    	=					[
         "num_images_per_prompt",
         "num_inference_steps",
         "generator",
         "latents",
         "guidance_scale",
         "frame_size",
         "output_type",
         "return_dict",
     ]
     a_    	=					False


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 32


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 32


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.time_input_dim * 4


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 8


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       torch.manual_seed(0 )
       a_      :	Dict      =							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							,		)

       a_      :	int      =							CLIPVisionModel(lowerCAmelCase_ )
       return model


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							CLIPImageProcessor(
           crop_size=2_24							,		do_center_crop=lowerCAmelCase_							,		do_normalize=lowerCAmelCase_							,		do_resize=lowerCAmelCase_							,		image_mean=[0.48145466, 0.4578275, 0.40821073]							,		image_std=[0.26862954, 0.26130258, 0.27577711]							,		resample=3							,		size=2_24							,		)

       return image_processor


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       torch.manual_seed(0 )

       a_      :	Optional[Any]      =							{
           '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,
       }

       a_      :	Optional[int]      =							PriorTransformer(**lowerCAmelCase_ )
       return model


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       torch.manual_seed(0 )

       a_      :	Optional[int]      =							{
           '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,
           ),
       }
       a_      :	Dict      =							ShapERenderer(**lowerCAmelCase_ )
       return model


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.dummy_prior
       a_      :	Any      =							self.dummy_image_encoder
       a_      :	Optional[Any]      =							self.dummy_image_processor
       a_      :	Any      =							self.dummy_renderer

       a_      :	Tuple      =							HeunDiscreteScheduler(
           beta_schedule="""exp"""							,		num_train_timesteps=10_24							,		prediction_type="""sample"""							,		use_karras_sigmas=lowerCAmelCase_							,		clip_sample=lowerCAmelCase_							,		clip_sample_range=1.0							,		)
       a_      :	Any      =							{
           'prior': prior,
           'image_encoder': image_encoder,
           'image_processor': image_processor,
           'renderer': renderer,
           'scheduler': scheduler,
       }

       return components


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       a_      :	List[Any]      =							floats_tensor((1, 3, 64, 64)							,		rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )

       if str(lowerCAmelCase_ ).startswith("""mps""" ):
         a_      :	List[str]      =							torch.manual_seed(lowerCAmelCase_ )
       else:
         a_      :	List[str]      =							torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
       a_      :	Dict      =							{
           'image': input_image,
           'generator': generator,
           'num_inference_steps': 1,
           'frame_size': 32,
           'output_type': 'np',
       }
       return inputs


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							'cpu'

       a_      :	List[Any]      =							self.get_dummy_components()

       a_      :	str      =							self.pipeline_class(**lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							pipe.to(lowerCAmelCase_ )

       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       a_      :	Dict      =							pipe(**self.get_dummy_inputs(lowerCAmelCase_ ) )
       a_      :	Any      =							output.images[0]
       a_      :	Union[str, Any]      =							image[0, -3:, -3:, -1]

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

       a_      :	int      =							np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ] )

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							torch_device == 'cpu'
       a_      :	Any      =							True
       self._test_inference_batch_single_identical(
           batch_size=2							,		test_max_difference=lowerCAmelCase_							,		relax_max_difference=lowerCAmelCase_							,		)






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_dummy_components()
       a_      :	Dict      =							self.pipeline_class(**lowerCAmelCase_ )
       a_      :	Dict      =							pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       a_      :	Optional[int]      =							1
       a_      :	Optional[int]      =							2

       a_      :	Union[str, Any]      =							self.get_dummy_inputs(lowerCAmelCase_ )

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

       a_      :	Union[str, Any]      =							pipe(**lowerCAmelCase_							,		num_images_per_prompt=lowerCAmelCase_ )[0]

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







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

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
       a_      :	Union[str, Any]      =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/shap_e/test_shap_e_img2img_out.npy""" )
       a_      :	List[str]      =							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
       a_      :	List[Any]      =							pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       a_      :	List[str]      =							torch.Generator(device=lowerCAmelCase_ ).manual_seed(0 )

       a_      :	int      =							pipe(
           lowerCAmelCase_							,		generator=lowerCAmelCase_							,		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(lowerCAmelCase_							,		lowerCAmelCase_ )




'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab() )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)
       processor.save_pretrained(
           self.tmpdirname							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		speaker_embeddings_directory=self.speaker_embeddings_directory							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	Any      =							BarkProcessor.from_pretrained(
           self.tmpdirname							,		self.speaker_embeddings_dict_path							,		bos_token="""(BOS)"""							,		eos_token="""(EOS)"""							,		)

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab() )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """semantic_prompt""": np.ones(lowerCAmelCase_ ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from npz file
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from the hub
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		add_special_tokens=lowerCAmelCase_							,		return_attention_mask=lowerCAmelCase_							,		return_token_type_ids=lowerCAmelCase_							,		)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]							,		encoded_processor[key].squeeze().tolist() )


