
'''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_      :	Tuple      =							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_      :	List[Any]      =							CLIPVisionModel(lowerCAmelCase_ )
       return model


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							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_      :	List[str]      =							{
           """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_      :	List[Any]      =							PriorTransformer(**lowerCAmelCase_ )
       return model


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       torch.manual_seed(0 )

       a_      :	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_      :	List[Any]      =							ShapERenderer(**lowerCAmelCase_ )
       return model


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       a_      :	Any      =							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_      :	int      =							{
           """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_      :	Union[str, Any]      =							floats_tensor((1, 3, 64, 64)							,		rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""cpu"""

       a_      :	Dict      =							self.get_dummy_components()

       a_      :	Optional[int]      =							self.pipeline_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							pipe.to(lowerCAmelCase_ )

       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

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

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

       a_      :	Optional[Any]      =							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_      :	Tuple      =							torch_device == """cpu"""
       a_      :	Union[str, 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_      :	Optional[Any]      =							self.get_dummy_components()
       a_      :	Dict      =							self.pipeline_class(**lowerCAmelCase_ )
       a_      :	Any      =							pipe.to(lowerCAmelCase_ )
       pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

       a_      :	Dict      =							1
       a_      :	List[str]      =							2

       a_      :	int      =							self.get_dummy_inputs(lowerCAmelCase_ )

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

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

       a_      :	Tuple      =							torch.Generator(device=lowerCAmelCase_ ).manual_seed(0 )

       a_      :	Optional[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'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


