


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__	(							UpperCamelCase_					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Union[str, Any]    =		ShapEImgaImgPipeline
			_UpperCamelCase		:					Tuple    =		["""image"""]
			_UpperCamelCase		:					Any    =		["""image"""]
			_UpperCamelCase		:					int    =		[
			    """num_images_per_prompt""",
			    """num_inference_steps""",
			    """generator""",
			    """latents""",
			    """guidance_scale""",
			    """frame_size""",
			    """output_type""",
			    """return_dict""",
			]
			_UpperCamelCase		:					Tuple    =		False


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 32


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 32


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.time_input_dim * 4


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return 8


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )
						UpperCamelCase__					=       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      ,						)

						UpperCamelCase__					=       CLIPVisionModel(_a     )
						return model


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       CLIPImageProcessor(
						    crop_size=224      ,						do_center_crop=_a      ,						do_normalize=_a      ,						do_resize=_a      ,						image_mean=[0.48145466, 0.4578275, 0.40821073]      ,						image_std=[0.26862954, 0.26130258, 0.27577711]      ,						resample=3      ,						size=224      ,						)

						return image_processor


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )

						UpperCamelCase__					=       {
						    """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,
						}

						UpperCamelCase__					=       PriorTransformer(**_a     )
						return model


			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )

						UpperCamelCase__					=       {
						    """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,
						    ),
						}
						UpperCamelCase__					=       ShapERenderer(**_a     )
						return model


			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.dummy_prior
						UpperCamelCase__					=       self.dummy_image_encoder
						UpperCamelCase__					=       self.dummy_image_processor
						UpperCamelCase__					=       self.dummy_renderer

						UpperCamelCase__					=       HeunDiscreteScheduler(
						    beta_schedule="exp"      ,						num_train_timesteps=1024      ,						prediction_type="sample"      ,						use_karras_sigmas=_a      ,						clip_sample=_a      ,						clip_sample_range=1.0      ,						)
						UpperCamelCase__					=       {
						    """prior""": prior,
						    """image_encoder""": image_encoder,
						    """image_processor""": image_processor,
						    """renderer""": renderer,
						    """scheduler""": scheduler,
						}

						return components


			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=0     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor((1, 3, 64, 64)      ,						rng=random.Random(_a     )     ).to(_a     )

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


			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       """cpu"""

						UpperCamelCase__					=       self.get_dummy_components()

						UpperCamelCase__					=       self.pipeline_class(**_a     )
						UpperCamelCase__					=       pipe.to(_a     )

						pipe.set_progress_bar_config(disable=_a     )

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

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

						UpperCamelCase__					=       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 				snake_case__				(							self     ):

						'''simple docstring'''


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


			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       torch_device == """cpu"""
						UpperCamelCase__					=       True
						self._test_inference_batch_single_identical(
						    batch_size=2      ,						test_max_difference=_a      ,						relax_max_difference=_a      ,						)







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.get_dummy_components()
						UpperCamelCase__					=       self.pipeline_class(**_a     )
						UpperCamelCase__					=       pipe.to(_a     )
						pipe.set_progress_bar_config(disable=_a     )

						UpperCamelCase__					=       1
						UpperCamelCase__					=       2

						UpperCamelCase__					=       self.get_dummy_inputs(_a     )

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

						UpperCamelCase__					=       pipe(**_a      ,						num_images_per_prompt=_a     )[0]

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







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






			"""simple docstring"""






			def 				snake_case__				(							self     ):

						'''simple docstring'''


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







			def 				snake_case__				(							self     ):

						'''simple docstring'''


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

						UpperCamelCase__					=       torch.Generator(device=_a     ).manual_seed(0     )

						UpperCamelCase__					=       pipe(
						    _a      ,						generator=_a      ,						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(_a      ,						_a     )






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil
@pytest.fixture
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		int:
			UpperCamelCase__					=       tmp_path / "file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :int  )->		Union[str, Any]:
			UpperCamelCase__					=       tmp_path / "malformed_file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20,\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       tmp_path / "csv_with_image.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    F'''\
        image
        {image_file}
        '''  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]  )->		Any:
			UpperCamelCase__					=       tmp_path / "csv_with_label.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        label\n        good\n        bad\n        good\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :Any  )->		int:
			UpperCamelCase__					=       tmp_path / "csv_with_int_list.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Optional[Any]				,     _A   :List[str]  )->		Union[str, Any]:
			UpperCamelCase__					=       Csv()
			UpperCamelCase__					=       csv._generate_tables([[csv_file, malformed_csv_file]]  )
			with pytest.raises(_A				,     match="Error tokenizing data"  ):
						for _ in generator:
									pass
			assert any(
			    record.levelname == "ERROR"
			    and "Failed to read file" in record.message
			    and os.path.basename(_A  ) in record.message
			    for record in caplog.records  )
@require_pil
def        UpperCamelCase_(				_A   :Any  )->		int:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"image": Image()}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_image]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("image"  ).type == Image()()
			UpperCamelCase__					=       pa_table.to_pydict()["image"]
			assert generated_content == [{"path": image_file, "bytes": None}]
def        UpperCamelCase_(				_A   :Any  )->		Dict:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1:]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"label": ClassLabel(names=["good", "bad"]  )}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_label]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("label"  ).type == ClassLabel(names=["good", "bad"]  )()
			UpperCamelCase__					=       pa_table.to_pydict()["label"]
			assert generated_content == [ClassLabel(names=["good", "bad"]  ).straint(_A  ) for label in labels]







def        UpperCamelCase_(				_A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     sep=","				,     converters={"int_list": lambda _A  : [int(_A  ) for i in x.split()]}  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_int_list]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa.types.is_list(pa_table.schema.field("int_list"  ).type  )
			UpperCamelCase__					=       pa_table.to_pydict()["int_list"]
			assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


