

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



   @property
   def 	__magic_name__   (							self							:					Optional[Any]						):
     return 3_2



   @property
   def 	__magic_name__   (							self							:					Optional[int]						):
     return 3_2



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return self.time_input_dim * 4



   @property
   def 	__magic_name__   (							self							:					Any						):
     return 8



   @property
   def 	__magic_name__   (							self							:					List[str]						):
     torch.manual_seed(0						)
     UpperCAmelCase	:							str						=			CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size,		image_size=6_4,		projection_dim=self.text_embedder_hidden_size,		intermediate_size=3_7,		num_attention_heads=4,		num_channels=3,		num_hidden_layers=5,		patch_size=1,		)

     UpperCAmelCase	:							Any						=			CLIPVisionModel(__A						)
     return model



   @property
   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			CLIPImageProcessor(
         crop_size=2_2_4,		do_center_crop=__A,		do_normalize=__A,		do_resize=__A,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],		resample=3,		size=2_2_4,		)

     return image_processor



   @property
   def 	__magic_name__   (							self							:					Dict						):
     torch.manual_seed(0						)

     UpperCAmelCase	:							Any						=			{
         '''num_attention_heads''': 2,
         '''attention_head_dim''': 1_6,
         '''embedding_dim''': self.time_input_dim,
         '''num_embeddings''': 3_2,
         '''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	:							List[Any]						=			PriorTransformer(**__A						)
     return model



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     torch.manual_seed(0						)

     UpperCAmelCase	:							List[Any]						=			{
         '''param_shapes''': (
             (self.renderer_dim, 9_3),
             (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''': 1_2,
         '''background''': (
             0.1,
             0.1,
             0.1,
         ),
     }
     UpperCAmelCase	:							List[str]						=			ShapERenderer(**__A						)
     return model



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							str						=			self.dummy_prior
     UpperCAmelCase	:							List[str]						=			self.dummy_image_encoder
     UpperCAmelCase	:							List[Any]						=			self.dummy_image_processor
     UpperCAmelCase	:							Dict						=			self.dummy_renderer

     UpperCAmelCase	:							int						=			HeunDiscreteScheduler(
         beta_schedule='''exp''',		num_train_timesteps=1_0_2_4,		prediction_type='''sample''',		use_karras_sigmas=__A,		clip_sample=__A,		clip_sample_range=1.0,		)
     UpperCAmelCase	:							List[str]						=			{
         '''prior''': prior,
         '''image_encoder''': image_encoder,
         '''image_processor''': image_processor,
         '''renderer''': renderer,
         '''scheduler''': scheduler,
     }

     return components



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Dict,		__A							:					List[Any]=0						):
     UpperCAmelCase	:							int						=			floats_tensor((1, 3, 6_4, 6_4),		rng=random.Random(__A						)						).to(__A						)

     if str(__A						).startswith('''mps'''						):
       UpperCAmelCase	:							List[str]						=			torch.manual_seed(__A						)
     else:
       UpperCAmelCase	:							Optional[Any]						=			torch.Generator(device=__A						).manual_seed(__A						)
     UpperCAmelCase	:							Dict						=			{
         '''image''': input_image,
         '''generator''': generator,
         '''num_inference_steps''': 1,
         '''frame_size''': 3_2,
         '''output_type''': '''np''',
     }
     return inputs



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Dict						=			'''cpu'''

     UpperCAmelCase	:							Any						=			self.get_dummy_components()

     UpperCAmelCase	:							Tuple						=			self.pipeline_class(**__A						)
     UpperCAmelCase	:							Dict						=			pipe.to(__A						)

     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[Any]						=			pipe(**self.get_dummy_inputs(__A						)						)
     UpperCAmelCase	:							Any						=			output.images[0]
     UpperCAmelCase	:							List[str]						=			image[0, -3:, -3:, -1]

     assert image.shape == (2_0, 3_2, 3_2, 3)

     UpperCAmelCase	:							Optional[int]						=			np.array(
         [
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
         ]						)

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



   def 	__magic_name__   (							self							:					Dict						):
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Tuple						=			torch_device == '''cpu'''
     UpperCAmelCase	:							List[Any]						=			True
     self._test_inference_batch_single_identical(
         batch_size=2,		test_max_difference=__A,		relax_max_difference=__A,		)





   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Dict						=			self.get_dummy_components()
     UpperCAmelCase	:							Union[str, Any]						=			self.pipeline_class(**__A						)
     UpperCAmelCase	:							List[str]						=			pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Any						=			1
     UpperCAmelCase	:							Any						=			2

     UpperCAmelCase	:							Optional[int]						=			self.get_dummy_inputs(__A						)

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

     UpperCAmelCase	:							int						=			pipe(**__A,		num_images_per_prompt=__A						)[0]

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






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



   def 	__magic_name__   (							self							:					List[Any]						):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							List[str]						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''						)
     UpperCAmelCase	:							Dict						=			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/shap_e/test_shap_e_img2img_out.npy'''						)
     UpperCAmelCase	:							Union[str, Any]						=			ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''						)
     UpperCAmelCase	:							int						=			pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Dict						=			torch.Generator(device=__A						).manual_seed(0						)

     UpperCAmelCase	:							List[str]						=			pipe(
         __A,		generator=__A,		guidance_scale=3.0,		num_inference_steps=6_4,		frame_size=6_4,		output_type='''np''',		).images[0]

     assert images.shape == (2_0, 6_4, 6_4, 3)

     assert_mean_pixel_difference(__A,		__A						)








import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


_lowerCamelCase    :       Optional[Any]        =							get_logger(__name__)





class  __UpperCAmelCase					:



   def __init__(							self							:					Any,		__A							:					Optional[str] = None						):
     UpperCAmelCase	:							str						=			(
         os.path.join(__A,		config.EXTRACTED_DATASETS_DIR						) if cache_dir else config.EXTRACTED_DATASETS_PATH
     )
     UpperCAmelCase	:							str						=			Extractor



   def 	__magic_name__   (							self							:					str,		__A							:					str						):
     from .file_utils import hash_url_to_filename

     # Path where we extract compressed archives
     # We extract in the cache dir, and get the extracted path name by hashing the original path"
     UpperCAmelCase	:							int						=			os.path.abspath(__A						)
     return os.path.join(self.extract_dir,		hash_url_to_filename(__A						)						)



   def 	__magic_name__   (							self							:					int,		__A							:					str,		__A							:					bool						):
     return force_extract or (
         not os.path.isfile(__A						) and not (os.path.isdir(__A						) and os.listdir(__A						))
     )





   def 	__magic_name__   (							self							:					str,		__A							:					str,		__A							:					bool = False						):
     UpperCAmelCase	:							Any						=			self.extractor.infer_extractor_format(__A						)
     if not extractor_format:
       return input_path
     UpperCAmelCase	:							Tuple						=			self._get_output_path(__A						)
     if self._do_extract(__A,		__A						):
       self.extractor.extract(__A,		__A,		__A						)
     return output_path






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @classmethod
   @abstractmethod
   def 	__magic_name__   (							cls							:					int,		__A							:					Union[Path, str],		**__A							:					List[Any]						):
     ...





   @staticmethod
   @abstractmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     ...






class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__						):
   UpperCamelCase       					=					[]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					int						):
     with open(__A,		'''rb'''						) as f:
       return f.read(__A						)





   @classmethod
   def 	__magic_name__   (							cls							:					List[str],		__A							:					Union[Path, str],		__A							:					bytes = b""						):
     if not magic_number:
       UpperCAmelCase	:							int						=			max(len(__A						) for cls_magic_number in cls.magic_numbers						)
       try:
         UpperCAmelCase	:							Any						=			cls.read_magic_number(__A,		__A						)
       except OSError:
         return False
     return any(magic_number.startswith(__A						) for cls_magic_number in cls.magic_numbers						)






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @classmethod
   def 	__magic_name__   (							cls							:					Union[str, Any],		__A							:					Union[Path, str],		**__A							:					int						):
     return tarfile.is_tarfile(__A						)



   @staticmethod
   def 	__magic_name__   (							__A							:					List[Any],		__A							:					Any						):

     def resolved(__A							:					str						) -> str:
       return os.path.realpath(os.path.abspath(__A						)						)

     def badpath(__A							:					str,		__A							:					str						) -> bool:
       # joinpath will ignore base if path is absolute
       return not resolved(os.path.join(__A,		__A						)						).startswith(__A						)

     def badlink(__A							:					List[Any],		__A							:					str						) -> bool:
       # Links are interpreted relative to the directory containing the link
       UpperCAmelCase	:							Dict						=			resolved(os.path.join(__A,		os.path.dirname(info.name						)						)						)
       return badpath(info.linkname,		base=__A						)

     UpperCAmelCase	:							Any						=			resolved(__A						)

     for finfo in members:
       if badpath(finfo.name,		__A						):
         logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)'''						)
       elif finfo.issym() and badlink(__A,		__A						):
         logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}'''						)
       elif finfo.islnk() and badlink(__A,		__A						):
         logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}'''						)
       else:
         yield finfo





   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     os.makedirs(__A,		exist_ok=__A						)
     UpperCAmelCase	:							Any						=			tarfile.open(__A						)
     tar_file.extractall(__A,		members=TarExtractor.safemembers(__A,		__A						)						)
     tar_file.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x1F\x8B"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with gzip.open(__A,		'''rb'''						) as gzip_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[
       B"""PK\x03\x04""",
       B"""PK\x05\x06""",  # empty archive
       B"""PK\x07\x08""",  # spanned archive
   ]



   @classmethod
   def 	__magic_name__   (							cls							:					Union[str, Any],		__A							:					Union[Path, str],		__A							:					bytes = b""						):
     if super().is_extractable(__A,		magic_number=__A						):
       return True
     try:
       # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
       # From: https://github.com/python/cpython/pull/5053
       from zipfile import (
           _CD_SIGNATURE,
           _ECD_DISK_NUMBER,
           _ECD_DISK_START,
           _ECD_ENTRIES_TOTAL,
           _ECD_OFFSET,
           _ECD_SIZE,
           _EndRecData,
           sizeCentralDir,
           stringCentralDir,
           structCentralDir,
       )

       with open(__A,		'''rb'''						) as fp:
         UpperCAmelCase	:							Union[str, Any]						=			_EndRecData(__A						)
         if endrec:
           if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
             return True  # Empty zipfiles are still zipfiles
           elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
             fp.seek(endrec[_ECD_OFFSET]						)  # Central directory is on the same disk
             if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
               UpperCAmelCase	:							Optional[int]						=			fp.read(__A						)  # CD is where we expect it to be
               if len(__A						) == sizeCentralDir:
                 UpperCAmelCase	:							List[Any]						=			struct.unpack(__A,		__A						)  # CD is the right size
                 if centdir[_CD_SIGNATURE] == stringCentralDir:
                   return True  # First central directory entry  has correct magic number
       return False
     except Exception:  # catch all errors in case future python versions change the zipfile internals
       return False





   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     os.makedirs(__A,		exist_ok=__A						)
     with zipfile.ZipFile(__A,		'''r'''						) as zip_file:
       zip_file.extractall(__A						)
       zip_file.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\xFD\x37\x7A\x58\x5A\x00"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with lzma.open(__A						) as compressed_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.RARFILE_AVAILABLE:
       raise ImportError('''Please pip install rarfile'''						)
     import rarfile

     os.makedirs(__A,		exist_ok=__A						)
     UpperCAmelCase	:							Tuple						=			rarfile.RarFile(__A						)
     rf.extractall(__A						)
     rf.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x28\xb5\x2F\xFD"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.ZSTANDARD_AVAILABLE:
       raise ImportError('''Please pip install zstandard'''						)
     import zstandard as zstd

     UpperCAmelCase	:							List[str]						=			zstd.ZstdDecompressor()
     with open(__A,		'''rb'''						) as ifh, open(__A,		'''wb'''						) as ofh:
       dctx.copy_stream(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x42\x5A\x68"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with bza.open(__A,		'''rb'''						) as compressed_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x37\x7A\xBC\xAF\x27\x1C"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.PY7ZR_AVAILABLE:
       raise ImportError('''Please pip install py7zr'''						)
     import pyazr

     os.makedirs(__A,		exist_ok=__A						)
     with pyazr.SevenZipFile(__A,		'''r'''						) as archive:
       archive.extractall(__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x04\x22\x4D\x18"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.LZ4_AVAILABLE:
       raise ImportError('''Please pip install lz4'''						)
     import lza.frame

     with lza.frame.open(__A,		'''rb'''						) as compressed_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					:
   #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
   UpperCamelCase       					=					{
       "tar": TarExtractor,
       "gzip": GzipExtractor,
       "zip": ZipExtractor,
       "xz": XzExtractor,
       "rar": RarExtractor,
       "zstd": ZstdExtractor,
       "bz2": BzipaExtractor,
       "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
       "lz4": LzaExtractor,  # <Added version="2.4.0"/>
   }



   @classmethod
   def 	__magic_name__   (							cls							:					Dict						):
     return max(
         len(__A						)
         for extractor in cls.extractors.values()
         if issubclass(__A,		__A						)
         for extractor_magic_number in extractor.magic_numbers						)



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					int						):
     try:
       return MagicNumberBaseExtractor.read_magic_number(__A,		magic_number_length=__A						)
     except OSError:
       return b""



   @classmethod
   def 	__magic_name__   (							cls							:					Dict,		__A							:					Union[Path, str],		__A							:					bool = False						):
     warnings.warn(
         '''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
         '''Use \'infer_extractor_format\' instead.''',		category=__A,		)
     UpperCAmelCase	:							Dict						=			cls.infer_extractor_format(__A						)
     if extractor_format:
       return True if not return_extractor else (True, cls.extractors[extractor_format])
     return False if not return_extractor else (False, None)



   @classmethod
   def 	__magic_name__   (							cls							:					Optional[Any],		__A							:					Union[Path, str]						):  # <Added version="2.4.0"/>
     UpperCAmelCase	:							Tuple						=			cls._get_magic_number_max_length()
     UpperCAmelCase	:							Tuple						=			cls._read_magic_number(__A,		__A						)
     for extractor_format, extractor in cls.extractors.items():
       if extractor.is_extractable(__A,		magic_number=__A						):
         return extractor_format





   @classmethod
   def 	__magic_name__   (							cls							:					Optional[Any],		__A							:					Union[Path, str],		__A							:					Union[Path, str],		__A							:					Optional[str] = None,		__A							:					Optional[BaseExtractor] = "deprecated",		):
     os.makedirs(os.path.dirname(__A						),		exist_ok=__A						)
     # Prevent parallel extractions
     UpperCAmelCase	:							Optional[int]						=			str(Path(__A						).with_suffix('''.lock'''						)						)
     with FileLock(__A						):
       shutil.rmtree(__A,		ignore_errors=__A						)
       if extractor_format or extractor != "deprecated":
         if extractor != "deprecated" or not isinstance(__A,		__A						):  # passed as positional arg
           warnings.warn(
               '''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
               '''Use \'extractor_format\' instead.''',		category=__A,		)
           UpperCAmelCase	:							List[str]						=			extractor if extractor != '''deprecated''' else extractor_format
         else:
           UpperCAmelCase	:							Optional[int]						=			cls.extractors[extractor_format]
         return extractor.extract(__A,		__A						)
       else:
         warnings.warn(
             '''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
             '''exception in 3.0.0.''',		category=__A,		)
         for extractor in cls.extractors.values():
           if extractor.is_extractable(__A						):
             return extractor.extract(__A,		__A						)





