






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		if is_vision_available():
				from transformers import OneFormerImageProcessor
				from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
				from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
		from PIL import Image




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Dict="shi-labs/oneformer_demo"  ):


					'''simple docstring'''


					with open(hf_hub_download(__snake_case     ,       __snake_case     ,       repo_type='dataset'  )     ,       'r'  ) as f:
										UpperCAmelCase_    :      Optional[Any]            =						json.load(__snake_case  )
					UpperCAmelCase_    :      str            =						{}
					UpperCAmelCase_    :      str            =						[]
					UpperCAmelCase_    :      List[Any]            =						[]
					for key, info in class_info.items():
										UpperCAmelCase_    :      Dict            =						info['name']
										class_names.append(info['name']  )
										if info["isthing"]:
															thing_ids.append(int(__snake_case  )  )
					UpperCAmelCase_    :      Tuple            =						thing_ids
					UpperCAmelCase_    :      Optional[Any]            =						class_names
					return metadata



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=7				,		_UpperCamelCase=3				,		_UpperCamelCase=3_0				,		_UpperCamelCase=4_0_0				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase=True				,		_UpperCamelCase=[0.5, 0.5, 0.5]				,		_UpperCamelCase=[0.5, 0.5, 0.5]				,		_UpperCamelCase=1_0				,		_UpperCamelCase=False				,		_UpperCamelCase=2_5_5				,		_UpperCamelCase="shi-labs/oneformer_demo"				,		_UpperCamelCase="ade20k_panoptic.json"				,		_UpperCamelCase=1_0				,		)	->							Tuple:
							UpperCAmelCase_    :      Optional[int]            =						parent
							UpperCAmelCase_    :      int            =						batch_size
							UpperCAmelCase_    :      int            =						num_channels
							UpperCAmelCase_    :      int            =						min_resolution
							UpperCAmelCase_    :      Union[str, Any]            =						max_resolution
							UpperCAmelCase_    :      Optional[int]            =						do_resize
							UpperCAmelCase_    :      Any            =						{'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
							UpperCAmelCase_    :      Optional[int]            =						do_normalize
							UpperCAmelCase_    :      Optional[Any]            =						image_mean
							UpperCAmelCase_    :      List[str]            =						image_std
							UpperCAmelCase_    :      Union[str, Any]            =						class_info_file
							UpperCAmelCase_    :      str            =						prepare_metadata(_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						num_text
							UpperCAmelCase_    :      int            =						repo_path

							# for the post_process_functions
							UpperCAmelCase_    :      List[str]            =						2
							UpperCAmelCase_    :      Dict            =						1_0
							UpperCAmelCase_    :      Any            =						1_0
							UpperCAmelCase_    :      int            =						3
							UpperCAmelCase_    :      Union[str, Any]            =						4
							UpperCAmelCase_    :      Optional[int]            =						num_labels
							UpperCAmelCase_    :      int            =						do_reduce_labels
							UpperCAmelCase_    :      Tuple            =						ignore_index



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False   )	->							Dict:
							if not batched:
												UpperCAmelCase_    :      Any            =						image_inputs[0]
												if isinstance(_UpperCamelCase				,		Image.Image   ):
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						image.size
												else:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						image.shape[1], image.shape[2]
												if w < h:
																	UpperCAmelCase_    :      List[str]            =						int(self.size['shortest_edge'] * h / w   )
																	UpperCAmelCase_    :      Optional[int]            =						self.size['shortest_edge']
												elif w > h:
																	UpperCAmelCase_    :      Optional[int]            =						self.size['shortest_edge']
																	UpperCAmelCase_    :      Dict            =						int(self.size['shortest_edge'] * w / h   )
												else:
																	UpperCAmelCase_    :      Optional[Any]            =						self.size['shortest_edge']
																	UpperCAmelCase_    :      Dict            =						self.size['shortest_edge']

							else:
												UpperCAmelCase_    :      List[str]            =						[]
												for image in image_inputs:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						self.get_expected_values([image]   )
																	expected_values.append((expected_height, expected_width)   )
												UpperCAmelCase_    :      str            =						max(_UpperCamelCase				,		key=lambda _UpperCamelCase   : item[0]   )[0]
												UpperCAmelCase_    :      str            =						max(_UpperCamelCase				,		key=lambda _UpperCamelCase   : item[1]   )[1]

							return expected_height, expected_width



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)   )				,		masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)   )				,		)





@require_torch
@require_vision
class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    str       						=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
		# only for test_image_processing_common.test_image_proc_to_json_string
		_snake_case    :    Union[str, Any]       						=  image_processing_class



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						OneFormerImageProcessorTester(self   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							return self.image_processing_tester.prepare_image_processor_dict()



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      List[str]            =						self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'image_mean'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'image_std'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_normalize'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_resize'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'size'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'ignore_index'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'class_info_file'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'num_text'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'repo_path'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'metadata'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_reduce_labels'   )   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							pass



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# Initialize image_processor
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							UpperCAmelCase_    :      List[str]            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		Image.Image   )

							# Test not batched input
							UpperCAmelCase_    :      Union[str, Any]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# Initialize image_processor
							UpperCAmelCase_    :      Tuple            =						self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							UpperCAmelCase_    :      Dict            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase				,		numpify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		np.ndarray   )

							# Test not batched input
							UpperCAmelCase_    :      Optional[Any]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# Initialize image_processor
							UpperCAmelCase_    :      List[Any]            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							UpperCAmelCase_    :      str            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase				,		torchify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		torch.Tensor   )

							# Test not batched input
							UpperCAmelCase_    :      Optional[int]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=False				,		_UpperCamelCase=False				,		_UpperCamelCase="np"   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_class(**self.image_processor_dict   )
							# prepare image and target
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_tester.num_labels
							UpperCAmelCase_    :      Tuple            =						None
							UpperCAmelCase_    :      Union[str, Any]            =						None
							UpperCAmelCase_    :      Dict            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase   )
							if with_segmentation_maps:
												UpperCAmelCase_    :      Tuple            =						num_labels
												if is_instance_map:
																	UpperCAmelCase_    :      Dict            =						list(range(_UpperCamelCase   )   ) * 2
																	UpperCAmelCase_    :      Any            =						dict(enumerate(_UpperCamelCase   )   )
												UpperCAmelCase_    :      Dict            =						[
												    np.random.randint(0				,		high * 2				,		(img.size[1], img.size[0])   ).astype(np.uinta   ) for img in image_inputs
												]
												if segmentation_type == "pil":
																	UpperCAmelCase_    :      Tuple            =						[Image.fromarray(_UpperCamelCase   ) for annotation in annotations]

							UpperCAmelCase_    :      Optional[int]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		_UpperCamelCase				,		return_tensors='pt'				,		instance_id_to_semantic_id=_UpperCamelCase				,		pad_and_return_pixel_mask=_UpperCamelCase				,		)

							return inputs



		def 					__UpperCAmelCase			(							self   )	->							Any:
							pass



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							def common(_UpperCamelCase=False				,		_UpperCamelCase=None   ):
												UpperCAmelCase_    :      int            =						self.comm_get_image_processor_inputs(
												    with_segmentation_maps=_UpperCamelCase				,		is_instance_map=_UpperCamelCase				,		segmentation_type=_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						inputs['mask_labels']
												UpperCAmelCase_    :      Optional[Any]            =						inputs['class_labels']
												UpperCAmelCase_    :      Dict            =						inputs['pixel_values']
												UpperCAmelCase_    :      List[str]            =						inputs['text_inputs']

												# check the batch_size
												for mask_label, class_label, text_input in zip(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
																	self.assertEqual(mask_label.shape[0]				,		class_label.shape[0]   )
																	# this ensure padding has happened
																	self.assertEqual(mask_label.shape[1:]				,		pixel_values.shape[2:]   )
																	self.assertEqual(len(_UpperCamelCase   )				,		self.image_processing_tester.num_text   )

							common()
							common(is_instance_map=_UpperCamelCase   )
							common(is_instance_map=_UpperCamelCase				,		segmentation_type='pil'   )
							common(is_instance_map=_UpperCamelCase				,		segmentation_type='pil'   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Tuple            =						np.zeros((2_0, 5_0)   )
							UpperCAmelCase_    :      Optional[Any]            =						1
							UpperCAmelCase_    :      str            =						1
							UpperCAmelCase_    :      Union[str, Any]            =						1

							UpperCAmelCase_    :      List[Any]            =						binary_mask_to_rle(_UpperCamelCase   )
							self.assertEqual(len(_UpperCamelCase   )				,		4   )
							self.assertEqual(rle[0]				,		2_1   )
							self.assertEqual(rle[1]				,		4_5   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_tester.get_fake_oneformer_outputs()

							UpperCAmelCase_    :      str            =						fature_extractor.post_process_semantic_segmentation(_UpperCamelCase   )

							self.assertEqual(len(_UpperCamelCase   )				,		self.image_processing_tester.batch_size   )
							self.assertEqual(
							    segmentation[0].shape				,		(
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    )				,		)

							UpperCAmelCase_    :      str            =						[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
							UpperCAmelCase_    :      List[Any]            =						fature_extractor.post_process_semantic_segmentation(_UpperCamelCase				,		target_sizes=_UpperCamelCase   )

							self.assertEqual(segmentation[0].shape				,		target_sizes[0]   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Dict            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Any            =						self.image_processing_tester.get_fake_oneformer_outputs()
							UpperCAmelCase_    :      Tuple            =						image_processor.post_process_instance_segmentation(_UpperCamelCase				,		threshold=0   )

							self.assertTrue(len(_UpperCamelCase   ) == self.image_processing_tester.batch_size   )
							for el in segmentation:
												self.assertTrue('segmentation' in el   )
												self.assertTrue('segments_info' in el   )
												self.assertEqual(type(el['segments_info']   )				,		_UpperCamelCase   )
												self.assertEqual(
												    el['segmentation'].shape				,		(self.image_processing_tester.height, self.image_processing_tester.width)   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      List[Any]            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Tuple            =						self.image_processing_tester.get_fake_oneformer_outputs()
							UpperCAmelCase_    :      Optional[Any]            =						image_processor.post_process_panoptic_segmentation(_UpperCamelCase				,		threshold=0   )

							self.assertTrue(len(_UpperCamelCase   ) == self.image_processing_tester.batch_size   )
							for el in segmentation:
												self.assertTrue('segmentation' in el   )
												self.assertTrue('segments_info' in el   )
												self.assertEqual(type(el['segments_info']   )				,		_UpperCamelCase   )
												self.assertEqual(
												    el['segmentation'].shape				,		(self.image_processing_tester.height, self.image_processing_tester.width)   )







'''simple docstring'''

import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_A			:      List[Any]					      =				(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


_A			:      List[Any]					      =				logging.get_logger(__name__)  # pylint: disable=invalid-name







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						"""https://pypi.org/pypi/diffusers/json"""
 __lowerCAmelCase											=						json.loads(request.urlopen(snake_case_      ).read()      )["""releases"""].keys()
 return sorted(snake_case_						,		key=lambda snake_case_      : version.Version(snake_case_      )      )







def      UpperCamelCase_						(					)					->     int:






 '''simple docstring'''

 if HF_MODULES_CACHE in sys.path:
  return

 sys.path.append(snake_case_      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]      )					->     Dict:






 '''simple docstring'''

 init_hf_modules()
 __lowerCAmelCase											=						Path(snake_case_      ) / name
 # If the parent module does not exist yet, recursively create it.
 if not dynamic_module_path.parent.exists():
  create_dynamic_module(dynamic_module_path.parent      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						dynamic_module_path / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       int      )					->     str:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import .xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+\.(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from .xxx import yyy`
 relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Unique-ify
 return list(set(snake_case_      )      )







def      UpperCamelCase_						(					snake_case_       :       int      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						False
 __lowerCAmelCase											=						[module_file]
 __lowerCAmelCase											=						[]

 # Let's recurse through all relative imports
 while not no_change:
  __lowerCAmelCase											=						[]
  for f in files_to_check:
   new_imports.extend(get_relative_imports(snake_case_      )      )

  __lowerCAmelCase											=						Path(snake_case_      ).parent
  __lowerCAmelCase											=						[str(module_path / m      ) for m in new_imports]
  __lowerCAmelCase											=						[f for f in new_import_files if f not in all_relative_imports]
  __lowerCAmelCase											=						[f"""{f}.py""" for f in new_import_files]

  __lowerCAmelCase											=						len(snake_case_      ) == 0
  all_relative_imports.extend(snake_case_      )

 return all_relative_imports







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Optional[Any]:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import xxx`
 __lowerCAmelCase											=						re.findall("""^\s*import\s+(\S+)\s*$"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Imports of the form `from xxx import yyy`
 imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,		snake_case_						,		flags=re.MULTILINE      )
 # Only keep the top-level module
 __lowerCAmelCase											=						[imp.split("""."""      )[0] for imp in imports if not imp.startswith("""."""      )]

 # Unique-ify and test we got them all
 __lowerCAmelCase											=						list(set(snake_case_      )      )
 __lowerCAmelCase											=						[]
 for imp in imports:
  try:
   importlib.import_module(snake_case_      )
  except ImportError:
   missing_packages.append(snake_case_      )

 if len(snake_case_      ) > 0:
  raise ImportError(
      """This modeling file requires the following packages that were not found in your environment: """
      f"""{", ".join(snake_case_      )}. Run `pip install {" ".join(snake_case_      )}`"""      )

 return get_relative_imports(snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Optional[int]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						module_path.replace(os.path.sep						,		"""."""      )
 __lowerCAmelCase											=						importlib.import_module(snake_case_      )

 if class_name is None:
  return find_pipeline_class(snake_case_      )
 return getattr(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

 from ..pipelines import DiffusionPipeline

 __lowerCAmelCase											=						dict(inspect.getmembers(snake_case_						,		inspect.isclass      )      )

 __lowerCAmelCase											=						None
 for cls_name, cls in cls_members.items():
  if (
      cls_name != DiffusionPipeline.__name__
      and issubclass(cls						,		snake_case_      )
      and cls.__module__.split("""."""      )[0] != "diffusers"
  ):
   if pipeline_class is not None:
    raise ValueError(
        f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
        f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
        f""" {loaded_module}."""      )
   __lowerCAmelCase											=						cls

 return pipeline_class







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						str(snake_case_      )

 __lowerCAmelCase											=						os.path.join(snake_case_						,		snake_case_      )

 if os.path.isfile(snake_case_      ):
  __lowerCAmelCase											=						module_file_or_url
  __lowerCAmelCase											=						"""local"""
 elif pretrained_model_name_or_path.count("""/"""      ) == 0:
  __lowerCAmelCase											=						get_diffusers_versions()
  # cut ".dev0"
  __lowerCAmelCase											=						"""v""" + """.""".join(__version__.split("""."""      )[:3]      )

  # retrieve github version that matches
  if revision is None:
   __lowerCAmelCase											=						latest_version if latest_version[1:] in available_versions else """main"""
   logger.info(f"""Defaulting to latest_version: {revision}."""      )
  elif revision in available_versions:
   __lowerCAmelCase											=						f"""v{revision}"""
  elif revision == "main":
   __lowerCAmelCase											=						revision
  else:
   raise ValueError(
       f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
       f""" {", ".join(available_versions + ["main"]      )}."""      )

  # community pipeline on GitHub
  __lowerCAmelCase											=						COMMUNITY_PIPELINES_URL.format(revision=snake_case_						,		pipeline=snake_case_      )
  try:
   __lowerCAmelCase											=						cached_download(
       snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						"""git"""
   __lowerCAmelCase											=						pretrained_model_name_or_path + """.py"""
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise
 else:
  try:
   # Load from URL or cache if already cached
   __lowerCAmelCase											=						hf_hub_download(
       snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		proxies=snake_case_						,		resume_download=snake_case_						,		local_files_only=snake_case_						,		use_auth_token=snake_case_						,		)
   __lowerCAmelCase											=						os.path.join("""local"""						,		"""--""".join(pretrained_model_name_or_path.split("""/"""      )      )      )
  except EnvironmentError:
   logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
   raise

    # Check we have all the requirements in our environment
 __lowerCAmelCase											=						check_imports(snake_case_      )

 # Now we move the module inside our cached dynamic modules.
 __lowerCAmelCase											=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
 create_dynamic_module(snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / full_submodule
 if submodule == "local" or submodule == "git":
  # We always copy local files (we could hash the file to see if there was a change, and give them the name of
  # that hash, to only copy when there is a modification but it seems overkill for now).
  # The only reason we do the copy is to avoid putting too many folders in sys.path.
  shutil.copy(snake_case_						,		submodule_path / module_file      )
  for module_needed in modules_needed:
   __lowerCAmelCase											=						f"""{module_needed}.py"""
   shutil.copy(os.path.join(snake_case_						,		snake_case_      )						,		submodule_path / module_needed      )
 else:
  # Get the commit hash
  # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
  if isinstance(snake_case_						,		snake_case_      ):
   __lowerCAmelCase											=						use_auth_token
  elif use_auth_token is True:
   __lowerCAmelCase											=						HfFolder.get_token()
  else:
   __lowerCAmelCase											=						None

  __lowerCAmelCase											=						model_info(snake_case_						,		revision=snake_case_						,		token=snake_case_      ).sha

  # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
  # benefit of versioning.
  __lowerCAmelCase											=						submodule_path / commit_hash
  __lowerCAmelCase											=						full_submodule + os.path.sep + commit_hash
  create_dynamic_module(snake_case_      )

  if not (submodule_path / module_file).exists():
   shutil.copy(snake_case_						,		submodule_path / module_file      )
  # Make sure we also have every file with relative
  for module_needed in modules_needed:
   if not (submodule_path / module_needed).exists():
    get_cached_module_file(
        snake_case_						,		f"""{module_needed}.py"""						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return os.path.join(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]						,		snake_case_       :       str						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       Optional[Union[str, os.PathLike]] = None						,		snake_case_       :       bool = False						,		snake_case_       :       bool = False						,		snake_case_       :       Optional[Dict[str, str]] = None						,		snake_case_       :       Optional[Union[bool, str]] = None						,		snake_case_       :       Optional[str] = None						,		snake_case_       :       bool = False						,		**snake_case_       :       Dict						,		)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						get_cached_module_file(
     snake_case_						,		snake_case_						,		cache_dir=snake_case_						,		force_download=snake_case_						,		resume_download=snake_case_						,		proxies=snake_case_						,		use_auth_token=snake_case_						,		revision=snake_case_						,		local_files_only=snake_case_						,		)
 return get_class_in_module(snake_case_						,		final_module.replace(""".py"""						,		""""""      )      )






