

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__			:					List[Any]							  =					'''src/transformers'''

# Matches is_xxx_available()
lowerCamelCase__			:					str							  =					re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
lowerCamelCase__			:					Tuple							  =					re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
lowerCamelCase__			:					str							  =					re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


lowerCamelCase__			:					Dict							  =					'''
{0} = None
'''

lowerCamelCase__			:					Any							  =					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


lowerCamelCase__			:					Tuple							  =					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any]	)			->     Dict:
	if _re_test_backend.search(_lowerCAmelCase	) is None:
		return None
	_UpperCAmelCase      :							Union[str, Any]       =   [b[0] for b in _re_backend.findall(_lowerCAmelCase	)]
	backends.sort()
	return "_and_".join(_lowerCAmelCase	)
def  UpperCamelCase   (     )			->     str:
	with open(os.path.join(_lowerCAmelCase,					"""__init__.py"""	),					"""r""",					encoding="""utf-8""",					newline="""\n"""	) as f:
		_UpperCAmelCase      :							Optional[int]       =   f.readlines()

	# Get to the point we do the actual imports for type checking
	_UpperCAmelCase      :							Tuple       =   0
	while not lines[line_index].startswith("""if TYPE_CHECKING"""	):
		line_index += 1

	_UpperCAmelCase      :							str       =   {}
	# Go through the end of the file
	while line_index < len(_lowerCAmelCase	):
		# If the line is an if is_backend_available, we grab all objects associated.
		_UpperCAmelCase      :							str       =   find_backend(lines[line_index]	)
		if backend is not None:
			while not lines[line_index].startswith("""    else:"""	):
				line_index += 1
			line_index += 1

			_UpperCAmelCase      :							List[str]       =   []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]	) <= 1 or lines[line_index].startswith(""" """ * 8	):
				_UpperCAmelCase      :							Optional[int]       =   lines[line_index]
				_UpperCAmelCase      :							Tuple       =   _re_single_line_import.search(_lowerCAmelCase	)
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(""", """	)	)
				elif line.startswith(""" """ * 12	):
					objects.append(line[12:-2]	)
				line_index += 1

			_UpperCAmelCase      :							str       =   objects
		else:
			line_index += 1

	return backend_specific_objects
def  UpperCamelCase   (     _lowerCAmelCase       :  List[Any],					_lowerCAmelCase       :  Any	)			->     Dict:
	if name.isupper():
		return DUMMY_CONSTANT.format(_lowerCAmelCase	)
	elif name.islower():
		return DUMMY_FUNCTION.format(_lowerCAmelCase,					_lowerCAmelCase	)
	else:
		return DUMMY_CLASS.format(_lowerCAmelCase,					_lowerCAmelCase	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Union[str, Any]=None	)			->     List[Any]:
	if backend_specific_objects is None:
		_UpperCAmelCase      :							Tuple       =   read_init()
	# For special correspondence backend to module name as used in the function requires_modulename
	_UpperCAmelCase      :							Any       =   {}

	for backend, objects in backend_specific_objects.items():
		_UpperCAmelCase      :							Any       =   """[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""	)	) + """]"""
		_UpperCAmelCase      :							Optional[Any]       =   """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
		dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
		dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase,					_lowerCAmelCase	) for o in objects]	)
		_UpperCAmelCase      :							Dict       =   dummy_file

	return dummy_files
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple=False	)			->     Tuple:
	_UpperCAmelCase      :							Union[str, Any]       =   create_dummy_files()
	# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
	_UpperCAmelCase      :							List[Any]       =   {"""torch""": """pt"""}

	# Locate actual dummy modules and read their content.
	_UpperCAmelCase      :							Tuple       =   os.path.join(_lowerCAmelCase,					"""utils"""	)
	_UpperCAmelCase      :							List[str]       =   {
	    backend: os.path.join(_lowerCAmelCase,					f'''dummy_{short_names.get(_lowerCAmelCase,					_lowerCAmelCase	)}_objects.py'''	)
	    for backend in dummy_files.keys()
	}

	_UpperCAmelCase      :							List[str]       =   {}
	for backend, file_path in dummy_file_paths.items():
		if os.path.isfile(_lowerCAmelCase	):
			with open(_lowerCAmelCase,					"""r""",					encoding="""utf-8""",					newline="""\n"""	) as f:
				_UpperCAmelCase      :							int       =   f.read()
		else:
			_UpperCAmelCase      :							Tuple       =   """"""

	for backend in dummy_files.keys():
		if dummy_files[backend] != actual_dummies[backend]:
			if overwrite:
				print(
				    f'''Updating transformers.utils.dummy_{short_names.get(_lowerCAmelCase,					_lowerCAmelCase	)}_objects.py as the main '''
				    """__init__ has new objects."""	)
				with open(dummy_file_paths[backend],					"""w""",					encoding="""utf-8""",					newline="""\n"""	) as f:
					f.write(dummy_files[backend]	)
			else:
				raise ValueError(
				    """The main __init__ has objects that are not present in """
				    f'''transformers.utils.dummy_{short_names.get(_lowerCAmelCase,					_lowerCAmelCase	)}_objects.py. Run `make fix-copies` '''
				    """to fix this."""	)


if __name__ == "__main__":
						lowerCamelCase__			:					Optional[Any]							  =					argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						lowerCamelCase__			:					List[Any]							  =					parser.parse_args()

						check_dummies(args.fix_and_overwrite)








"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCAmelCase     (						__a      ,       unittest.TestCase):
			__a	:    List[Any]			   =							KandinskyPriorPipeline
			__a	:    List[Any]			   =							["""prompt"""]
			__a	:    List[str]			   =							["""prompt""", """negative_prompt"""]
			__a	:    Tuple			   =							[
			    """num_images_per_prompt""",
			    """generator""",
			    """num_inference_steps""",
			    """latents""",
			    """negative_prompt""",
			    """guidance_scale""",
			    """output_type""",
			    """return_dict""",
			]
			__a	:    str			   =							False
			@property
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				return self.time_input_dim
			@property
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return self.time_input_dim * 4
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				return 1_00
			@property
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
				return tokenizer
			@property
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							List[Any]       =   CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=self.text_embedder_hidden_size ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=10_00 ,						)
				return CLIPTextModelWithProjection(_A       )
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				torch.manual_seed(0       )

				_UpperCAmelCase      :							Optional[int]       =   {
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 12,
				    """embedding_dim""": self.text_embedder_hidden_size,
				    """num_layers""": 1,
				}

				_UpperCAmelCase      :							List[Any]       =   PriorTransformer(**_A       )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				_UpperCAmelCase      :							List[Any]       =   nn.Parameter(torch.ones(model.clip_std.shape       )       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							Optional[Any]       =   CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size ,						image_size=2_24 ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						num_attention_heads=4 ,						num_channels=3 ,						num_hidden_layers=5 ,						patch_size=14 ,						)

				_UpperCAmelCase      :							Tuple       =   CLIPVisionModelWithProjection(_A       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   CLIPImageProcessor(
				    crop_size=2_24 ,						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=2_24 ,						)

				return image_processor
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.dummy_prior
				_UpperCAmelCase      :							int       =   self.dummy_image_encoder
				_UpperCAmelCase      :							int       =   self.dummy_text_encoder
				_UpperCAmelCase      :							str       =   self.dummy_tokenizer
				_UpperCAmelCase      :							Any       =   self.dummy_image_processor

				_UpperCAmelCase      :							Any       =   UnCLIPScheduler(
				    variance_type="""fixed_small_log""" ,						prediction_type="""sample""" ,						num_train_timesteps=10_00 ,						clip_sample=_A ,						clip_sample_range=10.0 ,						)

				_UpperCAmelCase      :							Any       =   {
				    """prior""": prior,
				    """image_encoder""": image_encoder,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """scheduler""": scheduler,
				    """image_processor""": image_processor,
				}

				return components
			def 							__snake_case	(						self ,						_A ,						_A=0       )      ->      Optional[Any]:






				'''simple docstring'''







				if str(_A       ).startswith("""mps"""       ):
					_UpperCAmelCase      :							Any       =   torch.manual_seed(_A       )
				else:
					_UpperCAmelCase      :							Optional[int]       =   torch.Generator(device=_A       ).manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   {
				    """prompt""": """horse""",
				    """generator""": generator,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   """cpu"""

				_UpperCAmelCase      :							int       =   self.get_dummy_components()

				_UpperCAmelCase      :							Optional[Any]       =   self.pipeline_class(**_A       )
				_UpperCAmelCase      :							List[Any]       =   pipe.to(_A       )

				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   pipe(**self.get_dummy_inputs(_A       )       )
				_UpperCAmelCase      :							Any       =   output.image_embeds

				_UpperCAmelCase      :							Optional[int]       =   pipe(
				    **self.get_dummy_inputs(_A       ) ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							int       =   image[0, -10:]
				_UpperCAmelCase      :							Optional[Any]       =   image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				_UpperCAmelCase      :							Optional[Any]       =   np.array(
				    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			@skip_mps
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   torch_device == """cpu"""
				_UpperCAmelCase      :							Optional[Any]       =   True
				_UpperCAmelCase      :							List[Any]       =   False

				self._test_inference_batch_single_identical(
				    test_max_difference=_A ,						relax_max_difference=_A ,						test_mean_pixel_difference=_A ,						)
			@skip_mps
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   torch_device == """cpu"""
				_UpperCAmelCase      :							List[str]       =   False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_A ,						test_mean_pixel_difference=_A ,						)





