





import argparse
import os
import re


A       :   Any					= """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A       :   Dict					= re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
A       :   List[str]					= re.compile(R'''\s*\(\s*\"(\S[^\"]+)\"''')







def   __lowerCamelCase   (				__a  :int       ,	__a  :bool = False       )						->							List[str]:






		"""simple docstring"""

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

		A__     =  content.split("""\n"""       )
		A__     =  []
		A__     =  0
		while line_idx < len(_lowercase       ):
				if _re_intro_mapping.search(lines[line_idx]       ) is not None:
						A__     =  len(re.search(R"""^(\s*)\S"""       ,	lines[line_idx]       ).groups()[0]       ) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(""" """ * indent + """("""       ):
								new_lines.append(lines[line_idx]       )
								line_idx += 1

						A__     =  []
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										A__     =  line_idx
										while not lines[line_idx].startswith(""" """ * indent + """)"""       ):
												line_idx += 1
										blocks.append("""\n""".join(lines[start_idx : line_idx + 1]       )       )
								else:
										blocks.append(lines[line_idx]       )
								line_idx += 1

						# Sort blocks by their identifiers
						A__     =  sorted(_lowercase       ,	key=lambda __a       : _re_identifier.search(_lowercase       ).groups()[0]       )
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]       )
						line_idx += 1

		if overwrite:
				with open(_lowercase       ,	"""w"""       ,	encoding="""utf-8"""       ) as f:
						f.write("""\n""".join(_lowercase       )       )
		elif "\n".join(_lowercase       ) != content:
				return True







def   __lowerCamelCase   (				__a  :bool = False       )						->							List[Any]:






		"""simple docstring"""

		A__     =  [os.path.join(_lowercase       ,	_lowercase       ) for f in os.listdir(_lowercase       ) if f.endswith(""".py"""       )]
		A__     =  [sort_auto_mapping(_lowercase       ,	overwrite=_lowercase       ) for fname in fnames]

		if not overwrite and any(_lowercase       ):
				A__     =  [f for f, d in zip(_lowercase       ,	_lowercase       ) if d]
				raise ValueError(
				    F'The following files have auto mappings that need sorting: {", ".join(_lowercase       )}. Run `make style` to fix'
				    """ this."""       )


if __name__ == "__main__":
					A       :   Tuple					= argparse.ArgumentParser()
					parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
					A       :   List[Any]					= parser.parse_args()

					sort_all_auto_mappings(not args.check_only)










import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PriorTransformer
	_A													=				"hidden_states"

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      List[Any]      =		4
			lowercase__					:      Optional[int]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Optional[Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      Any      =		floats_tensor((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	def    snake_case__(						self:						int,					lowerCamelCase_:						Dict=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Tuple      =		4
			lowercase__					:      List[Any]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Union[str, Any]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.randn((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	@property
	def    snake_case__(						self:						str			):
			return (4, 8)

	@property
	def    snake_case__(						self:						List[str]			):
			return (4, 8)

	def    snake_case__(						self:						Dict			):
			lowercase__					:      int      =		{
			    'num_attention_heads': 2,
			    'attention_head_dim': 4,
			    'num_layers': 2,
			    'embedding_dim': 8,
			    'num_embeddings': 7,
			    'additional_embeddings': 4,
			}
			lowercase__					:      Optional[Any]      =		self.dummy_input
			return init_dict, inputs_dict

	def    snake_case__(						self:						int			):
			lowercase__   ,				lowercase__					:      Dict      =		PriorTransformer.from_pretrained(
			    'hf-internal-testing/prior-dummy',					output_loading_info=lowerCamelCase_			)
			self.assertIsNotNone(lowerCamelCase_			)
			self.assertEqual(len(loading_info['missing_keys']			),					0			)

			model.to(lowerCamelCase_			)
			lowercase__					:      Tuple      =		model(**self.dummy_input			)[0]

			assert hidden_states is not None, "Make sure output is not None"

	def    snake_case__(						self:						str			):
			lowercase__   ,				lowercase__					:      List[Any]      =		self.prepare_init_args_and_inputs_for_common()

			lowercase__					:      Optional[int]      =		self.model_class(**lowerCamelCase_			)
			lowercase__					:      int      =		inspect.signature(model.forward			)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			lowercase__					:      Dict      =		[*signature.parameters.keys()]

			lowercase__					:      List[str]      =		['hidden_states', 'timestep']
			self.assertListEqual(arg_names[:2],					lowerCamelCase_			)





	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
			lowercase__					:      Optional[int]      =		model.to(lowerCamelCase_			)

			if hasattr(lowerCamelCase_,					'set_default_attn_processor'			):
					model.set_default_attn_processor()

			lowercase__					:      List[str]      =		self.get_dummy_seed_input()

			with torch.no_grad():
					lowercase__					:      str      =		model(**lowerCamelCase_			)[0]

			lowercase__					:      int      =		output[0, :5].flatten().cpu()
			print(lowerCamelCase_			)

			# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
			# the expected output slices are not the same for CPU and GPU.
			lowercase__					:      List[str]      =		torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]			)
			self.assertTrue(torch_all_close(lowerCamelCase_,					lowerCamelCase_,					rtol=1E-2			)			)


@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Union[str, Any]=1,					lowerCamelCase_:						Tuple=768,					lowerCamelCase_:						Dict=77,					lowerCamelCase_:						Union[str, Any]=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Dict      =		batch_size
			lowercase__					:      Dict      =		embedding_dim
			lowercase__					:      Dict      =		num_embeddings

			lowercase__					:      int      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      str      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.randn((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	def    snake_case__(						self:						str			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
	        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
	        # fmt: on
	    ]			)
	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						str			):
			lowercase__					:      List[Any]      =		PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',					subfolder='prior'			)
			model.to(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.get_dummy_seed_input(seed=lowerCamelCase_			)

			with torch.no_grad():
					lowercase__					:      List[str]      =		model(**lowerCamelCase_			)[0]

			assert list(sample.shape			) == [1, 768]

			lowercase__					:      Union[str, Any]      =		sample[0, :8].flatten().cpu()
			print(lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		torch.tensor(lowerCamelCase_			)

			assert torch_all_close(lowerCamelCase_,					lowerCamelCase_,					atol=1E-3			)


