




"""simple docstring"""

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCamelCase										=    logging.getLogger()



@unittest.skip("""Temporarily disable the doc tests."""							)
@require_torch
@require_tf
@slow
class 			_snake_case					(				unittest.TestCase							):

				'''simple docstring'''




				def 					snake_case_			(				self   :	Optional[int]       ,				snake_case   :	Optional[int]       ,				snake_case   :	Optional[Any] = None       ,				snake_case   :	int = None       ,				snake_case   :	Optional[int] = None       ,				snake_case   :	Optional[int] = True       ,				):
								UpperCAmelCase_       :Tuple         =   [file for file in os.listdir(__A  ) if os.path.isfile(os.path.join(__A       ,				__A  )  )]

								if identifier is not None:
												UpperCAmelCase_       :Any         =   [file for file in files if identifier in file]

								if n_identifier is not None:
												if isinstance(__A       ,				__A  ):
																for n_ in n_identifier:
																				UpperCAmelCase_       :Dict         =   [file for file in files if n_ not in file]
												else:
																UpperCAmelCase_       :Dict         =   [file for file in files if n_identifier not in file]

								UpperCAmelCase_       :str         =   ignore_files or []
								ignore_files.append('''__init__.py'''  )
								UpperCAmelCase_       :Union[str, Any]         =   [file for file in files if file not in ignore_files]

								for file in files:
												# Open all files
												print('''Testing'''       ,				__A  )

												if only_modules:
																UpperCAmelCase_       :List[Any]         =   file.split('''.'''  )[0]
																try:
																				UpperCAmelCase_       :Union[str, Any]         =   getattr(__A       ,				__A  )
																				UpperCAmelCase_       :int         =   doctest.DocTestSuite(__A  )
																				UpperCAmelCase_       :Optional[int]         =   unittest.TextTestRunner().run(__A  )
																				self.assertIs(len(result.failures  )       ,				0  )
																except AttributeError:
																				logger.info(f'{module_identifier} is not a module.'  )
												else:
																UpperCAmelCase_       :int         =   doctest.testfile(str('''..''' / directory / file  )       ,				optionflags=doctest.ELLIPSIS  )
																self.assertIs(result.failed       ,				0  )





				def 					snake_case_			(				self   :	Optional[int]  ):
								UpperCAmelCase_       :Tuple         =   Path('''src/transformers'''  )
								UpperCAmelCase_       :int         =   '''modeling'''
								UpperCAmelCase_       :Any         =   [
								    '''modeling_ctrl.py''',
								    '''modeling_tf_ctrl.py''',
								]
								self.analyze_directory(__A       ,				identifier=__A       ,				ignore_files=__A  )





				def 					snake_case_			(				self   :	int  ):
								UpperCAmelCase_       :Dict         =   Path('''src/transformers'''  )
								UpperCAmelCase_       :int         =   '''tokenization'''
								self.analyze_directory(__A       ,				identifier=__A  )





				def 					snake_case_			(				self   :	List[Any]  ):
								UpperCAmelCase_       :Optional[Any]         =   Path('''src/transformers'''  )
								UpperCAmelCase_       :str         =   '''configuration'''
								self.analyze_directory(__A       ,				identifier=__A  )





				def 					snake_case_			(				self   :	Dict  ):
								UpperCAmelCase_       :str         =   Path('''src/transformers'''  )
								UpperCAmelCase_       :int         =   ['''configuration''', '''modeling''', '''tokenization''']
								self.analyze_directory(__A       ,				n_identifier=__A  )





				def 					snake_case_			(				self   :	Optional[int]  ):
								UpperCAmelCase_       :List[str]         =   Path('''docs/source'''  )
								UpperCAmelCase_       :Tuple         =   ['''favicon.ico''']
								self.analyze_directory(__A       ,				ignore_files=__A       ,				only_modules=__A  )




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 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

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


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # 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.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

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

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
