

'''simple docstring'''


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     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    PriorTransformer
						__UpperCAmelCase			     =    """hidden_states"""







						@property
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  4
								snake_case__						:       List[str]       						=  8
								snake_case__						:       Any       						=  7

								snake_case__						:       Any       						=  floats_tensor((batch_size, embedding_dim)  ).to(snake_case_  )

								snake_case__						:       Optional[Any]       						=  floats_tensor((batch_size, embedding_dim)  ).to(snake_case_  )
								snake_case__						:       Optional[int]       						=  floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(snake_case_  )

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







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[str]=0  ):



								'''simple docstring'''



								torch.manual_seed(snake_case_  )
								snake_case__						:       Optional[Any]       						=  4
								snake_case__						:       List[str]       						=  8
								snake_case__						:       str       						=  7

								snake_case__						:       Any       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )

								snake_case__						:       List[Any]       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(snake_case_  )

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







						@property
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								return (4, 8)







						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								return (4, 8)







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  {
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 4,
								    '''num_layers''': 2,
								    '''embedding_dim''': 8,
								    '''num_embeddings''': 7,
								    '''additional_embeddings''': 4,
								}
								snake_case__						:       str       						=  self.dummy_input
								return init_dict, inputs_dict







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



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

								model.to(snake_case_  )
								snake_case__						:       str       						=  model(**self.dummy_input  )[0]

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







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Tuple       						=  self.prepare_init_args_and_inputs_for_common()

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

								snake_case__						:       Optional[Any]       						=  ['''hidden_states''', '''timestep''']
								self.assertListEqual(arg_names[:2]			,			snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''  )
								snake_case__						:       List[str]       						=  model.to(snake_case_  )

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

								snake_case__						:       Optional[Any]       						=  self.get_dummy_seed_input()

								with torch.no_grad():
										snake_case__						:       Union[str, Any]       						=  model(**snake_case_  )[0]

								snake_case__						:       Optional[int]       						=  output[0, :5].flatten().cpu()
								print(snake_case_  )

								# 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.
								snake_case__						:       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(snake_case_			,			snake_case_			,			rtol=1e-2  )  )





@slow
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int=1			,			snake_case_					:      Any=7_6_8			,			snake_case_					:      str=7_7			,			snake_case_					:      int=0  ):



								'''simple docstring'''



								torch.manual_seed(snake_case_  )
								snake_case__						:       Tuple       						=  batch_size
								snake_case__						:       Any       						=  embedding_dim
								snake_case__						:       Any       						=  num_embeddings

								snake_case__						:       Tuple       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )

								snake_case__						:       Optional[Any]       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(snake_case_  )

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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







						@parameterized.expand(
						    [
						        # fmt: off
						        [1_3, [-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]],
						        [3_7, [-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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      Any  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''			,			subfolder='''prior'''  )
								model.to(snake_case_  )
								snake_case__						:       List[Any]       						=  self.get_dummy_seed_input(seed=snake_case_  )

								with torch.no_grad():
										snake_case__						:       str       						=  model(**snake_case_  )[0]

								assert list(sample.shape  ) == [1, 7_6_8]

								snake_case__						:       List[str]       						=  sample[0, :8].flatten().cpu()
								print(snake_case_  )
								snake_case__						:       Optional[int]       						=  torch.tensor(snake_case_  )

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






'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



