import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_lowerCamelCase    :      Optional[Any]	 =						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Dict = True     ):
					SCREAMING_SNAKE_CASE				=				{}
					for name in model.attn_processors.keys():
										SCREAMING_SNAKE_CASE				=				name.endswith("attn2.processor"     ) and not (
										    name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
										)
										SCREAMING_SNAKE_CASE				=				model.config.cross_attention_dim if has_cross_attention else None
										if name.startswith("mid_block"     ):
															SCREAMING_SNAKE_CASE				=				model.config.block_out_channels[-1]
										elif name.startswith("up_blocks"     ):
															SCREAMING_SNAKE_CASE				=				int(name[len("up_blocks."     )]     )
															SCREAMING_SNAKE_CASE				=				list(reversed(model.config.block_out_channels     )     )[block_id]
										elif name.startswith("down_blocks"     ):
															SCREAMING_SNAKE_CASE				=				int(name[len("down_blocks."     )]     )
															SCREAMING_SNAKE_CASE				=				model.config.block_out_channels[block_id]
										elif name.startswith("transformer_in"     ):
															# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
															SCREAMING_SNAKE_CASE				=				8 * model.config.attention_head_dim

										SCREAMING_SNAKE_CASE				=				LoRAAttnProcessor(hidden_size=UpperCAmelCase__					,       cross_attention_dim=UpperCAmelCase__     )
										SCREAMING_SNAKE_CASE				=				lora_attn_procs[name].to(model.device     )

										if mock_weights:
															# add 1 to weights to mock trained weights
															with torch.no_grad():
																				lora_attn_procs[name].to_q_lora.up.weight += 1
																				lora_attn_procs[name].to_k_lora.up.weight += 1
																				lora_attn_procs[name].to_v_lora.up.weight += 1
																				lora_attn_procs[name].to_out_lora.up.weight += 1

					return lora_attn_procs


@skip_mps
class   lowercase			(   SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__ ,				unittest.TestCase   ):
							lowercase__  :				int									=							UNetaDConditionModel
							lowercase__  :				int									=							"""sample"""




							@property
							def 		__snake_case(  self      :    Dict					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				4
												SCREAMING_SNAKE_CASE				=				4
												SCREAMING_SNAKE_CASE				=				4
												SCREAMING_SNAKE_CASE				=				(32, 32)

												SCREAMING_SNAKE_CASE				=				floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(snake_case__					)
												SCREAMING_SNAKE_CASE				=				torch.tensor([10]					).to(snake_case__					)
												SCREAMING_SNAKE_CASE				=				floats_tensor((batch_size, 4, 32)					).to(snake_case__					)

												return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}




							@property
							def 		__snake_case(  self      :    str					)    -> Any:



												'''simple docstring'''

												return (4, 4, 32, 32)




							@property
							def 		__snake_case(  self      :    List[str]					)    -> int:



												'''simple docstring'''

												return (4, 4, 32, 32)




							def 		__snake_case(  self      :    Any					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				{
												    "block_out_channels": (32, 64),
												    "down_block_types": (
												        "CrossAttnDownBlock3D",
												        "DownBlock3D",
												    ),
												    "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
												    "cross_attention_dim": 32,
												    "attention_head_dim": 8,
												    "out_channels": 4,
												    "in_channels": 4,
												    "layers_per_block": 1,
												    "sample_size": 32,
												}
												SCREAMING_SNAKE_CASE				=				self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()		,							reason="XFormers attention is only available with CUDA and `xformers` installed"		,							)
							def 		__snake_case(  self      :    List[Any]					)    -> List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)

												model.enable_xformers_memory_efficient_attention()

												assert (
												    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
												    == "XFormersAttnProcessor"
												), "xformers is not enabled"




							def 		__snake_case(  self      :    Tuple					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				32

												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)
												model.eval()

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)

																	if isinstance(snake_case__		,							snake_case__					):
																						SCREAMING_SNAKE_CASE				=				output.sample

												self.assertIsNotNone(snake_case__					)
												SCREAMING_SNAKE_CASE				=				inputs_dict["sample"].shape
												self.assertEqual(output.shape		,							snake_case__		,							"Input and output shapes do not match"					)




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)
												model.eval()

												with torch.no_grad():
																	# Warmup pass when using mps (see #372)
																	if torch_device == "mps" and isinstance(snake_case__		,							snake_case__					):
																						model(**self.dummy_input					)

																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)
																	if isinstance(snake_case__		,							snake_case__					):
																						SCREAMING_SNAKE_CASE				=				first.sample

																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)
																	if isinstance(snake_case__		,							snake_case__					):
																						SCREAMING_SNAKE_CASE				=				second.sample

												SCREAMING_SNAKE_CASE				=				first.cpu().numpy()
												SCREAMING_SNAKE_CASE				=				second.cpu().numpy()
												SCREAMING_SNAKE_CASE				=				out_a[~np.isnan(snake_case__					)]
												SCREAMING_SNAKE_CASE				=				out_a[~np.isnan(snake_case__					)]
												SCREAMING_SNAKE_CASE				=				np.amax(np.abs(out_a - out_a					)					)
												self.assertLessEqual(snake_case__		,							1e-5					)




							def 		__snake_case(  self      :    Optional[int]					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)
												model.eval()

												model.set_attention_slice("auto"					)
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)
												assert output is not None

												model.set_attention_slice("max"					)
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)
												assert output is not None

												model.set_attention_slice(2					)
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)
												assert output is not None




							def 		__snake_case(  self      :    Any					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__					)

												# make sure we can set a list of attention processors
												model.set_attn_processor(snake_case__					)
												model.to(snake_case__					)

												# test that attn processors can be set to itself
												model.set_attn_processor(model.attn_processors					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.0}					).sample
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample

												assert (samplea - samplea).abs().max() < 3e-3
												assert (samplea - samplea).abs().max() < 3e-3

												# sample 2 and sample 3 should be different
												assert (samplea - samplea).abs().max() > 3e-3




							def 		__snake_case(  self      :    List[str]					)    -> List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__					)
												model.set_attn_processor(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case__					)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case__		,							"pytorch_lora_weights.bin"					)					)					)
																	torch.manual_seed(0					)
																	SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
																	new_model.to(snake_case__					)
																	new_model.load_attn_procs(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				new_model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample

												assert (sample - new_sample).abs().max() < 1e-3

												# LoRA and no LoRA should NOT be the same
												assert (sample - old_sample).abs().max() > 1e-4




							def 		__snake_case(  self      :    List[str]					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__					)
												model.set_attn_processor(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case__		,							safe_serialization=snake_case__					)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case__		,							"pytorch_lora_weights.safetensors"					)					)					)
																	torch.manual_seed(0					)
																	SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
																	new_model.to(snake_case__					)
																	new_model.load_attn_procs(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				new_model(**snake_case__		,							cross_attention_kwargs={"scale": 0.5}					).sample

												assert (sample - new_sample).abs().max() < 3e-3

												# LoRA and no LoRA should NOT be the same
												assert (sample - old_sample).abs().max() > 1e-4




							def 		__snake_case(  self      :    List[str]					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__		,							mock_weights=snake_case__					)
												model.set_attn_processor(snake_case__					)
												# Saving as torch, properly reloads with directly filename
												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case__					)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case__		,							"pytorch_lora_weights.bin"					)					)					)
																	torch.manual_seed(0					)
																	SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
																	new_model.to(snake_case__					)
																	new_model.load_attn_procs(snake_case__		,							weight_name="pytorch_lora_weights.bin"					)




							def 		__snake_case(  self      :    Any					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__		,							mock_weights=snake_case__					)
												model.set_attn_processor(snake_case__					)
												# Saving as torch, properly reloads with directly filename
												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case__					)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case__		,							"pytorch_lora_weights.bin"					)					)					)
																	torch.manual_seed(0					)
																	SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
																	new_model.to(snake_case__					)
																	with self.assertRaises(snake_case__					) as e:
																						new_model.load_attn_procs(snake_case__		,							use_safetensors=snake_case__					)
																	self.assertIn("Error no file named pytorch_lora_weights.safetensors"		,							str(e.exception					)					)




							def 		__snake_case(  self      :    Any					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				8

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__					)
												model.set_attn_processor(snake_case__					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__		,							cross_attention_kwargs={"scale": 0.0}					).sample

												model.set_attn_processor(AttnProcessor()					)

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												assert (sample - new_sample).abs().max() < 1e-4
												assert (sample - old_sample).abs().max() < 3e-3




							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()		,							reason="XFormers attention is only available with CUDA and `xformers` installed"		,							)
							def 		__snake_case(  self      :    Optional[Any]					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()

												SCREAMING_SNAKE_CASE				=				4

												torch.manual_seed(0					)
												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)
												SCREAMING_SNAKE_CASE				=				create_lora_layers(snake_case__					)
												model.set_attn_processor(snake_case__					)

												# default
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

																	model.enable_xformers_memory_efficient_attention()
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

																	model.disable_xformers_memory_efficient_attention()
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					).sample

												assert (sample - on_sample).abs().max() < 1e-4
												assert (sample - off_sample).abs().max() < 1e-4





							def 		__snake_case(  self      :    int					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_init_args_and_inputs_for_common()
												SCREAMING_SNAKE_CASE				=				32

												SCREAMING_SNAKE_CASE				=				self.model_class(**snake_case__					)
												model.to(snake_case__					)
												model.eval()

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

												model.enable_forward_chunking()
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE				=				model(**snake_case__					)[0]

												self.assertEqual(output.shape		,							output_a.shape		,							"Shape doesn't match"					)
												assert np.abs(output.cpu() - output_a.cpu()					).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.












"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		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(
			    """--bert_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."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






