





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : Dict         =   AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE_       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   AlbertForPreTraining(SCREAMING_SNAKE_CASE_       )

						# Load weights from tf checkpoint
						load_tf_weights_in_albert(SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_       )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
						UpperCamelCase_									=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(
						    """--albert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained ALBERT 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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)










"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   AltDiffusionPipeline
				UpperCamelCase								=   TEXT_TO_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS






				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),			layers_per_block=2,			sample_size=3_2,			in_channels=4,			out_channels=4,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),			cross_attention_dim=3_2,			)
										_UpperCamelCase      : Union[str, Any]         =   DDIMScheduler(
										    beta_start=0.00_085,			beta_end=0.012,			beta_schedule='''scaled_linear''',			clip_sample=lowerCAmelCase__,			set_alpha_to_one=lowerCAmelCase__,			)
										torch.manual_seed(0							)
										_UpperCamelCase      : List[str]         =   AutoencoderKL(
										    block_out_channels=[3_2, 6_4],			in_channels=3,			out_channels=3,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],			latent_channels=4,			)

										# TODO: address the non-deterministic text encoder (fails for save-load tests)
										# torch.manual_seed(0)
										# text_encoder_config = RobertaSeriesConfig(
										#     hidden_size=32,
										#     project_dim=32,
										#     intermediate_size=37,
										#     layer_norm_eps=1e-05,
										#     num_attention_heads=4,
										#     num_hidden_layers=5,
										#     vocab_size=5002,
										# )
										# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

										torch.manual_seed(0							)
										_UpperCamelCase      : str         =   CLIPTextConfig(
										    bos_token_id=0,			eos_token_id=2,			hidden_size=3_2,			projection_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=5_0_0_2,			)
										_UpperCamelCase      : List[Any]         =   CLIPTextModel(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
										_UpperCamelCase      : str         =   7_7

										_UpperCamelCase      : int         =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   int=0							)     ->				Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__							).startswith('''mps'''							):
																_UpperCamelCase      : Any         =   torch.manual_seed(lowerCAmelCase__							)
										else:
																_UpperCamelCase      : Dict         =   torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs






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




										'''simple docstring'''



										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)






				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : int         =   self.get_dummy_components()
										torch.manual_seed(0							)
										_UpperCamelCase      : Any         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   text_encoder

										_UpperCamelCase      : List[Any]         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   '''A photo of an astronaut'''
										_UpperCamelCase      : Any         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   output.images
										_UpperCamelCase      : Union[str, Any]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def     snake_case     (			self		:   int							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : Optional[Any]         =   self.get_dummy_components()
										_UpperCamelCase      : str         =   PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										torch.manual_seed(0							)
										_UpperCamelCase      : int         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   text_encoder
										_UpperCamelCase      : str         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   output.images
										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




@slow
@require_torch_gpu
class 			_a						(				unittest.TestCase    ):






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




										'''simple docstring'''



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






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : str         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : int         =   torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			guidance_scale=6.0,			num_inference_steps=2_0,			output_type='''np'''							)

										_UpperCamelCase      : Optional[int]         =   output.images

										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : List[str]         =   np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def     snake_case     (			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''',			subfolder='''scheduler'''							)

										_UpperCamelCase      : Dict         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			scheduler=lowerCAmelCase__,			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : Optional[Any]         =   torch.manual_seed(0							)

										_UpperCamelCase      : Union[str, Any]         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			num_inference_steps=2,			output_type='''numpy'''							)
										_UpperCamelCase      : Tuple         =   output.images

										_UpperCamelCase      : Dict         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : str         =   np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2



