



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 				lowerCAmelCase_		(  a__       ,    a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Optional[int]        =  AltDiffusionPipeline
       UpperCAmelCase__				:					Optional[Any]        =  TEXT_TO_IMAGE_PARAMS
       UpperCAmelCase__				:					Optional[Any]        =  TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCAmelCase__				:					List[Any]        =  TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCAmelCase__				:					Optional[int]        =  TEXT_TO_IMAGE_IMAGE_PARAMS
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          torch.manual_seed(0       )
          UpperCamelCase    :  Union[str, Any]							=	UNetaDConditionModel(
              block_out_channels=(32, 64),			layers_per_block=2,			sample_size=32,			in_channels=4,			out_channels=4,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),			cross_attention_dim=32,			)
          UpperCamelCase    :  Any							=	DDIMScheduler(
              beta_start=0.0_00_85,			beta_end=0.0_12,			beta_schedule='scaled_linear',			clip_sample=SCREAMING_SNAKE_CASE_,			set_alpha_to_one=SCREAMING_SNAKE_CASE_,			)
          torch.manual_seed(0       )
          UpperCamelCase    :  Tuple							=	AutoencoderKL(
              block_out_channels=[32, 64],			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    :  List[str]							=	CLIPTextConfig(
              bos_token_id=0,			eos_token_id=2,			hidden_size=32,			projection_dim=32,			intermediate_size=37,			layer_norm_eps=1e-05,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=5002,			)
          UpperCamelCase    :  str							=	CLIPTextModel(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
          UpperCamelCase    :  Union[str, Any]							=	77

          UpperCamelCase    :  Dict							=	{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		int:
          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  Union[str, Any]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  List[Any]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	{
              '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       )     ->		Optional[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Any							=	'cpu'  # ensure determinism for the device-dependent torch.Generator

          UpperCamelCase    :  List[str]							=	self.get_dummy_components()
          torch.manual_seed(0       )
          UpperCamelCase    :  Optional[Any]							=	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,			)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCamelCase    :  Tuple							=	RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	text_encoder

          UpperCamelCase    :  Tuple							=	AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	alt_pipe.to(SCREAMING_SNAKE_CASE_       )
          alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	'A photo of an astronaut'
          UpperCamelCase    :  int							=	alt_pipe(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	output.images
          UpperCamelCase    :  Dict							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase    :  int							=	np.array(
              [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]       )

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






       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Dict							=	'cpu'  # ensure determinism for the device-dependent torch.Generator

          UpperCamelCase    :  Dict							=	self.get_dummy_components()
          UpperCamelCase    :  Tuple							=	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_       )
          torch.manual_seed(0       )
          UpperCamelCase    :  Optional[int]							=	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,			)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCamelCase    :  Any							=	RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	text_encoder
          UpperCamelCase    :  str							=	AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	alt_pipe.to(SCREAMING_SNAKE_CASE_       )
          alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	alt_pipe(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Tuple							=	output.images
          UpperCamelCase    :  List[str]							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase    :  Optional[int]							=	np.array(
              [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]       )

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






@slow
@require_torch_gpu
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		int:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def 					snake_case_       (			self       )     ->		Optional[int]:
          # make sure here that pndm scheduler skips prk
          UpperCamelCase    :  List[str]							=	AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion',			safety_checker=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	alt_pipe.to(SCREAMING_SNAKE_CASE_       )
          alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	'A painting of a squirrel eating a burger'
          UpperCamelCase    :  Any							=	torch.manual_seed(0       )
          UpperCamelCase    :  Optional[int]							=	alt_pipe([prompt],			generator=SCREAMING_SNAKE_CASE_,			guidance_scale=6.0,			num_inference_steps=20,			output_type='np'       )

          UpperCamelCase    :  str							=	output.images

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

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase    :  Union[str, Any]							=	np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]       )

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






       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	DDIMScheduler.from_pretrained('BAAI/AltDiffusion',			subfolder='scheduler'       )

          UpperCamelCase    :  Tuple							=	AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion',			scheduler=SCREAMING_SNAKE_CASE_,			safety_checker=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	alt_pipe.to(SCREAMING_SNAKE_CASE_       )
          alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	'A painting of a squirrel eating a burger'
          UpperCamelCase    :  int							=	torch.manual_seed(0       )

          UpperCamelCase    :  Optional[Any]							=	alt_pipe([prompt],			generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=2,			output_type='numpy'       )
          UpperCamelCase    :  Optional[int]							=	output.images

          UpperCamelCase    :  List[str]							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase    :  Any							=	np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]       )

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








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     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(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT 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.mobilebert_config_file, args.pytorch_dump_path)



