






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 		_snake_case	(	A__ ,     A__ ,     unittest.TestCase       ):
			_lowercase		:     List[Any]        =				IFPipeline
			_lowercase		:     List[Any]        =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
			_lowercase		:     Dict        =				TEXT_TO_IMAGE_BATCH_PARAMS
			_lowercase		:     List[Any]        =				PipelineTesterMixin.required_optional_params - {'''latents'''}






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						return self._get_dummy_components()






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a=0)   ->							List[Any]:
						if str(a).startswith('mps'):
									SCREAMING_SNAKE_CASE							=			torch.manual_seed(a)
						else:
									SCREAMING_SNAKE_CASE							=			torch.Generator(device=a).manual_seed(a)

						SCREAMING_SNAKE_CASE							=			{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != 'cuda'		,       reason='float16 requires CUDA')
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1E-1)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						self._test_save_load_local()






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2		,       )







			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()		,       reason='XFormers attention is only available with CUDA and `xformers` installed'		,       )
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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






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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						# if

						SCREAMING_SNAKE_CASE							=			IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'		,       variant='fp16'		,       torch_dtype=torch.floataa)

						SCREAMING_SNAKE_CASE							=			IFSuperResolutionPipeline.from_pretrained(
						    'DeepFloyd/IF-II-L-v1.0'		,       variant='fp16'		,       torch_dtype=torch.floataa		,       text_encoder=a		,       tokenizer=a)

						# pre compute text embeddings and remove T5 to save memory

						pipe_a.text_encoder.to('cuda')

						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			pipe_a.encode_prompt('anime turtle'		,       device='cuda')

						del pipe_a.tokenizer
						del pipe_a.text_encoder
						gc.collect()

						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

						self._test_if(a		,       a		,       a		,       a)

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# img2img

						SCREAMING_SNAKE_CASE							=			IFImgaImgPipeline(**pipe_a.components)
						SCREAMING_SNAKE_CASE							=			IFImgaImgSuperResolutionPipeline(**pipe_a.components)

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

						self._test_if_imgaimg(a		,       a		,       a		,       a)

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# inpainting

						SCREAMING_SNAKE_CASE							=			IFInpaintingPipeline(**pipe_a.components)
						SCREAMING_SNAKE_CASE							=			IFInpaintingSuperResolutionPipeline(**pipe_a.components)

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

						self._test_if_inpainting(a		,       a		,       a		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a)   ->							str:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)
						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       num_inference_steps=2		,       generator=a		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (64, 64, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 13 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy')
						assert_mean_pixel_difference(a		,       a)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)

						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(0)).to(a)

						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       image=a		,       generator=a		,       num_inference_steps=2		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy')
						assert_mean_pixel_difference(a		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a)   ->							Optional[int]:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(0)).to(a)

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)

						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       image=a		,       num_inference_steps=2		,       generator=a		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (64, 64, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy')
						assert_mean_pixel_difference(a		,       a)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)

						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 256, 256)		,       rng=random.Random(0)).to(a)
						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(0)).to(a)

						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       image=a		,       original_image=a		,       generator=a		,       num_inference_steps=2		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy')
						assert_mean_pixel_difference(a		,       a)







			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a)   ->							Any:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(0)).to(a)
						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(1)).to(a)

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)
						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       image=a		,       mask_image=a		,       num_inference_steps=2		,       generator=a		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (64, 64, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 10 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy')
						assert_mean_pixel_difference(a		,       a)

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE							=			torch.Generator(device='cpu').manual_seed(0)

						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 64, 64)		,       rng=random.Random(0)).to(a)
						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 256, 256)		,       rng=random.Random(0)).to(a)
						SCREAMING_SNAKE_CASE							=			floats_tensor((1, 3, 256, 256)		,       rng=random.Random(1)).to(a)

						SCREAMING_SNAKE_CASE							=			pipe_a(
						    prompt_embeds=a		,       negative_prompt_embeds=a		,       image=a		,       mask_image=a		,       original_image=a		,       generator=a		,       num_inference_steps=2		,       output_type='np'		,       )

						SCREAMING_SNAKE_CASE							=			output.images[0]

						assert image.shape == (256, 256, 3)

						SCREAMING_SNAKE_CASE							=			torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 10**9

						SCREAMING_SNAKE_CASE							=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy')
						assert_mean_pixel_difference(a		,       a)






def 					lowerCamelCase__					():
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()









import argparse

import torch

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


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  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(
         '--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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)