
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class   UpperCamelCase_      :







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										torch.manual_seed(0     )
										lowercase         =      TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"     )

										torch.manual_seed(0     )
										lowercase         =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"     )

										torch.manual_seed(0     )
										lowercase         =      UNetaDConditionModel(
										    sample_size=32		,    layers_per_block=1		,    block_out_channels=[32, 64]		,    down_block_types=[
										        "ResnetDownsampleBlock2D",
										        "SimpleCrossAttnDownBlock2D",
										    ]		,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"		,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]		,    in_channels=3		,    out_channels=6		,    cross_attention_dim=32		,    encoder_hid_dim=32		,    attention_head_dim=8		,    addition_embed_type="text"		,    addition_embed_type_num_heads=2		,    cross_attention_norm="group_norm"		,    resnet_time_scale_shift="scale_shift"		,    act_fn="gelu"		,    )
										unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

										torch.manual_seed(0     )
										lowercase         =      DDPMScheduler(
										    num_train_timesteps=1000		,    beta_schedule="squaredcos_cap_v2"		,    beta_start=0.00_01		,    beta_end=0.02		,    thresholding=__lowercase		,    dynamic_thresholding_ratio=0.95		,    sample_max_value=1.0		,    prediction_type="epsilon"		,    variance_type="learned_range"		,    )

										torch.manual_seed(0     )
										lowercase         =      IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Union[str, Any]:
										torch.manual_seed(0     )
										lowercase         =      TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"     )

										torch.manual_seed(0     )
										lowercase         =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"     )

										torch.manual_seed(0     )
										lowercase         =      UNetaDConditionModel(
										    sample_size=32		,    layers_per_block=[1, 2]		,    block_out_channels=[32, 64]		,    down_block_types=[
										        "ResnetDownsampleBlock2D",
										        "SimpleCrossAttnDownBlock2D",
										    ]		,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"		,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]		,    in_channels=6		,    out_channels=6		,    cross_attention_dim=32		,    encoder_hid_dim=32		,    attention_head_dim=8		,    addition_embed_type="text"		,    addition_embed_type_num_heads=2		,    cross_attention_norm="group_norm"		,    resnet_time_scale_shift="scale_shift"		,    act_fn="gelu"		,    class_embed_type="timestep"		,    mid_block_scale_factor=1.4_14		,    time_embedding_act_fn="gelu"		,    time_embedding_dim=32		,    )
										unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

										torch.manual_seed(0     )
										lowercase         =      DDPMScheduler(
										    num_train_timesteps=1000		,    beta_schedule="squaredcos_cap_v2"		,    beta_start=0.00_01		,    beta_end=0.02		,    thresholding=__lowercase		,    dynamic_thresholding_ratio=0.95		,    sample_max_value=1.0		,    prediction_type="epsilon"		,    variance_type="learned_range"		,    )

										torch.manual_seed(0     )
										lowercase         =      DDPMScheduler(
										    num_train_timesteps=1000		,    beta_schedule="squaredcos_cap_v2"		,    beta_start=0.00_01		,    beta_end=0.02		,    )

										torch.manual_seed(0     )
										lowercase         =      IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "image_noising_scheduler": image_noising_scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Union[str, Any]:
										lowercase         =      self.get_dummy_components()
										lowercase         =      self.pipeline_class(**__lowercase     )
										pipe.to(__lowercase     )
										pipe.set_progress_bar_config(disable=__lowercase     )

										lowercase         =      self.get_dummy_inputs(__lowercase     )

										lowercase         =      inputs["prompt"]
										lowercase         =      inputs["generator"]
										lowercase         =      inputs["num_inference_steps"]
										lowercase         =      inputs["output_type"]

										if "image" in inputs:
														lowercase         =      inputs["image"]
										else:
														lowercase         =      None

										if "mask_image" in inputs:
														lowercase         =      inputs["mask_image"]
										else:
														lowercase         =      None

										if "original_image" in inputs:
														lowercase         =      inputs["original_image"]
										else:
														lowercase         =      None

										lowercase     ,	lowercase         =      pipe.encode_prompt(__lowercase     )

										# inputs with prompt converted to embeddings
										lowercase         =      {
										    "prompt_embeds": prompt_embeds,
										    "negative_prompt_embeds": negative_prompt_embeds,
										    "generator": generator,
										    "num_inference_steps": num_inference_steps,
										    "output_type": output_type,
										}

										if image is not None:
														lowercase         =      image

										if mask_image is not None:
														lowercase         =      mask_image

										if original_image is not None:
														lowercase         =      original_image

										# set all optional components to None
										for optional_component in pipe._optional_components:
														setattr(__lowercase		,    __lowercase		,    __lowercase     )

										lowercase         =      pipe(**__lowercase     )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(__lowercase     )
														lowercase         =      self.pipeline_class.from_pretrained(__lowercase     )
														pipe_loaded.to(__lowercase     )
														pipe_loaded.set_progress_bar_config(disable=__lowercase     )

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

										for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(__lowercase		,    __lowercase     ) is None		,    F'''`{optional_component}` did not stay set to None after loading.'''		,    )

										lowercase         =      self.get_dummy_inputs(__lowercase     )

										lowercase         =      inputs["generator"]
										lowercase         =      inputs["num_inference_steps"]
										lowercase         =      inputs["output_type"]

										# inputs with prompt converted to embeddings
										lowercase         =      {
										    "prompt_embeds": prompt_embeds,
										    "negative_prompt_embeds": negative_prompt_embeds,
										    "generator": generator,
										    "num_inference_steps": num_inference_steps,
										    "output_type": output_type,
										}

										if image is not None:
														lowercase         =      image

										if mask_image is not None:
														lowercase         =      mask_image

										if original_image is not None:
														lowercase         =      original_image

										lowercase         =      pipe_loaded(**__lowercase     )[0]

										lowercase         =      np.abs(to_np(__lowercase     ) - to_np(__lowercase     )     ).max()
										self.assertLess(__lowercase		,    1E-4     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Dict:
										lowercase         =      self.get_dummy_components()
										lowercase         =      self.pipeline_class(**__lowercase     )
										pipe.to(__lowercase     )
										pipe.set_progress_bar_config(disable=__lowercase     )

										lowercase         =      self.get_dummy_inputs(__lowercase     )
										lowercase         =      pipe(**__lowercase     )[0]

										with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(__lowercase     )
														lowercase         =      self.pipeline_class.from_pretrained(__lowercase     )
														pipe_loaded.to(__lowercase     )
														pipe_loaded.set_progress_bar_config(disable=__lowercase     )

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

										lowercase         =      self.get_dummy_inputs(__lowercase     )
										lowercase         =      pipe_loaded(**__lowercase     )[0]

										lowercase         =      np.abs(to_np(__lowercase     ) - to_np(__lowercase     )     ).max()
										self.assertLess(__lowercase		,    1E-4     )






import argparse

import torch

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


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


if __name__ == "__main__":
		lowercase__							:	Dict											=		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."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
