





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase   				=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		SCREAMING_SNAKE_CASE__			=      [trans(img.convert("""RGB"""    )    ) for img in image]
		SCREAMING_SNAKE_CASE__			=      torch.stack(UpperCamelCase__    )
		return image




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :Dict							,				__A    :int							,				__A    :List[str]		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__			=      DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=__A							,				scheduler=__A		)


							def      _snake_case  (							self    :str							,				__A    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									if strength < 0 or strength > 1:
											raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :List[Any]							,				__A    :Any							,				__A    :Optional[int]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      min(int(num_inference_steps * strength		)							,				__A		)

									SCREAMING_SNAKE_CASE__			=      max(num_inference_steps - init_timestep							,				0		)
									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start


							def      _snake_case  (							self    :List[str]							,				__A    :Optional[int]							,				__A    :Any							,				__A    :List[Any]							,				__A    :Any							,				__A    :Dict							,				__A    :Union[str, Any]=None		)   ->   Any:



									"""simple docstring"""



									if not isinstance(__A							,				(torch.Tensor, PIL.Image.Image, list)		):
											raise ValueError(
											    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__A		)}'''		)

									SCREAMING_SNAKE_CASE__			=      image.to(device=__A							,				dtype=__A		)

									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      init_latents.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=__A							,				dtype=__A		)

									# get latents
									print("""add noise to latents at timestep"""							,				__A		)
									SCREAMING_SNAKE_CASE__			=      self.scheduler.add_noise(__A							,				__A							,				__A		)
									SCREAMING_SNAKE_CASE__			=      init_latents

									return latents


							@torch.no_grad()
							def __call__(							self    :Any							,				__A    :Union[torch.FloatTensor, PIL.Image.Image] = None							,				__A    :float = 0.8							,				__A    :int = 1							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :float = 0.0							,				__A    :int = 50							,				__A    :Optional[bool] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									self.check_inputs(__A		)

									# 2. Preprocess image
									SCREAMING_SNAKE_CASE__			=      preprocess(__A		)

									# 3. set timesteps
									self.scheduler.set_timesteps(__A							,				device=self.device		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.get_timesteps(__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      timesteps[:1].repeat(__A		)

									# 4. Prepare latent variables
									SCREAMING_SNAKE_CASE__			=      self.prepare_latents(__A							,				__A							,				__A							,				self.unet.dtype							,				self.device							,				__A		)
									SCREAMING_SNAKE_CASE__			=      latents

									# 5. Denoising loop
									for t in self.progress_bar(__A		):
											# 1. predict noise model_output
											SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											SCREAMING_SNAKE_CASE__			=      self.scheduler.step(
											    __A							,				__A							,				__A							,				eta=__A							,				use_clipped_model_output=__A							,				generator=__A							,				).prev_sample

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image, latent_timestep.item())

									return ImagePipelineOutput(images=__A		)






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		def get_masked_lm_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_attention_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      array.reshape(UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		print(f'''Loading model based on config from {config_path}...'''    )
		SCREAMING_SNAKE_CASE__			=      BertConfig.from_json_file(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM(UpperCamelCase__    )

		# Layers
		for layer_index in range(0					,   config.num_hidden_layers    ):
				SCREAMING_SNAKE_CASE__			=      model.bert.encoder.layer[layer_index]

				# Self-attention
				SCREAMING_SNAKE_CASE__			=      layer.attention.self

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/kernel"""					,   self_attn.query.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/bias"""					,   self_attn.query.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/kernel"""					,   self_attn.key.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/bias"""					,   self_attn.key.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/kernel"""					,   self_attn.value.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/bias"""					,   self_attn.value.bias.data.shape    )

				# Self-attention Output
				SCREAMING_SNAKE_CASE__			=      layer.attention.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/kernel"""					,   self_output.dense.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/bias"""					,   self_output.dense.bias.data.shape    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/beta"""    )

				# Intermediate
				SCREAMING_SNAKE_CASE__			=      layer.intermediate

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/bias"""    )

				# Output
				SCREAMING_SNAKE_CASE__			=      layer.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/bias"""    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/beta"""    )

		# Embeddings
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_position_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_type_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/beta"""    )

		# LM Head
		SCREAMING_SNAKE_CASE__			=      model.cls.predictions.transform

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/bias"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/beta"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""embedding_table"""    )

		# Pooling
		SCREAMING_SNAKE_CASE__			=      BertPooler(config=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/bias"""    )

		# Export final model
		model.save_pretrained(UpperCamelCase__    )

		# Integration test - should load without any errors ;)
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM.from_pretrained(UpperCamelCase__    )
		print(new_model.eval()    )

		print("""Model conversion was done sucessfully!"""    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
	)
	parser.add_argument(
	    '--bert_config_file',
	    type=str,
	    required=True,
	    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path',
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)