






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__a      :       Union[str, Any]				  =   logging.get_logger(__name__)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			str			,				__lowercase   :			str    )						->      str:





				"""simple docstring"""

				__A         =	os.path.abspath(__lowercase    )
				logger.info(f"Converting TensorFlow checkpoint from {tf_path}"    )
				# Load weights from TF model
				__A         =	tf.train.list_variables(__lowercase    )
				__A         =	[]
				__A         =	[]
				__A         =	[]
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								__A         =	full_name.split("""/"""    )
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(f"Skipping non-model layer {full_name}"    )
												continue
								if "optimizer" in full_name:
												logger.info(f"Skipping optimization layer {full_name}"    )
												continue
								if name[0] == "model":
												# ignore initial 'model'
												__A         =	name[1:]
								# figure out how many levels deep the name is
								__A         =	0
								for _name in name:
												if _name.startswith("""layer_with_weights"""    ):
																depth += 1
												else:
																break
								layer_depth.append(__lowercase    )
								# read data
								__A         =	tf.train.load_variable(__lowercase			,				__lowercase    )
								names.append("""/""".join(__lowercase    )    )
								arrays.append(__lowercase    )
				logger.info(f"Read a total of {len(__lowercase    ):,} layers"    )

				# Sanity check
				if len(set(__lowercase    )    ) != 1:
								raise ValueError(f"Found layer names with different depths (layer depth {list(set(__lowercase    )    )})"    )
				__A         =	list(set(__lowercase    )    )[0]
				if layer_depth != 1:
								raise ValueError(
								    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
								    """ heads."""    )

				# convert layers
				logger.info("""Converting weights..."""    )
				for full_name, array in zip(__lowercase			,				__lowercase    ):
								__A         =	full_name.split("""/"""    )
								__A         =	model
								__A         =	[]
								for i, m_name in enumerate(__lowercase    ):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith("""layer_with_weights"""    ):
																__A         =	int(m_name.split("""-"""    )[-1]    )
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(["""embeddings""", """LayerNorm"""]    )
																				__A         =	getattr(__lowercase			,				"""embeddings"""    )
																				__A         =	getattr(__lowercase			,				"""LayerNorm"""    )
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(["""encoder""", """layer""", str(layer_num - 4    )]    )
																				__A         =	getattr(__lowercase			,				"""encoder"""    )
																				__A         =	getattr(__lowercase			,				"""layer"""    )
																				__A         =	pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(["""pooler""", """dense"""]    )
																				__A         =	getattr(__lowercase			,				"""pooler"""    )
																				__A         =	getattr(__lowercase			,				"""dense"""    )
												elif m_name == "embeddings":
																trace.append("""embeddings"""    )
																__A         =	getattr(__lowercase			,				"""embeddings"""    )
																if layer_num == 0:
																				trace.append("""word_embeddings"""    )
																				__A         =	getattr(__lowercase			,				"""word_embeddings"""    )
																elif layer_num == 1:
																				trace.append("""position_embeddings"""    )
																				__A         =	getattr(__lowercase			,				"""position_embeddings"""    )
																elif layer_num == 2:
																				trace.append("""token_type_embeddings"""    )
																				__A         =	getattr(__lowercase			,				"""token_type_embeddings"""    )
																else:
																				raise ValueError(f"Unknown embedding layer with name {full_name}"    )
																trace.append("""weight"""    )
																__A         =	getattr(__lowercase			,				"""weight"""    )
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(["""attention""", """self"""]    )
																__A         =	getattr(__lowercase			,				"""attention"""    )
																__A         =	getattr(__lowercase			,				"""self"""    )
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(["""attention""", """output""", """LayerNorm"""]    )
																__A         =	getattr(__lowercase			,				"""attention"""    )
																__A         =	getattr(__lowercase			,				"""output"""    )
																__A         =	getattr(__lowercase			,				"""LayerNorm"""    )
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(["""attention""", """output""", """dense"""]    )
																__A         =	getattr(__lowercase			,				"""attention"""    )
																__A         =	getattr(__lowercase			,				"""output"""    )
																__A         =	getattr(__lowercase			,				"""dense"""    )
												elif m_name == "_output_dense":
																# output dense
																trace.extend(["""output""", """dense"""]    )
																__A         =	getattr(__lowercase			,				"""output"""    )
																__A         =	getattr(__lowercase			,				"""dense"""    )
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(["""output""", """LayerNorm"""]    )
																__A         =	getattr(__lowercase			,				"""output"""    )
																__A         =	getattr(__lowercase			,				"""LayerNorm"""    )
												elif m_name == "_key_dense":
																# attention key
																trace.append("""key"""    )
																__A         =	getattr(__lowercase			,				"""key"""    )
												elif m_name == "_query_dense":
																# attention query
																trace.append("""query"""    )
																__A         =	getattr(__lowercase			,				"""query"""    )
												elif m_name == "_value_dense":
																# attention value
																trace.append("""value"""    )
																__A         =	getattr(__lowercase			,				"""value"""    )
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(["""intermediate""", """dense"""]    )
																__A         =	getattr(__lowercase			,				"""intermediate"""    )
																__A         =	getattr(__lowercase			,				"""dense"""    )
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append("""output"""    )
																__A         =	getattr(__lowercase			,				"""output"""    )
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append("""bias"""    )
																__A         =	getattr(__lowercase			,				"""bias"""    )
												elif m_name in ["kernel", "gamma"]:
																trace.append("""weight"""    )
																__A         =	getattr(__lowercase			,				"""weight"""    )
												else:
																logger.warning(f"Ignored {m_name}"    )
        # for certain layers reshape is necessary
								__A         =	""".""".join(__lowercase    )
								if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""			,				__lowercase    ) or re.match(
								    R"""(\S+)\.attention\.output\.dense\.weight"""			,				__lowercase    ):
												__A         =	array.reshape(pointer.data.shape    )
								if "kernel" in full_name:
												__A         =	array.transpose()
								if pointer.shape == array.shape:
												__A         =	torch.from_numpy(__lowercase    )
								else:
												raise ValueError(
												    f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"
												    f" {array.shape}"    )
								logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}"    )
				return model



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			List[Any]			,				__lowercase   :			Optional[Any]    )						->      List[str]:





				"""simple docstring"""

				logger.info(f"Loading model based on config from {config_path}..."    )
				__A         =	BertConfig.from_json_file(__lowercase    )
				__A         =	BertModel(__lowercase    )

				# Load weights from checkpoint
				logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}..."    )
				load_tfa_weights_in_bert(__lowercase			,				__lowercase			,				__lowercase    )

				# Save pytorch-model
				logger.info(f"Saving PyTorch model to {pytorch_dump_path}..."    )
				torch.save(model.state_dict()			,				__lowercase    )


if __name__ == "__main__":
		__a      :       Union[str, Any]				  =   argparse.ArgumentParser()
		parser.add_argument(
		    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
		)
		__a      :       str				  =   parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      ImageTextPipelineOutput,
      UniDiffuserPipeline,
  )
else:
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
  from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




