
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()
lowercase__	:Dict										=					logging.get_logger(__name__)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		os.path.abspath(lowerCAmelCase__	)
					logger.info(f'Converting TensorFlow checkpoint from {tf_path}'	)
					# Load weights from TF model
					lowercase					=		tf.train.list_variables(lowerCAmelCase__	)
					lowercase					=		[]
					lowercase					=		[]
					lowercase					=		[]
					for full_name, shape in init_vars:
										# logger.info(f"Loading TF weight {name} with shape {shape}")
										lowercase					=		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'
															lowercase					=		name[1:]
										# figure out how many levels deep the name is
										lowercase					=		0
										for _name in name:
															if _name.startswith('''layer_with_weights'''	):
																				depth += 1
															else:
																				break
										layer_depth.append(lowerCAmelCase__	)
										# read data
										lowercase					=		tf.train.load_variable(lowerCAmelCase__				,    lowerCAmelCase__	)
										names.append('''/'''.join(lowerCAmelCase__	)	)
										arrays.append(lowerCAmelCase__	)
					logger.info(f'Read a total of {len(lowerCAmelCase__	):,} layers'	)

					# Sanity check
					if len(set(lowerCAmelCase__	)	) != 1:
										raise ValueError(f'Found layer names with different depths (layer depth {list(set(lowerCAmelCase__	)	)})'	)
					lowercase					=		list(set(lowerCAmelCase__	)	)[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(lowerCAmelCase__				,    lowerCAmelCase__	):
										lowercase					=		full_name.split('''/'''	)
										lowercase					=		model
										lowercase					=		[]
										for i, m_name in enumerate(lowerCAmelCase__	):
															if m_name == ".ATTRIBUTES":
																				# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																				break
															if m_name.startswith('''layer_with_weights'''	):
																				lowercase					=		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''']	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''embeddings'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''LayerNorm'''	)
																				elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																									# encoder layers
																									trace.extend(['''encoder''', '''layer''', str(layer_num - 4	)]	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''encoder'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''layer'''	)
																									lowercase					=		pointer[layer_num - 4]
																				elif layer_num == config.num_hidden_layers + 4:
																									# pooler layer
																									trace.extend(['''pooler''', '''dense''']	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''pooler'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''dense'''	)
															elif m_name == "embeddings":
																				trace.append('''embeddings'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''embeddings'''	)
																				if layer_num == 0:
																									trace.append('''word_embeddings'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''word_embeddings'''	)
																				elif layer_num == 1:
																									trace.append('''position_embeddings'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''position_embeddings'''	)
																				elif layer_num == 2:
																									trace.append('''token_type_embeddings'''	)
																									lowercase					=		getattr(lowerCAmelCase__				,    '''token_type_embeddings'''	)
																				else:
																									raise ValueError(f'Unknown embedding layer with name {full_name}'	)
																				trace.append('''weight'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''weight'''	)
															elif m_name == "_attention_layer":
																				# self-attention layer
																				trace.extend(['''attention''', '''self''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''attention'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''self'''	)
															elif m_name == "_attention_layer_norm":
																				# output attention norm
																				trace.extend(['''attention''', '''output''', '''LayerNorm''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''attention'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''output'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''LayerNorm'''	)
															elif m_name == "_attention_output_dense":
																				# output attention dense
																				trace.extend(['''attention''', '''output''', '''dense''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''attention'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''output'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''dense'''	)
															elif m_name == "_output_dense":
																				# output dense
																				trace.extend(['''output''', '''dense''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''output'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''dense'''	)
															elif m_name == "_output_layer_norm":
																				# output dense
																				trace.extend(['''output''', '''LayerNorm''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''output'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''LayerNorm'''	)
															elif m_name == "_key_dense":
																				# attention key
																				trace.append('''key'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''key'''	)
															elif m_name == "_query_dense":
																				# attention query
																				trace.append('''query'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''query'''	)
															elif m_name == "_value_dense":
																				# attention value
																				trace.append('''value'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''value'''	)
															elif m_name == "_intermediate_dense":
																				# attention intermediate dense
																				trace.extend(['''intermediate''', '''dense''']	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''intermediate'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''dense'''	)
															elif m_name == "_output_layer_norm":
																				# output layer norm
																				trace.append('''output'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''output'''	)
															# weights & biases
															elif m_name in ["bias", "beta"]:
																				trace.append('''bias'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''bias'''	)
															elif m_name in ["kernel", "gamma"]:
																				trace.append('''weight'''	)
																				lowercase					=		getattr(lowerCAmelCase__				,    '''weight'''	)
															else:
																				logger.warning(f'Ignored {m_name}'	)
        # for certain layers reshape is necessary
										lowercase					=		'''.'''.join(lowerCAmelCase__	)
										if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''				,    lowerCAmelCase__	) or re.match(
										    R'''(\S+)\.attention\.output\.dense\.weight'''				,    lowerCAmelCase__	):
															lowercase					=		array.reshape(pointer.data.shape	)
										if "kernel" in full_name:
															lowercase					=		array.transpose()
										if pointer.shape == array.shape:
															lowercase					=		torch.from_numpy(lowerCAmelCase__	)
										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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Instantiate model
					logger.info(f'Loading model based on config from {config_path}...'	)
					lowercase					=		BertConfig.from_json_file(lowerCAmelCase__	)
					lowercase					=		BertModel(lowerCAmelCase__	)

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

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


if __name__ == "__main__":
							lowercase__	:int										=					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).",
							)
							lowercase__	:List[Any]										=					parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:List[str]										=					[
							    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "InstructBlipQFormerModel",
							    "InstructBlipPreTrainedModel",
							    "InstructBlipForConditionalGeneration",
							    "InstructBlipVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

							lowercase__	:List[str]										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




