






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)












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



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





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """token_embedder""": """embeddings""",
        """encoder_norm""": """layernorm""",
        """kernel""": """weight""",
        """.out""": """.output""",
        """scale""": """weight""",
        """embedders_0.pos_embedding""": """row_embedder.weight""",
        """embedders_1.pos_embedding""": """column_embedder.weight""",
    }

    __A         =	{
        """query""": """attention.query""",
        """key""": """attention.key""",
        """value""": """attention.value""",
        """output.dense""": """output""",
        """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
        """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
        """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
        """mlp.""": """mlp.DenseReluDense.""",
        """pre_mlp_layer_norm""": """mlp.layer_norm""",
        """self_attention.o""": """self_attention.attention.o""",
        """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
        """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
        """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            __A         =	""".""".join(key[1:]    )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )

            __A         =	flax_dict[key]

    __A         =	{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            __A         =	torch.from_numpy(converted_dict[key].T    )
        else:
            __A         =	torch.from_numpy(converted_dict[key]    )

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

    # mkdir if needed
    os.makedirs(__lowercase			,				exist_ok=__lowercase    )

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

    print("""Model saved in {}""".format(__lowercase    )    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   argparse.ArgumentParser()
  parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--use_large", action="store_true", help="Use large model.")
  parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
  __a      :       Dict				  =   parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )




