

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase__  :      str			=    [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    for pegasus_name, hf_name in PATTERNS:
        SCREAMING_SNAKE_CASE_									=						k.replace(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    return k


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						DEFAULTS.copy()
    cfg_kwargs.update(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						PegasusConfig(**_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						PegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						torch_model.model.state_dict()
    SCREAMING_SNAKE_CASE_									=						{}
    for k, v in tf_weights.items():
        SCREAMING_SNAKE_CASE_									=						rename_state_dict_key(_SCREAMING_SNAKE_CASE       )
        if new_k not in sd:
            raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

        if "dense" in k or "proj" in new_k:
            SCREAMING_SNAKE_CASE_									=						v.T
        SCREAMING_SNAKE_CASE_									=						torch.tensor(_SCREAMING_SNAKE_CASE ,   dtype=sd[new_k].dtype       )
        assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
    # make sure embedding.padding_idx is respected
    SCREAMING_SNAKE_CASE_									=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
    SCREAMING_SNAKE_CASE_									=						mapping['shared.weight']
    SCREAMING_SNAKE_CASE_									=						mapping['shared.weight']
    SCREAMING_SNAKE_CASE_									=						{k: torch.zeros_like(_SCREAMING_SNAKE_CASE       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
    mapping.update(**_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch_model.model.load_state_dict(_SCREAMING_SNAKE_CASE ,   strict=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						[
        k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
    ]
    assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
    assert extra == [], f"""no matches found for the following tf keys {extra}"""
    return torch_model


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple="./ckpt/aeslc/model.ckpt-32000"       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tf.train.list_variables(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						{}
    SCREAMING_SNAKE_CASE_									=						['Adafactor', 'global_step']
    for name, shape in tqdm(_SCREAMING_SNAKE_CASE ,   desc='converting tf checkpoint to dict'       ):
        SCREAMING_SNAKE_CASE_									=						any(pat in name for pat in ignore_name       )
        if skip_key:
            continue
        SCREAMING_SNAKE_CASE_									=						tf.train.load_variable(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						array
    return tf_weights


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						Path(_SCREAMING_SNAKE_CASE       ).parent.name
    SCREAMING_SNAKE_CASE_									=						task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
    SCREAMING_SNAKE_CASE_									=						PegasusTokenizer.from_pretrained('sshleifer/pegasus' ,   model_max_length=_SCREAMING_SNAKE_CASE       )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(_SCREAMING_SNAKE_CASE       )

    # convert model
    SCREAMING_SNAKE_CASE_									=						get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						task_specific_params[f"""summarization_{dataset}"""]
    if dataset == "large":
        SCREAMING_SNAKE_CASE_									=						task_specific_params
    SCREAMING_SNAKE_CASE_									=						convert_pegasus(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    torch_model.save_pretrained(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						torch_model.state_dict()
    sd.pop('model.decoder.embed_positions.weight'       )
    sd.pop('model.encoder.embed_positions.weight'       )
    torch.save(_SCREAMING_SNAKE_CASE ,   Path(_SCREAMING_SNAKE_CASE       ) / 'pytorch_model.bin'       )


if __name__ == "__main__":
       UpperCamelCase__  :      Any			=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
       parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       if args.save_dir is None:
              UpperCamelCase__  :      Optional[Any]			=    Path(args.tf_ckpt_path).parent.name
              UpperCamelCase__  :      Any			=    os.path.join("pegasus", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

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


