"""simple docstring"""


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[str, Any]						):
    if "model" in orig_key:
        __a			      =						orig_key.replace("""model."""				, """"""						)
    if "norm1" in orig_key:
        __a			      =						orig_key.replace("""norm1"""				, """attention.output.LayerNorm"""						)
    if "norm2" in orig_key:
        __a			      =						orig_key.replace("""norm2"""				, """output.LayerNorm"""						)
    if "norm" in orig_key:
        __a			      =						orig_key.replace("""norm"""				, """LayerNorm"""						)
    if "transformer" in orig_key:
        __a			      =						orig_key.split("""."""						)[0].split("""_"""						)[-1]
        __a			      =						orig_key.replace(f"""transformer_{layer_num}"""				, f"""encoder.layer.{layer_num}"""						)
    if "mha.attn" in orig_key:
        __a			      =						orig_key.replace("""mha.attn"""				, """attention.self"""						)
    if "mha" in orig_key:
        __a			      =						orig_key.replace("""mha"""				, """attention"""						)
    if "W_q" in orig_key:
        __a			      =						orig_key.replace("""W_q"""				, """self.query"""						)
    if "W_k" in orig_key:
        __a			      =						orig_key.replace("""W_k"""				, """self.key"""						)
    if "W_v" in orig_key:
        __a			      =						orig_key.replace("""W_v"""				, """self.value"""						)
    if "ff.0" in orig_key:
        __a			      =						orig_key.replace("""ff.0"""				, """intermediate.dense"""						)
    if "ff.2" in orig_key:
        __a			      =						orig_key.replace("""ff.2"""				, """output.dense"""						)
    if "ff" in orig_key:
        __a			      =						orig_key.replace("""ff"""				, """output.dense"""						)
    if "mlm_class" in orig_key:
        __a			      =						orig_key.replace("""mlm.mlm_class"""				, """cls.predictions.decoder"""						)
    if "mlm" in orig_key:
        __a			      =						orig_key.replace("""mlm"""				, """cls.predictions.transform"""						)
    if "backbone.backbone.encoders" in orig_key:
        __a			      =						orig_key.replace("""backbone.backbone.encoders"""				, """encoder.layer"""						)
    if "cls" not in orig_key:
        __a			      =						"""mra.""" + orig_key

    return orig_key

def 						UpperCamelCase				(	_lowerCAmelCase   :							int				, _lowerCAmelCase   :							List[Any]						):
    for key in orig_state_dict.copy().keys():
        __a			      =						orig_state_dict.pop(_lowerCAmelCase						)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __a			      =						val

    __a			      =						orig_state_dict["""cls.predictions.decoder.bias"""]
    __a			      =						torch.arange(_lowerCAmelCase						).expand((1, -1)						) + 2

    return orig_state_dict

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							Any						):
    __a			      =						torch.load(_lowerCAmelCase				, map_location="""cpu"""						)["""model_state_dict"""]
    __a			      =						MraConfig.from_json_file(_lowerCAmelCase						)
    __a			      =						MraForMaskedLM(_lowerCAmelCase						)

    __a			      =						convert_checkpoint_helper(config.max_position_embeddings				, _lowerCAmelCase						)

    print(model.load_state_dict(_lowerCAmelCase						)						)
    model.eval()
    model.save_pretrained(_lowerCAmelCase						)

    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
 __A       			=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The json file for Mra model config.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __A       			=      parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # Create a stack and push index of fist element to it
    __a			      =						[]
    st.append(0						)

    # Span value of first element is always 1
    __a			      =						1

    # Calculate span values for rest of the elements
    for i in range(1				, _lowerCAmelCase						):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(_lowerCAmelCase						) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        __a			      =						i + 1 if len(_lowerCAmelCase						) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


# Driver program to test above function
__A       			=      [10, 4, 5, 90, 1_20, 80]
__A       			=      [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)






print_array(S, len(price))
