
import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_snake_case      :							Optional[Any]     	=    ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
_snake_case      :							Union[str, Any]     	=    {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_snake_case      :							str     	=    logging.get_logger(__name__)

_snake_case      :							List[Any]     	=    " Hello world! cécé herlolip"

_snake_case      :							List[str]     	=    [
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      [
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(__magic_name__		,    __magic_name__						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      dct.pop(__magic_name__						)
				lowercase         =      val

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      torch.load(__magic_name__		,    map_location="cpu"						)
				lowercase         =      torch.hub.load("pytorch/fairseq"		,    "bart.large.cnn"						).eval()
				hub_interface.model.load_state_dict(sd["model"]						)
				return hub_interface

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase         =      emb.weight.shape
				lowercase         =      nn.Linear(__magic_name__		,    __magic_name__		,    bias=__magic_name__						)
				lowercase         =      emb.weight.data
				return lin_layer

@torch.no_grad()
def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=None						):







				'''simple docstring'''


				if not os.path.exists(__magic_name__						):
								lowercase         =      torch.hub.load("pytorch/fairseq"		,    __magic_name__						).eval()
				else:
								lowercase         =      load_xsum_checkpoint(__magic_name__						)

				bart.model.upgrade_state_dict(bart.model.state_dict()						)
				if hf_checkpoint_name is None:
								lowercase         =      checkpoint_path.replace("."		,    "-"						)
				lowercase         =      BartConfig.from_pretrained(__magic_name__						)
				lowercase         =      bart.encode(__magic_name__						).unsqueeze(0						)
				lowercase         =      BartTokenizer.from_pretrained(__magic_name__						).encode(__magic_name__		,    return_tensors="pt"						).unsqueeze(0						)
				if not torch.eq(__magic_name__		,    __magic_name__						).all():
								raise ValueError(
								    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''						)

				if checkpoint_path == "bart.large.mnli":
								lowercase         =      bart.state_dict()
								remove_ignore_keys_(__magic_name__						)
								lowercase         =      state_dict["model.decoder.embed_tokens.weight"]
								for src, dest in mnli_rename_keys:
												rename_key(__magic_name__		,    __magic_name__		,    __magic_name__						)
								lowercase         =      BartForSequenceClassification(__magic_name__						).eval()
								model.load_state_dict(__magic_name__						)
								lowercase         =      bart.predict("mnli"		,    __magic_name__		,    return_logits=__magic_name__						)
								lowercase         =      model(__magic_name__						)[0]  # logits
				else:  # no classification heads to worry about
								lowercase         =      bart.model.state_dict()
								remove_ignore_keys_(__magic_name__						)
								lowercase         =      state_dict["decoder.embed_tokens.weight"]
								lowercase         =      bart.extract_features(__magic_name__						)
								if hf_checkpoint_name == "facebook/bart-large":
												lowercase         =      BartModel(__magic_name__						).eval()
												model.load_state_dict(__magic_name__						)
												lowercase         =      model(__magic_name__						).model[0]
								else:
												lowercase         =      BartForConditionalGeneration(__magic_name__						).eval()  # an existing summarization ckpt
												model.model.load_state_dict(__magic_name__						)
												if hasattr(__magic_name__		,    "lm_head"						):
																lowercase         =      make_linear_from_emb(model.model.shared						)
												lowercase         =      model.model(__magic_name__						)[0]

    # Check results
				if fairseq_output.shape != new_model_outputs.shape:
								raise ValueError(
								    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''						)
				if (fairseq_output != new_model_outputs).any().item():
								raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"						)
				Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
				model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
						)
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument(
						    "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
						)
						_snake_case      :							Optional[Any]     	=    parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)


# Function to print upper half of diamond (pyramid)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(0		,    __magic_name__						):
								for _ in range(0		,    n - i - 1						):  # printing spaces
												print(" "		,    end=""						)
								for _ in range(0		,    i + 1						):  # printing stars
												print("* "		,    end=""						)
								print()

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(__magic_name__		,    0		,    -1						):
								for _ in range(__magic_name__		,    0		,    -1						):  # printing stars
												print("* "		,    end=""						)
								print()
								for _ in range(n - i + 1		,    0		,    -1						):  # printing spaces
												print(" "		,    end=""						)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if n <= 0:
								print("       ...       ....        nothing printing :("						)
								return
				floyd(__magic_name__						)  # upper half
				reverse_floyd(__magic_name__						)  # lower half


if __name__ == "__main__":
						print(r"| /\ | |- |  |-  |--| |\  /| |-")
						print(r"|/  \| |- |_ |_  |__| | \/ | |_")
						_snake_case      :							Union[str, Any]     	=    1
						while K:
												_snake_case      :							Dict     	=    int(input("enter the number and , and see the magic : "))
												print()
												pretty_print(user_number)
												_snake_case      :							Tuple     	=    int(input("press 0 to exit... and 1 to continue..."))

						print("Good Bye...")
