import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
  raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
  raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			:						Any				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		"""The Nymphenburg Palace is a beautiful palace in Munich!"""



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	{
     "attention_cell": "multi_head",
     "num_layers": 4,
     "units": 1024,
     "hidden_size": 768,
     "max_length": 512,
     "num_heads": 8,
     "scaled": True,
     "dropout": 0.1,
     "use_residual": True,
     "embed_size": 1024,
     "embed_dropout": 0.1,
     "word_embed": None,
     "layer_norm_eps": 1E-5,
     "token_type_vocab_size": 2,
 }

 __a							=	bort_4_8_768_1024_hparams

 # Let's construct the original Bort model here
 # Taken from official BERT implementation, see:
 # https://github.com/alexa/bort/blob/master/bort/bort.py
 __a							=	BERTEncoder(
     attention_cell=predefined_args["attention_cell"]				,					num_layers=predefined_args["num_layers"]				,					units=predefined_args["units"]				,					hidden_size=predefined_args["hidden_size"]				,					max_length=predefined_args["max_length"]				,					num_heads=predefined_args["num_heads"]				,					scaled=predefined_args["scaled"]				,					dropout=predefined_args["dropout"]				,					output_attention=_A				,					output_all_encodings=_A				,					use_residual=predefined_args["use_residual"]				,					activation=predefined_args.get("activation"				,					"gelu"       )				,					layer_norm_eps=predefined_args.get("layer_norm_eps"				,					_A       )				,					)

 # Vocab information needs to be fetched first
 # It's the same as RoBERTa, so RobertaTokenizer can be used later
 __a							=	"openwebtext_ccnews_stories_books_cased"

 # Specify download folder to Gluonnlp's vocab
 __a							=	os.path.join(get_home_dir()				,					"models"       )
 __a							=	_load_vocab(_A				,					_A				,					_A				,					cls=_A       )

 __a							=	nlp.model.BERTModel(
     _A				,					len(_A       )				,					units=predefined_args["units"]				,					embed_size=predefined_args["embed_size"]				,					embed_dropout=predefined_args["embed_dropout"]				,					word_embed=predefined_args["word_embed"]				,					use_pooler=_A				,					use_token_type_embed=_A				,					token_type_vocab_size=predefined_args["token_type_vocab_size"]				,					use_classifier=_A				,					use_decoder=_A				,					)

 original_bort.load_parameters(_A				,					cast_dtype=_A				,					ignore_extra=_A       )
 __a							=	original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __a							=	{
     "architectures": ["BertForMaskedLM"],
     "attention_probs_dropout_prob": predefined_args["dropout"],
     "hidden_act": "gelu",
     "hidden_dropout_prob": predefined_args["dropout"],
     "hidden_size": predefined_args["embed_size"],
     "initializer_range": 0.02,
     "intermediate_size": predefined_args["hidden_size"],
     "layer_norm_eps": predefined_args["layer_norm_eps"],
     "max_position_embeddings": predefined_args["max_length"],
     "model_type": "bort",
     "num_attention_heads": predefined_args["num_heads"],
     "num_hidden_layers": predefined_args["num_layers"],
     "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
     "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
     "vocab_size": len(_A       ),
 }

 __a							=	BertConfig.from_dict(_A       )
 __a							=	BertForMaskedLM(_A       )
 hf_bort_model.eval()

 # Parameter mapping table (Gluonnlp to Transformers)
 # * denotes layer index
 #
 # | Gluon Parameter                                                | Transformers Parameter
 # | -------------------------------------------------------------- | ----------------------
 # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
 # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
 # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
 # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
 # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
 # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
 # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
 # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
 # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
 # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
 # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
 # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
 # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
 # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

 # Helper function to convert MXNET Arrays to PyTorch
 def to_torch(_A       ) -> nn.Parameter:
  return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()       )       )

 # Check param shapes and map new HF param back
 def check_and_map_params(_A				,					_A       ):
  __a							=	hf_param.shape

  __a							=	to_torch(params[gluon_param]       )
  __a							=	gluon_param.shape

  assert (
      shape_hf == shape_gluon
  ), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

  return gluon_param

 __a							=	check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight				,					"word_embed.0.weight"       )
 __a							=	check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight				,					"encoder.position_weight"       )
 __a							=	check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias				,					"encoder.layer_norm.beta"       )
 __a							=	check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.weight				,					"encoder.layer_norm.gamma"       )

 # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
 __a							=	torch.zeros_like(
     hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

 for i in range(hf_bort_config.num_hidden_layers       ):
  __a							=	hf_bort_model.bert.encoder.layer[i]

  # self attention
  __a							=	layer.attention.self

  __a							=	check_and_map_params(
      self_attn.key.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""       )

  __a							=	check_and_map_params(
      self_attn.key.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""       )
  __a							=	check_and_map_params(
      self_attn.query.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""       )
  __a							=	check_and_map_params(
      self_attn.query.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""       )
  __a							=	check_and_map_params(
      self_attn.value.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""       )
  __a							=	check_and_map_params(
      self_attn.value.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""       )

  # self attention output
  __a							=	layer.attention.output

  __a							=	check_and_map_params(
      self_output.dense.bias				,					f"""encoder.transformer_cells.{i}.proj.bias"""       )
  __a							=	check_and_map_params(
      self_output.dense.weight				,					f"""encoder.transformer_cells.{i}.proj.weight"""       )
  __a							=	check_and_map_params(
      self_output.LayerNorm.bias				,					f"""encoder.transformer_cells.{i}.layer_norm.beta"""       )
  __a							=	check_and_map_params(
      self_output.LayerNorm.weight				,					f"""encoder.transformer_cells.{i}.layer_norm.gamma"""       )

  # intermediate
  __a							=	layer.intermediate

  __a							=	check_and_map_params(
      intermediate.dense.bias				,					f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""       )
  __a							=	check_and_map_params(
      intermediate.dense.weight				,					f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""       )

  # output
  __a							=	layer.output

  __a							=	check_and_map_params(
      bert_output.dense.bias				,					f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""       )
  __a							=	check_and_map_params(
      bert_output.dense.weight				,					f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""       )
  __a							=	check_and_map_params(
      bert_output.LayerNorm.bias				,					f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""       )
  __a							=	check_and_map_params(
      bert_output.LayerNorm.weight				,					f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""       )

 # Save space and energy 🎄
 hf_bort_model.half()

 # Compare output of both models
 __a							=	RobertaTokenizer.from_pretrained("roberta-base"       )

 __a							=	tokenizer.encode_plus(_A       )["input_ids"]

 # Get gluon output
 __a							=	mx.nd.array([input_ids]       )
 __a							=	original_bort(inputs=_A				,					token_types=[]       )

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(_A       )
 __a							=	BertModel.from_pretrained(_A       )
 hf_bort_model.eval()

 __a							=	tokenizer.encode_plus(_A				,					return_tensors="pt"       )
 __a							=	hf_bort_model(**_A       )[0]

 __a							=	output_gluon[0].asnumpy()
 __a							=	output_hf[0].detach().numpy()

 __a							=	np.max(np.abs(hf_layer - gluon_layer       )       ).item()
 __a							=	np.allclose(_A				,					_A				,					atol=1E-3       )

 if success:
  print("✔️ Both model do output the same tensors"       )
 else:
  print("❌ Both model do **NOT** output the same tensors"       )
  print("Absolute difference is:"				,					_A       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Dict				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	original_name.split("."       )[0]
 __a							=	key.split("."       )
 __a							=	int(key_list[key_list.index(_A       ) - 2]       )
 __a							=	int(key_list[key_list.index(_A       ) - 1]       )
 __a							=	orig_block_num - offset

 __a							=	key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,					f"""block.{new_block_num}.{layer_num}.{new_name}"""       )
 return key



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	OrderedDict()
 __a							,  __a							=	0, 0
 for key, value in state_dict.items():
  if key.startswith("network"       ):
   __a							=	key.replace("network"				,					"poolformer.encoder"       )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith("bias"       ) and "patch_embed" not in key:
    patch_emb_offset += 1
   __a							=	key[: key.find("proj"       )]
   __a							=	key.replace(_A				,					f"""patch_embeddings.{total_embed_found}."""       )
   __a							=	key.replace("proj"				,					"projection"       )
   if key.endswith("bias"       ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   __a							=	"poolformer.encoder." + key
  if "mlp.fc1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc1"				,					"output.conv1"       )
  if "mlp.fc2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc2"				,					"output.conv2"       )
  if "norm1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm1"				,					"before_norm"       )
  if "norm2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm2"				,					"after_norm"       )
  if "layer_scale_1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_1"				,					"layer_scale_1"       )
  if "layer_scale_2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_2"				,					"layer_scale_2"       )
  if "head" in key:
   __a							=	key.replace("head"				,					"classifier"       )
  __a							=	value
 return new_state_dict



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )

 return image



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	PoolFormerConfig()

 # set attributes based on model_name
 __a							=	"huggingface/label-files"
 __a							=	model_name[-3:]
 __a							=	1000
 __a							=	"imagenet-1k-id2label.json"
 __a							=	(1, 1000)

 # set config attributes
 __a							=	json.load(open(hf_hub_download(_A				,					_A				,					repo_type="dataset"       )				,					"r"       )       )
 __a							=	{int(_A       ): v for k, v in idalabel.items()}
 __a							=	idalabel
 __a							=	{v: k for k, v in idalabel.items()}
 if size == "s12":
  __a							=	[2, 2, 6, 2]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s24":
  __a							=	[4, 4, 12, 4]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s36":
  __a							=	[6, 6, 18, 6]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.9
 elif size == "m36":
  __a							=	[6, 6, 18, 6]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 elif size == "m48":
  __a							=	[8, 8, 24, 8]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # load image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )

 # Prepare image
 __a							=	prepare_img()
 __a							=	image_processor(images=_A				,					return_tensors="pt"       ).pixel_values

 logger.info(f"""Converting model {model_name}..."""       )

 # load original state dict
 __a							=	torch.load(_A				,					map_location=torch.device("cpu"       )       )

 # rename keys
 __a							=	rename_keys(_A       )

 # create HuggingFace model and load state dict
 __a							=	PoolFormerForImageClassification(_A       )
 model.load_state_dict(_A       )
 model.eval()

 # Define image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )
 __a							=	image_processor(images=prepare_img()				,					return_tensors="pt"       ).pixel_values

 # forward pass
 __a							=	model(_A       )
 __a							=	outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __a							=	torch.tensor([-0.3045, -0.6758, -0.4869]       )
 elif size == "s24":
  __a							=	torch.tensor([0.4402, -0.1374, -0.8045]       )
 elif size == "s36":
  __a							=	torch.tensor([-0.6080, -0.5133, -0.5898]       )
 elif size == "m36":
  __a							=	torch.tensor([0.3952, 0.2263, -1.2668]       )
 elif size == "m48":
  __a							=	torch.tensor([0.1167, -0.0656, -0.3423]       )
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]				,					_A				,					atol=1E-2       )

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
 Path(_A       ).mkdir(exist_ok=_A       )
 model.save_pretrained(_A       )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
 image_processor.save_pretrained(_A       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""poolformer_s12""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


