
'''simple docstring'''





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification

def      A (       A_							:					Optional[int]       ):
						snake_case       :     Any =    SwinConfig()
						snake_case       :     Dict =    swin_name.split('''_'''       )

						snake_case       :     List[Any] =    name_split[1]
						snake_case       :     Optional[int] =    int(name_split[4]       )
						snake_case       :     Any =    int(name_split[3][-1]       )

						if model_size == "tiny":
												snake_case       :     int =    96
												snake_case       :     Optional[Any] =    (2, 2, 6, 2)
												snake_case       :     Dict =    (3, 6, 12, 24)
						elif model_size == "small":
												snake_case       :     Any =    96
												snake_case       :     Optional[int] =    (2, 2, 18, 2)
												snake_case       :     Optional[Any] =    (3, 6, 12, 24)
						elif model_size == "base":
												snake_case       :     Union[str, Any] =    128
												snake_case       :     Any =    (2, 2, 18, 2)
												snake_case       :     Optional[Any] =    (4, 8, 16, 32)
						else:
												snake_case       :     str =    192
												snake_case       :     List[Any] =    (2, 2, 18, 2)
												snake_case       :     List[str] =    (6, 12, 24, 48)

						if "in22k" in swin_name:
												snake_case       :     Optional[int] =    21841
						else:
												snake_case       :     Optional[int] =    1000
												snake_case       :     List[Any] =    '''huggingface/label-files'''
												snake_case       :     Optional[int] =    '''imagenet-1k-id2label.json'''
												snake_case       :     str =    json.load(open(hf_hub_download(A_    ,							A_    ,							repo_type='''dataset'''       )    ,							'''r'''       )       )
												snake_case       :     str =    {int(A_       ): v for k, v in idalabel.items()}
												snake_case       :     Optional[int] =    idalabel
												snake_case       :     Dict =    {v: k for k, v in idalabel.items()}

						snake_case       :     int =    img_size
						snake_case       :     str =    num_classes
						snake_case       :     Tuple =    embed_dim
						snake_case       :     Tuple =    depths
						snake_case       :     List[Any] =    num_heads
						snake_case       :     Union[str, Any] =    window_size

						return config

def      A (       A_							:					str       ):
						if "patch_embed.proj" in name:
												snake_case       :     Dict =    name.replace('''patch_embed.proj'''    ,							'''embeddings.patch_embeddings.projection'''       )
						if "patch_embed.norm" in name:
												snake_case       :     List[str] =    name.replace('''patch_embed.norm'''    ,							'''embeddings.norm'''       )
						if "layers" in name:
												snake_case       :     Dict =    '''encoder.''' + name
						if "attn.proj" in name:
												snake_case       :     str =    name.replace('''attn.proj'''    ,							'''attention.output.dense'''       )
						if "attn" in name:
												snake_case       :     Tuple =    name.replace('''attn'''    ,							'''attention.self'''       )
						if "norm1" in name:
												snake_case       :     Dict =    name.replace('''norm1'''    ,							'''layernorm_before'''       )
						if "norm2" in name:
												snake_case       :     Any =    name.replace('''norm2'''    ,							'''layernorm_after'''       )
						if "mlp.fc1" in name:
												snake_case       :     List[str] =    name.replace('''mlp.fc1'''    ,							'''intermediate.dense'''       )
						if "mlp.fc2" in name:
												snake_case       :     int =    name.replace('''mlp.fc2'''    ,							'''output.dense'''       )

						if name == "norm.weight":
												snake_case       :     Dict =    '''layernorm.weight'''
						if name == "norm.bias":
												snake_case       :     Dict =    '''layernorm.bias'''

						if "head" in name:
												snake_case       :     Optional[Any] =    name.replace('''head'''    ,							'''classifier'''       )
						else:
												snake_case       :     List[str] =    '''swin.''' + name

						return name

def      A (       A_							:					str    ,							A_							:					List[Any]       ):
						for key in orig_state_dict.copy().keys():
												snake_case       :     List[str] =    orig_state_dict.pop(A_       )

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		snake_case       :     int =    key.split('''.'''       )
																		snake_case       :     int =    int(key_split[1]       )
																		snake_case       :     Dict =    int(key_split[3]       )
																		snake_case       :     Dict =    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								snake_case       :     Any =    val[:dim, :]
																								snake_case       :     Optional[Any] =    val[
																								    dim : dim * 2, :
																								]
																								snake_case       :     int =    val[-dim:, :]
																		else:
																								snake_case       :     Any =    val[
																								    :dim
																								]
																								snake_case       :     Any =    val[
																								    dim : dim * 2
																								]
																								snake_case       :     Tuple =    val[
																								    -dim:
																								]
												else:
																		snake_case       :     Optional[int] =    val

						return orig_state_dict

def      A (       A_							:					Optional[int]    ,							A_							:					List[str]       ):
						snake_case       :     Optional[int] =    timm.create_model(A_    ,							pretrained=A_       )
						timm_model.eval()

						snake_case       :     Optional[Any] =    get_swin_config(A_       )
						snake_case       :     List[str] =    SwinForImageClassification(A_       )
						model.eval()

						snake_case       :     int =    convert_state_dict(timm_model.state_dict()    ,							A_       )
						model.load_state_dict(A_       )

						snake_case       :     str =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

						snake_case       :     Optional[int] =    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''    ,							'''-'''       )       )       )
						snake_case       :     Tuple =    Image.open(requests.get(A_    ,							stream=A_       ).raw       )
						snake_case       :     int =    image_processor(images=A_    ,							return_tensors='''pt'''       )

						snake_case       :     List[str] =    timm_model(inputs['''pixel_values''']       )
						snake_case       :     int =    model(**A_       ).logits

						assert torch.allclose(A_    ,							A_    ,							atol=1e-3       )

						print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""       )
						model.save_pretrained(A_       )

						print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
						image_processor.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--swin_name",
						    default="swin_tiny_patch4_window7_224",
						    type=str,
						    help="Name of the Swin timm model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						UpperCAmelCase    					=   parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



'''simple docstring'''





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()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   "The Nymphenburg Palace is a beautiful palace in Munich!"

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     str =    {
						    '''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,
						}

						snake_case       :     int =    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
						snake_case       :     int =    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
						snake_case       :     List[Any] =    '''openwebtext_ccnews_stories_books_cased'''

						# Specify download folder to Gluonnlp's vocab
						snake_case       :     Optional[Any] =    os.path.join(get_home_dir()    ,							'''models'''       )
						snake_case       :     Dict =    _load_vocab(A_    ,							A_    ,							A_    ,							cls=A_       )

						snake_case       :     Tuple =    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_       )
						snake_case       :     Union[str, Any] =    original_bort._collect_params_with_prefix()

						# Build our config 🤗
						snake_case       :     Optional[int] =    {
						    '''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_       ),
						}

						snake_case       :     Optional[int] =    BertConfig.from_dict(A_       )
						snake_case       :     int =    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_							:					List[str]       ) -> 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_							:					Tuple    ,							A_							:					Dict       ):
												snake_case       :     Tuple =    hf_param.shape

												snake_case       :     List[Any] =    to_torch(params[gluon_param]       )
												snake_case       :     int =    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

						snake_case       :     List[str] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight    ,							'''word_embed.0.weight'''       )
						snake_case       :     Optional[Any] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight    ,							'''encoder.position_weight'''       )
						snake_case       :     Dict =    check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias    ,							'''encoder.layer_norm.beta'''       )
						snake_case       :     Tuple =    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)
						snake_case       :     str =    torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

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

												# self attention
												snake_case       :     BertSelfAttention =    layer.attention.self

												snake_case       :     Optional[int] =    check_and_map_params(
												    self_attn.key.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""       )

												snake_case       :     Any =    check_and_map_params(
												    self_attn.key.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_attn.query.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""       )
												snake_case       :     Dict =    check_and_map_params(
												    self_attn.query.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""       )

												# self attention output
												snake_case       :     BertSelfOutput =    layer.attention.output

												snake_case       :     Optional[Any] =    check_and_map_params(
												    self_output.dense.bias    ,							F"""encoder.transformer_cells.{i}.proj.bias"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_output.dense.weight    ,							F"""encoder.transformer_cells.{i}.proj.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_output.LayerNorm.bias    ,							F"""encoder.transformer_cells.{i}.layer_norm.beta"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    self_output.LayerNorm.weight    ,							F"""encoder.transformer_cells.{i}.layer_norm.gamma"""       )

												# intermediate
												snake_case       :     BertIntermediate =    layer.intermediate

												snake_case       :     Optional[Any] =    check_and_map_params(
												    intermediate.dense.bias    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    intermediate.dense.weight    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""       )

												# output
												snake_case       :     BertOutput =    layer.output

												snake_case       :     List[Any] =    check_and_map_params(
												    bert_output.dense.bias    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""       )
												snake_case       :     int =    check_and_map_params(
												    bert_output.dense.weight    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""       )
												snake_case       :     List[str] =    check_and_map_params(
												    bert_output.LayerNorm.bias    ,							F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""       )
												snake_case       :     List[str] =    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
						snake_case       :     Dict =    RobertaTokenizer.from_pretrained('''roberta-base'''       )

						snake_case       :     Any =    tokenizer.encode_plus(A_       )['''input_ids''']

						# Get gluon output
						snake_case       :     Dict =    mx.nd.array([input_ids]       )
						snake_case       :     Optional[int] =    original_bort(inputs=A_    ,							token_types=[]       )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(A_       )
						snake_case       :     Optional[int] =    BertModel.from_pretrained(A_       )
						hf_bort_model.eval()

						snake_case       :     Any =    tokenizer.encode_plus(A_    ,							return_tensors='''pt'''       )
						snake_case       :     Any =    hf_bort_model(**A_       )[0]

						snake_case       :     Optional[Any] =    output_gluon[0].asnumpy()
						snake_case       :     Union[str, Any] =    output_hf[0].detach().numpy()

						snake_case       :     Tuple =    np.max(np.abs(hf_layer - gluon_layer       )       ).item()
						snake_case       :     Tuple =    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__":
						UpperCAmelCase    					=   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."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

