

'''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()
lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			'''The Nymphenburg Palace is a beautiful palace in Munich!'''


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				{
     '''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,
 }

 __lowercase													=				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
 __lowercase													=				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
 __lowercase													=				'''openwebtext_ccnews_stories_books_cased'''

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

 __lowercase													=				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__   )
 __lowercase													=				original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __lowercase													=				{
     '''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''': 0.0_2,
     '''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__   ),
 }

 __lowercase													=				BertConfig.from_dict(A__   )
 __lowercase													=				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__   ):
  __lowercase													=				hf_param.shape

  __lowercase													=				to_torch(params[gluon_param]   )
  __lowercase													=				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

 __lowercase													=				check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight  ,       '''word_embed.0.weight'''   )
 __lowercase													=				check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight  ,       '''encoder.position_weight'''   )
 __lowercase													=				check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias  ,       '''encoder.layer_norm.beta'''   )
 __lowercase													=				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)
 __lowercase													=				torch.zeros_like(
     hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

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

  # self attention
  __lowercase													=				layer.attention.self

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

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

  # self attention output
  __lowercase													=				layer.attention.output

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

  # intermediate
  __lowercase													=				layer.intermediate

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

  # output
  __lowercase													=				layer.output

  __lowercase													=				check_and_map_params(
      bert_output.dense.bias  ,       F"encoder.transformer_cells.{i}.ffn.ffn_2.bias"   )
  __lowercase													=				check_and_map_params(
      bert_output.dense.weight  ,       F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"   )
  __lowercase													=				check_and_map_params(
      bert_output.LayerNorm.bias  ,       F"encoder.transformer_cells.{i}.ffn.layer_norm.beta"   )
  __lowercase													=				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
 __lowercase													=				RobertaTokenizer.from_pretrained('''roberta-base'''   )

 __lowercase													=				tokenizer.encode_plus(A__   )['''input_ids''']

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

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

 __lowercase													=				tokenizer.encode_plus(A__  ,       return_tensors='''pt'''   )
 __lowercase													=				hf_bort_model(**A__   )[0]

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

 __lowercase													=				np.max(np.abs(hf_layer - gluon_layer   )   ).item()
 __lowercase													=				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__":
 lowerCAmelCase__      		=			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.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				F"{anno[0]} {x_center} {y_center} {width} {height}"
   annos_list.append(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       F"{label_name}.jpg"   )

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) + float(obj[4]   ) / 2

   boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
  if not boxes:
   continue
  img_paths.append(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __lowercase													=				np.zeros([output_size[0], output_size[1], 3]  ,       dtype=np.uinta   )
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				int(scale_x * output_size[1]   )
 __lowercase													=				int(scale_y * output_size[0]   )

 __lowercase													=				[]
 __lowercase													=				[]
 for i, index in enumerate(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   __lowercase													=				cva.resize(A__  ,       (output_size[1] - divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   __lowercase													=				cva.resize(
       A__  ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  __lowercase													=				[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]


def 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


if __name__ == "__main__":
 main()
 print('''DONE ✅''')

