




'''simple docstring'''



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

import cva
import numpy as np

# Parrameters
UpperCAmelCase_							:	Any	     =		(7_2_0, 1_2_8_0)  # Height, Width
UpperCAmelCase_							:	List[Any]	     =		(0.4, 0.6)  # if height or width lower than this scale, drop it.
UpperCAmelCase_							:	Tuple	     =		1 / 1_0_0
UpperCAmelCase_							:	Dict	     =		''''''
UpperCAmelCase_							:	Tuple	     =		''''''
UpperCAmelCase_							:	List[Any]	     =		''''''
UpperCAmelCase_							:	Optional[Any]	     =		2_5_0







def       _UpperCamelCase			()-> None:




     '''simple docstring'''





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

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







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  str						)-> tuple[list, list]:




     '''simple docstring'''





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

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  list ,						_lowerCamelCase				:  list ,						_lowerCamelCase				:  list[int] ,						_lowerCamelCase				:  tuple[int, int] ,						_lowerCamelCase				:  tuple[float, float] ,						_lowerCamelCase				:  float = 0.0 ,						)-> tuple[list, list, str]:




     '''simple docstring'''





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

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




     '''simple docstring'''





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


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








'''simple docstring'''

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									=    ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
snake_case									=    {"""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									=    logging.get_logger(__name__)

snake_case									=    """ Hello world! cécé herlolip"""

snake_case									=    [
    ("""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        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
 lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			"bart.large.cnn"			).eval()
 hub_interface.model.load_state_dict(sd["model"]			)
 return hub_interface


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	emb.weight.shape
 lowerCAmelCase__     :   List[Any]     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if not os.path.exists(lowerCamelCase_			):
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			lowerCamelCase_			).eval()
 else:
  lowerCAmelCase__     :   Tuple     		=	load_xsum_checkpoint(lowerCamelCase_			)

 bart.model.upgrade_state_dict(bart.model.state_dict()			)
 if hf_checkpoint_name is None:
  lowerCAmelCase__     :   Union[str, Any]     		=	checkpoint_path.replace("."      ,			"-"			)
 lowerCAmelCase__     :   Optional[int]     		=	BartConfig.from_pretrained(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	bart.encode(lowerCamelCase_			).unsqueeze(0			)
 lowerCAmelCase__     :   str     		=	BartTokenizer.from_pretrained(lowerCamelCase_			).encode(lowerCamelCase_      ,			return_tensors="pt"			).unsqueeze(0			)
 if not torch.eq(lowerCamelCase_      ,			lowerCamelCase_			).all():
  raise ValueError(
      f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''			)

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


if __name__ == "__main__":
     snake_case									=    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									=    parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





