
import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[int]						    =							[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

lowercase  :					Any						    =							[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     str       	=      torch.load(_lowerCamelCase					,	map_location="""cpu"""				)
       return sd







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=rename_keys_prefix				) ->    Union[str, Any]:
       lowercase							:     Tuple       	=      OrderedDict()
       lowercase							:     Optional[Any]       	=      torch.arange(config.max_position_embeddings				).expand((1, -1)				)
       # detector_d = OrderedDict()
       for key in d:
              if "detector" in key:
                     # detector_d[key.replace('detector.','')] = d[key]
                     continue
              lowercase							:     Tuple       	=      key
              for name_pair in rename_keys_prefix:
                     lowercase							:     List[str]       	=      new_key.replace(name_pair[0]					,	name_pair[1]				)
              lowercase							:     str       	=      d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     lowercase							:     Dict       	=      new_d["cls.predictions.bias"]
       return new_d







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       assert (
           checkpoint_path.split("""/"""				)[-1] in ACCEPTABLE_CHECKPOINTS
       ), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."

       # Get Config
       if "pre" in checkpoint_path:
              lowercase							:     Dict       	=      "pretraining"
              if "vcr" in checkpoint_path:
                     lowercase							:     Dict       	=      {"visual_embedding_dim": 512}
              elif "vqa_advanced" in checkpoint_path:
                     lowercase							:     List[Any]       	=      {"visual_embedding_dim": 2_048}
              elif "vqa" in checkpoint_path:
                     lowercase							:     int       	=      {"visual_embedding_dim": 2_048}
              elif "nlvr" in checkpoint_path:
                     lowercase							:     List[Any]       	=      {"visual_embedding_dim": 1_024}
              else:
                     raise NotImplementedError(f"No implementation found for `{checkpoint_path}`."				)
       else:
              if "vcr" in checkpoint_path:
                     lowercase							:     Any       	=      {"visual_embedding_dim": 512}
                     lowercase							:     Dict       	=      "multichoice"
              elif "vqa_advanced" in checkpoint_path:
                     lowercase							:     str       	=      {"visual_embedding_dim": 2_048}
                     lowercase							:     Tuple       	=      "vqa_advanced"
              elif "vqa" in checkpoint_path:
                     lowercase							:     Optional[Any]       	=      {"visual_embedding_dim": 2_048, "num_labels": 3_129}
                     lowercase							:     str       	=      "vqa"
              elif "nlvr" in checkpoint_path:
                     lowercase							:     Any       	=      {
                         "visual_embedding_dim": 1_024,
                         "num_labels": 2,
                     }
                     lowercase							:     Dict       	=      "nlvr"

       lowercase							:     Dict       	=      VisualBertConfig(**_lowerCamelCase				)

       # Load State Dict
       lowercase							:     List[str]       	=      load_state_dict(_lowerCamelCase				)

       lowercase							:     Tuple       	=      get_new_dict(_lowerCamelCase					,	_lowerCamelCase				)

       if model_type == "pretraining":
              lowercase							:     Optional[int]       	=      VisualBertForPreTraining(_lowerCamelCase				)
       elif model_type == "vqa":
              lowercase							:     Optional[Any]       	=      VisualBertForQuestionAnswering(_lowerCamelCase				)
       elif model_type == "nlvr":
              lowercase							:     Any       	=      VisualBertForVisualReasoning(_lowerCamelCase				)
       elif model_type == "multichoice":
              lowercase							:     Tuple       	=      VisualBertForMultipleChoice(_lowerCamelCase				)

       model.load_state_dict(_lowerCamelCase				)
       # Save Checkpoints
       Path(_lowerCamelCase				).mkdir(exist_ok=_lowerCamelCase				)
       model.save_pretrained(_lowerCamelCase				)


if __name__ == "__main__":
  lowercase  :					Any						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
  parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
  lowercase  :					Tuple						    =							parser.parse_args()
  convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)


import argparse
import datetime




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					{
							    "0": "Sunday",
							    "1": "Monday",
							    "2": "Tuesday",
							    "3": "Wednesday",
							    "4": "Thursday",
							    "5": "Friday",
							    "6": "Saturday",
							}

							__UpperCamelCase	:   List[str]			   =					{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

							# Validate
							if not 0 < len(_lowerCamelCase) < 11:
														raise ValueError("Must be 10 characters long")

							# Get month
							__UpperCamelCase	:   int			   =					int(date_input[0] + date_input[1])
							# Validate
							if not 0 < m < 13:
														raise ValueError("Month must be between 1 - 12")

							__UpperCamelCase	:   str			   =					date_input[2]
							# Validate
							if sep_a not in ["-", "/"]:
														raise ValueError("Date separator must be '-' or '/'")

							# Get day
							__UpperCamelCase	:   int			   =					int(date_input[3] + date_input[4])
							# Validate
							if not 0 < d < 32:
														raise ValueError("Date must be between 1 - 31")

							# Get second separator
							__UpperCamelCase	:   str			   =					date_input[5]
							# Validate
							if sep_a not in ["-", "/"]:
														raise ValueError("Date separator must be '-' or '/'")

							# Get year
							__UpperCamelCase	:   int			   =					int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
							# Arbitrary year range
							if not 45 < y < 8_500:
														raise ValueError(
														    "Year out of range. There has to be some sort of limit...right?")

							# Get datetime obj for validation
							__UpperCamelCase	:   Union[str, Any]			   =					datetime.date(int(_lowerCamelCase)						,  int(_lowerCamelCase)						,  int(_lowerCamelCase))

							# Start math
							if m <= 2:
														__UpperCamelCase	:   Any			   =					y - 1
														__UpperCamelCase	:   Optional[Any]			   =					m + 12
							# maths var
							__UpperCamelCase	:   int			   =					int(str(_lowerCamelCase)[:2])
							__UpperCamelCase	:   int			   =					int(str(_lowerCamelCase)[2:])
							__UpperCamelCase	:   int			   =					int(2.6 * m - 5.3_9)
							__UpperCamelCase	:   int			   =					int(c / 4)
							__UpperCamelCase	:   int			   =					int(k / 4)
							__UpperCamelCase	:   int			   =					int(d + k)
							__UpperCamelCase	:   int			   =					int(t + u + v + x)
							__UpperCamelCase	:   int			   =					int(z - (2 * c))
							__UpperCamelCase	:   int			   =					round(w % 7)
							# End math

							# Validate math
							if f != convert_datetime_days[dt_ck.weekday()]:
														raise AssertionError("The date was evaluated incorrectly. Contact developer.")

							# Response
							__UpperCamelCase	:   str			   =					F'Your date {date_input}, is a {days[str(_lowerCamelCase)]}!'
							return response


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase  :					Optional[int]													=       argparse.ArgumentParser(
		    description=(
		        'Find out what day of the week nearly any date is or was. Enter '
		        'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
		    )
		)
		parser.add_argument(
		    'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
		)
		lowercase  :					Tuple													=       parser.parse_args()
		zeller(args.date_input)