



"""simple docstring"""


import argparse
import json
from pathlib import Path

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

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_				     =       logging.get_logger(__name__)

def  _lowerCAmelCase      (   lowercase_		,	lowercase_=False		,	lowercase_=False		,	lowercase_=False						):
						UpperCAmelCase             = []
						for i in range(config.num_hidden_layers						):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F"""transformer.blocks.{i}.norm1.weight""", F"""vilt.encoder.layer.{i}.layernorm_before.weight""")						)
												rename_keys.append((F"""transformer.blocks.{i}.norm1.bias""", F"""vilt.encoder.layer.{i}.layernorm_before.bias""")						)
												rename_keys.append(
												    (F"""transformer.blocks.{i}.attn.proj.weight""", F"""vilt.encoder.layer.{i}.attention.output.dense.weight""")						)
												rename_keys.append(
												    (F"""transformer.blocks.{i}.attn.proj.bias""", F"""vilt.encoder.layer.{i}.attention.output.dense.bias""")						)
												rename_keys.append((F"""transformer.blocks.{i}.norm2.weight""", F"""vilt.encoder.layer.{i}.layernorm_after.weight""")						)
												rename_keys.append((F"""transformer.blocks.{i}.norm2.bias""", F"""vilt.encoder.layer.{i}.layernorm_after.bias""")						)
												rename_keys.append(
												    (F"""transformer.blocks.{i}.mlp.fc1.weight""", F"""vilt.encoder.layer.{i}.intermediate.dense.weight""")						)
												rename_keys.append((F"""transformer.blocks.{i}.mlp.fc1.bias""", F"""vilt.encoder.layer.{i}.intermediate.dense.bias""")						)
												rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.weight""", F"""vilt.encoder.layer.{i}.output.dense.weight""")						)
												rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.bias""", F"""vilt.encoder.layer.{i}.output.dense.bias""")						)

						# embeddings
						rename_keys.extend(
						    [
						        # text embeddings
						        ('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
						        (
						            'text_embeddings.position_embeddings.weight',
						            'vilt.embeddings.text_embeddings.position_embeddings.weight',
						        ),
						        ('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
						        (
						            'text_embeddings.token_type_embeddings.weight',
						            'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
						        ),
						        ('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
						        ('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
						        # patch embeddings
						        ('transformer.cls_token', 'vilt.embeddings.cls_token'),
						        ('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
						        ('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
						        ('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
						        # token type embeddings
						        ('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
						    ]						)

						# final layernorm + pooler
						rename_keys.extend(
						    [
						        ('transformer.norm.weight', 'vilt.layernorm.weight'),
						        ('transformer.norm.bias', 'vilt.layernorm.bias'),
						        ('pooler.dense.weight', 'vilt.pooler.dense.weight'),
						        ('pooler.dense.bias', 'vilt.pooler.dense.bias'),
						    ]						)

						# classifier head(s)
						if vqa_model:
												# classification head
												rename_keys.extend(
												    [
												        ('vqa_classifier.0.weight', 'classifier.0.weight'),
												        ('vqa_classifier.0.bias', 'classifier.0.bias'),
												        ('vqa_classifier.1.weight', 'classifier.1.weight'),
												        ('vqa_classifier.1.bias', 'classifier.1.bias'),
												        ('vqa_classifier.3.weight', 'classifier.3.weight'),
												        ('vqa_classifier.3.bias', 'classifier.3.bias'),
												    ]						)
						elif nlvr_model:
												# classification head
												rename_keys.extend(
												    [
												        ('nlvr2_classifier.0.weight', 'classifier.0.weight'),
												        ('nlvr2_classifier.0.bias', 'classifier.0.bias'),
												        ('nlvr2_classifier.1.weight', 'classifier.1.weight'),
												        ('nlvr2_classifier.1.bias', 'classifier.1.bias'),
												        ('nlvr2_classifier.3.weight', 'classifier.3.weight'),
												        ('nlvr2_classifier.3.bias', 'classifier.3.bias'),
												    ]						)
						else:
												pass

						return rename_keys

def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):





						for i in range(config.num_hidden_layers						):
												UpperCAmelCase             = '''vilt.'''
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												UpperCAmelCase             = state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight"""						)
												UpperCAmelCase             = state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.bias"""						)
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase             = in_proj_weight[
												    : config.hidden_size, :
												]
												UpperCAmelCase             = in_proj_bias[: config.hidden_size]
												UpperCAmelCase             = in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase             = in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase             = in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase             = in_proj_bias[-config.hidden_size :]

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = ['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase_		,	UpperCAmelCase_						)

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						UpperCAmelCase             = dct.pop(UpperCAmelCase_						)
						UpperCAmelCase             = val

@torch.no_grad()
def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):
						UpperCAmelCase             = ViltConfig(image_size=384		,	patch_size=32		,	tie_word_embeddings=UpperCAmelCase_						)
						UpperCAmelCase             = False
						UpperCAmelCase             = False
						UpperCAmelCase             = False
						UpperCAmelCase             = False
						if "vqa" in checkpoint_url:
												UpperCAmelCase             = True
												UpperCAmelCase             = 3129
												UpperCAmelCase             = '''huggingface/label-files'''
												UpperCAmelCase             = '''vqa2-id2label.json'''
												UpperCAmelCase             = json.load(open(hf_hub_download(UpperCAmelCase_		,	UpperCAmelCase_		,	repo_type='dataset'						)		,	'r'						)						)
												UpperCAmelCase             = {int(UpperCAmelCase_						): v for k, v in idalabel.items()}
												UpperCAmelCase             = idalabel
												UpperCAmelCase             = {v: k for k, v in idalabel.items()}
												UpperCAmelCase             = ViltForQuestionAnswering(UpperCAmelCase_						)
						elif "nlvr" in checkpoint_url:
												UpperCAmelCase             = True
												UpperCAmelCase             = 2
												UpperCAmelCase             = {0: '''False''', 1: '''True'''}
												UpperCAmelCase             = {v: k for k, v in config.idalabel.items()}
												UpperCAmelCase             = 3
												UpperCAmelCase             = ViltForImagesAndTextClassification(UpperCAmelCase_						)
						elif "irtr" in checkpoint_url:
												UpperCAmelCase             = True
												UpperCAmelCase             = ViltForImageAndTextRetrieval(UpperCAmelCase_						)
						elif "mlm_itm" in checkpoint_url:
												UpperCAmelCase             = True
												UpperCAmelCase             = ViltForMaskedLM(UpperCAmelCase_						)
						else:
												raise ValueError('Unknown model type'						)

						# load state_dict of original model, remove and rename some keys
						UpperCAmelCase             = torch.hub.load_state_dict_from_url(UpperCAmelCase_		,	map_location='cpu'						)['''state_dict''']
						UpperCAmelCase             = create_rename_keys(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_						)
						for src, dest in rename_keys:
												rename_key(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_						)
						read_in_q_k_v(UpperCAmelCase_		,	UpperCAmelCase_						)
						if mlm_model or irtr_model:
												UpperCAmelCase             = ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
												for k in ignore_keys:
																		state_dict.pop(UpperCAmelCase_		,	UpperCAmelCase_						)

    # load state dict into HuggingFace model
						model.eval()
						if mlm_model:
												UpperCAmelCase             = model.load_state_dict(UpperCAmelCase_		,	strict=UpperCAmelCase_						)
												assert missing_keys == ["mlm_score.decoder.bias"]
						else:
												model.load_state_dict(UpperCAmelCase_						)

						# Define processor
						UpperCAmelCase             = ViltImageProcessor(size=384						)
						UpperCAmelCase             = BertTokenizer.from_pretrained('bert-base-uncased'						)
						UpperCAmelCase             = ViltProcessor(UpperCAmelCase_		,	UpperCAmelCase_						)

						# Forward pass on example inputs (image + text)
						if nlvr_model:
												UpperCAmelCase             = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'		,	stream=UpperCAmelCase_						).raw						)
												UpperCAmelCase             = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'		,	stream=UpperCAmelCase_						).raw						)
												UpperCAmelCase             = (
												    '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
												    ''' standing.'''
												)
												UpperCAmelCase             = processor(UpperCAmelCase_		,	UpperCAmelCase_		,	return_tensors='pt'						)
												UpperCAmelCase             = processor(UpperCAmelCase_		,	UpperCAmelCase_		,	return_tensors='pt'						)
												UpperCAmelCase             = model(
												    input_ids=encoding_a.input_ids		,	pixel_values=encoding_a.pixel_values		,	pixel_values_a=encoding_a.pixel_values		,	)
						else:
												UpperCAmelCase             = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'		,	stream=UpperCAmelCase_						).raw						)
												if mlm_model:
																		UpperCAmelCase             = '''a bunch of [MASK] laying on a [MASK].'''
												else:
																		UpperCAmelCase             = '''How many cats are there?'''
												UpperCAmelCase             = processor(UpperCAmelCase_		,	UpperCAmelCase_		,	return_tensors='pt'						)
												UpperCAmelCase             = model(**UpperCAmelCase_						)

						# Verify outputs
						if mlm_model:
												UpperCAmelCase             = torch.Size([1, 11, 30522]						)
												UpperCAmelCase             = torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]						)
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3]		,	UpperCAmelCase_		,	atol=1e-4						)

												# verify masked token prediction equals "cats"
												UpperCAmelCase             = outputs.logits[0, 4, :].argmax(-1						).item()
												assert tokenizer.decode([predicted_id]						) == "cats"
						elif vqa_model:
												UpperCAmelCase             = torch.Size([1, 3129]						)
												UpperCAmelCase             = torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]						)
												assert torch.allclose(outputs.logits[0, :3]		,	UpperCAmelCase_		,	atol=1e-4						)
												assert outputs.logits.shape == expected_shape
												assert torch.allclose(outputs.logits[0, 0, :3]		,	UpperCAmelCase_		,	atol=1e-4						)

												# verify vqa prediction equals "2"
												UpperCAmelCase             = outputs.logits.argmax(-1						).item()
												assert model.config.idalabel[predicted_idx] == "2"
						elif nlvr_model:
												UpperCAmelCase             = torch.Size([1, 2]						)
												UpperCAmelCase             = torch.tensor([-2.8_7_2_1, 2.1_2_9_1]						)
												assert torch.allclose(outputs.logits[0, :3]		,	UpperCAmelCase_		,	atol=1e-4						)
												assert outputs.logits.shape == expected_shape

						Path(UpperCAmelCase_						).mkdir(exist_ok=UpperCAmelCase_						)
						print(F"""Saving model and processor to {pytorch_dump_folder_path}"""						)
						model.save_pretrained(UpperCAmelCase_						)
						processor.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
				snake_case_				     =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--checkpoint_url""",
				    default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
				    type=str,
				    help="""URL of the checkpoint you\'d like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)

				snake_case_				     =       parser.parse_args()
				convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		list[str] | None = None ,  UpperCAmelCase_						:		dict[str, float] | None = None ,  UpperCAmelCase_						:		bool = False ,  ):




      """simple docstring"""





      a						:str 							=       cipher_alphabet or [chr(UpperCAmelCase_						) for i in range(97 ,  123						)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            a						:List[Any] 							=       {
                '''a''': 0.08497,
                '''b''': 0.01492,
                '''c''': 0.02202,
                '''d''': 0.04253,
                '''e''': 0.11162,
                '''f''': 0.02228,
                '''g''': 0.02015,
                '''h''': 0.06094,
                '''i''': 0.07546,
                '''j''': 0.00153,
                '''k''': 0.01292,
                '''l''': 0.04025,
                '''m''': 0.02406,
                '''n''': 0.06749,
                '''o''': 0.07507,
                '''p''': 0.01929,
                '''q''': 0.00095,
                '''r''': 0.07587,
                '''s''': 0.06327,
                '''t''': 0.09356,
                '''u''': 0.02758,
                '''v''': 0.00978,
                '''w''': 0.02560,
                '''x''': 0.00150,
                '''y''': 0.01994,
                '''z''': 0.00077,
            }
      else:
            # Custom frequencies dictionary
            a						:Dict 							=       frequencies_dict

      if not case_sensitive:
            a						:Union[str, Any] 							=       ciphertext.lower()

      # Chi squared statistic values
      a						:dict[int, tuple[float, str]] 							=       {}

      # cycle through all of the shifts
      for shift in range(len(UpperCAmelCase_						)						):
            a						:int 							=       ''''''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        a						:Dict 							=       (alphabet_letters.index(letter.lower()						) - shift) % len(
                            UpperCAmelCase_						)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            a						:List[Any] 							=       0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        a						:Optional[int] 							=       letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:List[Any] 							=       decrypted_with_shift.lower().count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Dict 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Any 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:int 							=       decrypted_with_shift.count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Tuple 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Optional[Any] 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            a						:Optional[Any] 							=       (
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(UpperCAmelCase_						:		int						) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      a						:int 							=       min(
          UpperCAmelCase_ ,  key=UpperCAmelCase_ ,  )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          a
      )							,				(
          a
      )							,				
      )						:Optional[int] 							=       chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )





