
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()
lowerCamelCase_						: Optional[int]         =  logging.get_logger(__name__)







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







def        A__					(						lowerCamelCase						)  ->	List[Any]:
				UpperCamelCase_:							Any	=						["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase     ,    lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Union[str, Any]	=						dct.pop(lowerCamelCase						)
				UpperCamelCase_:							int	=						val







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				UpperCamelCase_:							str	=						ViltConfig(image_size=3_84     ,    patch_size=32     ,    tie_word_embeddings=lowerCamelCase						)
				UpperCamelCase_:							Union[str, Any]	=						False
				UpperCamelCase_:							List[Any]	=						False
				UpperCamelCase_:							Optional[int]	=						False
				UpperCamelCase_:							Union[str, Any]	=						False
				if "vqa" in checkpoint_url:
								UpperCamelCase_:							str	=						True
								UpperCamelCase_:							Optional[int]	=						31_29
								UpperCamelCase_:							List[str]	=						"""huggingface/label-files"""
								UpperCamelCase_:							List[str]	=						"""vqa2-id2label.json"""
								UpperCamelCase_:							int	=						json.load(open(hf_hub_download(lowerCamelCase     ,    lowerCamelCase     ,    repo_type="""dataset"""						)     ,    """r"""						)						)
								UpperCamelCase_:							str	=						{int(lowerCamelCase						): v for k, v in idalabel.items()}
								UpperCamelCase_:							str	=						idalabel
								UpperCamelCase_:							List[str]	=						{v: k for k, v in idalabel.items()}
								UpperCamelCase_:							Union[str, Any]	=						ViltForQuestionAnswering(lowerCamelCase						)
				elif "nlvr" in checkpoint_url:
								UpperCamelCase_:							Optional[Any]	=						True
								UpperCamelCase_:							Tuple	=						2
								UpperCamelCase_:							str	=						{0: """False""", 1: """True"""}
								UpperCamelCase_:							Tuple	=						{v: k for k, v in config.idalabel.items()}
								UpperCamelCase_:							Union[str, Any]	=						3
								UpperCamelCase_:							int	=						ViltForImagesAndTextClassification(lowerCamelCase						)
				elif "irtr" in checkpoint_url:
								UpperCamelCase_:							str	=						True
								UpperCamelCase_:							Any	=						ViltForImageAndTextRetrieval(lowerCamelCase						)
				elif "mlm_itm" in checkpoint_url:
								UpperCamelCase_:							Optional[int]	=						True
								UpperCamelCase_:							Union[str, Any]	=						ViltForMaskedLM(lowerCamelCase						)
				else:
								raise ValueError("""Unknown model type"""						)

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

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

				# Define processor
				UpperCamelCase_:							List[str]	=						ViltImageProcessor(size=3_84						)
				UpperCamelCase_:							Any	=						BertTokenizer.from_pretrained("""bert-base-uncased"""						)
				UpperCamelCase_:							str	=						ViltProcessor(lowerCamelCase     ,    lowerCamelCase						)

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

				# Verify outputs
				if mlm_model:
								UpperCamelCase_:							str	=						torch.Size([1, 11, 3_05_22]						)
								UpperCamelCase_:							Tuple	=						torch.tensor([-12.5061, -12.5123, -12.5174]						)
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]     ,    lowerCamelCase     ,    atol=1E-4						)

								# verify masked token prediction equals "cats"
								UpperCamelCase_:							Optional[Any]	=						outputs.logits[0, 4, :].argmax(-1						).item()
								assert tokenizer.decode([predicted_id]						) == "cats"
				elif vqa_model:
								UpperCamelCase_:							Union[str, Any]	=						torch.Size([1, 31_29]						)
								UpperCamelCase_:							str	=						torch.tensor([-15.9495, -18.1472, -10.3041]						)
								assert torch.allclose(outputs.logits[0, :3]     ,    lowerCamelCase     ,    atol=1E-4						)
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]     ,    lowerCamelCase     ,    atol=1E-4						)

								# verify vqa prediction equals "2"
								UpperCamelCase_:							List[str]	=						outputs.logits.argmax(-1						).item()
								assert model.config.idalabel[predicted_idx] == "2"
				elif nlvr_model:
								UpperCamelCase_:							Any	=						torch.Size([1, 2]						)
								UpperCamelCase_:							Dict	=						torch.tensor([-2.8721, 2.1291]						)
								assert torch.allclose(outputs.logits[0, :3]     ,    lowerCamelCase     ,    atol=1E-4						)
								assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
							lowerCamelCase_						: int         =  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."""
							)

							lowerCamelCase_						: int         =  parser.parse_args()
							convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[Any]	=						prev_guess - multiplicity * func(lowerCamelCase						) / diff_function(
												    lowerCamelCase						)
								else:
												raise ZeroDivisionError("""Could not find root"""						) from None

								# Precision is checked by comparing the difference of consecutive guesses
								if abs(next_guess - prev_guess						) < precision:
												return next_guess

								UpperCamelCase_:							Any	=						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")






