





"""simple docstring"""




import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Any	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Optional[int]   )					->List[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	for i in range(encoder_config.num_hidden_layers   ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.norm1.weight""", F"""encoder.encoder.layer.{i}.layernorm_before.weight""")   )
		rename_keys.append((F"""encoder.deit.blocks.{i}.norm1.bias""", F"""encoder.encoder.layer.{i}.layernorm_before.bias""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.attn.proj.weight""", F"""encoder.encoder.layer.{i}.attention.output.dense.weight""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.attn.proj.bias""", F"""encoder.encoder.layer.{i}.attention.output.dense.bias""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.norm2.weight""", F"""encoder.encoder.layer.{i}.layernorm_after.weight""")   )
		rename_keys.append((F"""encoder.deit.blocks.{i}.norm2.bias""", F"""encoder.encoder.layer.{i}.layernorm_after.bias""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.mlp.fc1.weight""", F"""encoder.encoder.layer.{i}.intermediate.dense.weight""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.mlp.fc1.bias""", F"""encoder.encoder.layer.{i}.intermediate.dense.bias""")   )
		rename_keys.append(
		    (F"""encoder.deit.blocks.{i}.mlp.fc2.weight""", F"""encoder.encoder.layer.{i}.output.dense.weight""")   )
		rename_keys.append((F"""encoder.deit.blocks.{i}.mlp.fc2.bias""", F"""encoder.encoder.layer.{i}.output.dense.bias""")   )

	# cls token, position embeddings and patch embeddings of encoder
	rename_keys.extend(
	    [
	        ("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
	        ("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
	        ("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
	        ("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
	        ("encoder.deit.norm.weight", "encoder.layernorm.weight"),
	        ("encoder.deit.norm.bias", "encoder.layernorm.bias"),
	    ]   )

	return rename_keys






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       List[Any]   )					->Any:






	'''simple docstring'''






	for i in range(encoder_config.num_hidden_layers   ):
		# queries, keys and values (only weights, no biases)
		a :						List[Any]      =							state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""   )

		a :						Union[str, Any]      =							in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		a :						Union[str, Any]      =							in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		a :						str      =							in_proj_weight[
		    -encoder_config.hidden_size :, :
		]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]       ,			_lowercase		:       Optional[int]       ,			_lowercase		:       Tuple   )					->List[str]:






	'''simple docstring'''






	a :						List[str]      =							dct.pop(snake_case__   )
	a :						List[str]      =							val






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]   )					->List[str]:






	'''simple docstring'''






	if "handwritten" in checkpoint_url:
		a :						Any      =							'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
	elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
		a :						int      =							'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	a :						List[Any]      =							Image.open(requests.get(snake_case__       ,			stream=snake_case__   ).raw   ).convert("RGB"   )
	return im






@torch.no_grad()
def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Dict   )					->Dict:






	'''simple docstring'''






	a :						Optional[int]      =							ViTConfig(image_size=384       ,			qkv_bias=snake_case__   )
	a :						Optional[int]      =							TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		a :						Union[str, Any]      =							768
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		a :						Tuple      =							1024
		a :						int      =							4096
		a :						Any      =							24
		a :						int      =							16
		a :						Optional[Any]      =							1024
	else:
		raise ValueError("Should either find \'base\' or \'large\' in checkpoint URL"   )

	# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
	if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
		a :						List[str]      =							False
		a :						int      =							'''relu'''
		a :						Tuple      =							1024
		a :						Any      =							True
		a :						Optional[int]      =							False
		a :						Optional[int]      =							False

	# load HuggingFace model
	a :						Tuple      =							ViTModel(snake_case__       ,			add_pooling_layer=snake_case__   )
	a :						int      =							TrOCRForCausalLM(snake_case__   )
	a :						Dict      =							VisionEncoderDecoderModel(encoder=snake_case__       ,			decoder=snake_case__   )
	model.eval()

	# load state_dict of original model, rename some keys
	a :						List[str]      =							torch.hub.load_state_dict_from_url(snake_case__       ,			map_location="cpu"       ,			check_hash=snake_case__   )['''model''']

	a :						Tuple      =							create_rename_keys(snake_case__       ,			snake_case__   )
	for src, dest in rename_keys:
		rename_key(snake_case__       ,			snake_case__       ,			snake_case__   )
	read_in_q_k_v(snake_case__       ,			snake_case__   )

	# remove parameters we don't need
	del state_dict["encoder.deit.head.weight"]
	del state_dict["encoder.deit.head.bias"]
	del state_dict["decoder.version"]

	# add prefix to decoder keys
	for key, val in state_dict.copy().items():
		a :						Tuple      =							state_dict.pop(snake_case__   )
		if key.startswith("decoder"   ) and "output_projection" not in key:
			a :						Any      =							val
		else:
			a :						List[Any]      =							val

    # load state dict
	model.load_state_dict(snake_case__   )

	# Check outputs on an image
	a :						List[str]      =							ViTImageProcessor(size=encoder_config.image_size   )
	a :						Tuple      =							RobertaTokenizer.from_pretrained("roberta-large"   )
	a :						Any      =							TrOCRProcessor(snake_case__       ,			snake_case__   )

	a :						Any      =							processor(images=prepare_img(snake_case__   )       ,			return_tensors="pt"   ).pixel_values

	# verify logits
	a :						str      =							torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
	a :						Union[str, Any]      =							model(pixel_values=snake_case__       ,			decoder_input_ids=snake_case__   )
	a :						Union[str, Any]      =							outputs.logits

	a :						List[str]      =							torch.Size([1, 1, 5_0265]   )
	if "trocr-base-handwritten" in checkpoint_url:
		a :						Union[str, Any]      =							torch.tensor(
		    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]   )
	elif "trocr-large-handwritten" in checkpoint_url:
		a :						Tuple      =							torch.tensor(
		    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]   )
	elif "trocr-base-printed" in checkpoint_url:
		a :						Tuple      =							torch.tensor(
		    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]   )
	elif "trocr-large-printed" in checkpoint_url:
		a :						Optional[Any]      =							torch.tensor(
		    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]   )

	if "stage1" not in checkpoint_url:
		assert logits.shape == expected_shape, "Shape of logits not as expected"
		assert torch.allclose(logits[0, 0, :10]       ,			snake_case__       ,			atol=1E-3   ), "First elements of logits not as expected"

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


if __name__ == "__main__":
		a		:					Optional[Any]	      =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
		    type=str,
		    help='''URL to the original PyTorch checkpoint (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		a		:					Dict	      =     parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets





def 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						if iterations % 1_00 == 0:
									print(f"loss: {j} \t"		)  # printing the loss after every 100 iterations
			return theta


# In[68]:

if __name__ == "__main__":
					UpperCamelCase__				:Union[str, Any]			   =	datasets.load_iris()
					UpperCamelCase__				:Dict			   =	iris.data[:, :2]
					UpperCamelCase__				:Any			   =	(iris.target != 0) * 1

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def 				A_       ( snake_case__		)					->  Optional[Any]:
								return sigmoid_function(
								    np.dot(snake_case__       ,					snake_case__		)		)  # predicting the value of probability from the logistic regression algorithm

					plt.figure(figsize=(10, 6))
					plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
					plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

