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


def 						UpperCAmelCase      (     a_		,       a_						)		->     Optional[Any]:







				"""simple docstring"""



				__A             =			[]
				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 						UpperCAmelCase      (     a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



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

								__A             =			in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								__A             =			in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								__A             =			in_proj_weight[
								    -encoder_config.hidden_size :, :
								]


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Dict:







				"""simple docstring"""



				__A             =			dct.pop(a_						)
				__A             =			val


def 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



				if "handwritten" in checkpoint_url:
								__A             =			"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             =			"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             =			Image.open(requests.get(a_		,       stream=a_						).raw						).convert("RGB"						)
				return im


@torch.no_grad()
def 						UpperCAmelCase      (     a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			ViTConfig(image_size=3_8_4		,       qkv_bias=a_						)
				__A             =			TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								__A             =			7_6_8
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								__A             =			1_0_2_4
								__A             =			4_0_9_6
								__A             =			2_4
								__A             =			1_6
								__A             =			1_0_2_4
				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             =			False
								__A             =			"relu"
								__A             =			1_0_2_4
								__A             =			True
								__A             =			False
								__A             =			False

				# load HuggingFace model
				__A             =			ViTModel(a_		,       add_pooling_layer=a_						)
				__A             =			TrOCRForCausalLM(a_						)
				__A             =			VisionEncoderDecoderModel(encoder=a_		,       decoder=a_						)
				model.eval()

				# load state_dict of original model, rename some keys
				__A             =			torch.hub.load_state_dict_from_url(a_		,       map_location="cpu"		,       check_hash=a_						)["model"]

				__A             =			create_rename_keys(a_		,       a_						)
				for src, dest in rename_keys:
								rename_key(a_		,       a_		,       a_						)
				read_in_q_k_v(a_		,       a_						)

				# 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             =			state_dict.pop(a_						)
								if key.startswith("decoder"						) and "output_projection" not in key:
												__A             =			val
								else:
												__A             =			val

    # load state dict
				model.load_state_dict(a_						)

				# Check outputs on an image
				__A             =			ViTImageProcessor(size=encoder_config.image_size						)
				__A             =			RobertaTokenizer.from_pretrained("roberta-large"						)
				__A             =			TrOCRProcessor(a_		,       a_						)

				__A             =			processor(images=prepare_img(a_						)		,       return_tensors="pt"						).pixel_values

				# verify logits
				__A             =			torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
				__A             =			model(pixel_values=a_		,       decoder_input_ids=a_						)
				__A             =			outputs.logits

				__A             =			torch.Size([1, 1, 5_0_2_6_5]						)
				if "trocr-base-handwritten" in checkpoint_url:
								__A             =			torch.tensor(
								    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]						)
				elif "trocr-large-handwritten" in checkpoint_url:
								__A             =			torch.tensor(
								    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]						)
				elif "trocr-base-printed" in checkpoint_url:
								__A             =			torch.tensor(
								    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]						)
				elif "trocr-large-printed" in checkpoint_url:
								__A             =			torch.tensor(
								    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]						)

				if "stage1" not in checkpoint_url:
								assert logits.shape == expected_shape, "Shape of logits not as expected"
								assert torch.allclose(logits[0, 0, :1_0]		,       a_		,       atol=1E-3						), "First elements of logits not as expected"

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[int]										=    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.'
					)
					SCREAMING_SNAKE_CASE							:Dict										=    parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			input_array

					# Random initial weights are assigned where first argument is the
					# number of nodes in previous layer and second argument is the
					# number of nodes in the next layer.

					# Random initial weights are assigned.
					# self.input_array.shape[1] is used to represent number of nodes in input layer.
					# First hidden layer consists of 4 nodes.
					__A             =			numpy.random.rand(
					    self.input_array.shape[1]							,4			)

					# Random initial values for the first hidden layer.
					# First hidden layer has 4 nodes.
					# Second hidden layer has 3 nodes.
					__A             =			numpy.random.rand(
					    4							,3			)

					# Random initial values for the second hidden layer.
					# Second hidden layer has 3 nodes.
					# Output layer has 1 node.
					__A             =			numpy.random.rand(3							,1			)

					# Real output values provided.
					__A             =			output_array

					# Predicted output values by the neural network.
					# Predicted_output array initially consists of zeroes.
					__A             =			numpy.zeros(output_array.shape			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			sigmoid(
					    numpy.dot(self.input_array							,self.input_layer_and_first_hidden_layer_weights			)			)

					# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
					# connecting the first hidden set of nodes with the second hidden set of nodes.
					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_input_and_first_hidden_layer							,self.first_hidden_layer_and_second_hidden_layer_weights							,)			)

					# layer_between_second_hidden_layer_and_output is the layer connecting
					# second hidden layer with the output node.
					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer							,self.second_hidden_layer_and_output_layer_weights							,)			)

					return self.layer_between_second_hidden_layer_and_output






	def     UpperCamelCase_  (	self :			str			):
					__A             =			numpy.dot(
					    self.layer_between_first_hidden_layer_and_second_hidden_layer.T							,2
					    * (self.output_array - self.predicted_output)
					    * sigmoid_derivative(self.predicted_output			)							,)
					__A             =			numpy.dot(
					    self.layer_between_input_and_first_hidden_layer.T							,numpy.dot(
					        2
					        * (self.output_array - self.predicted_output)
					        * sigmoid_derivative(self.predicted_output			)							,self.second_hidden_layer_and_output_layer_weights.T							,)
					    * sigmoid_derivative(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer			)							,)
					__A             =			numpy.dot(
					    self.input_array.T							,numpy.dot(
					        numpy.dot(
					            2
					            * (self.output_array - self.predicted_output)
					            * sigmoid_derivative(self.predicted_output			)							,self.second_hidden_layer_and_output_layer_weights.T							,)
					        * sigmoid_derivative(
					            self.layer_between_first_hidden_layer_and_second_hidden_layer			)							,self.first_hidden_layer_and_second_hidden_layer_weights.T							,)
					    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer			)							,)

					self.input_layer_and_first_hidden_layer_weights += (
					    updated_input_layer_and_first_hidden_layer_weights
					)
					self.first_hidden_layer_and_second_hidden_layer_weights += (
					    updated_first_hidden_layer_and_second_hidden_layer_weights
					)
					self.second_hidden_layer_and_output_layer_weights += (
					    updated_second_hidden_layer_and_output_layer_weights
					)






	def     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

					__A             =			sigmoid(
					    numpy.dot(self.array							,self.input_layer_and_first_hidden_layer_weights			)			)

					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_input_and_first_hidden_layer							,self.first_hidden_layer_and_second_hidden_layer_weights							,)			)

					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer							,self.second_hidden_layer_and_output_layer_weights							,)			)

					return int(self.layer_between_second_hidden_layer_and_output > 0.6			)





def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			numpy.array(
				    (
				        [0, 0, 0],
				        [0, 0, 1],
				        [0, 1, 0],
				        [0, 1, 1],
				        [1, 0, 0],
				        [1, 0, 1],
				        [1, 1, 0],
				        [1, 1, 1],
				    )		,       dtype=numpy.floataa		,       )

				# True output values for the given input values.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			TwoHiddenLayerNeuralNetwork(
				    input_array=a_		,       output_array=a_						)

				# Calling training function.
				# Set give_loss to True if you want to see loss in every iteration.
				neural_network.train(output=a_		,       iterations=1_0		,       give_loss=a_						)

				return neural_network.predict(numpy.array(([1, 1, 1])		,       dtype=numpy.floataa						)						)


if __name__ == "__main__":
					example()
