
'''simple docstring'''
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()







def 		A_(     A						:		str		,     A						:		str		,     A						:		str):
							def get_masked_lm_array(A						:		str):
														UpperCamelCase   			=     f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)

														if "kernel" in name:
																					UpperCamelCase   			=     array.transpose()

														return torch.from_numpy(A)

							def get_encoder_array(A						:		str):
														UpperCamelCase   			=     f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)

														if "kernel" in name:
																					UpperCamelCase   			=     array.transpose()

														return torch.from_numpy(A)

							def get_encoder_layer_array(A						:		int		,     A						:		str):
														UpperCamelCase   			=     f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)

														if "kernel" in name:
																					UpperCamelCase   			=     array.transpose()

														return torch.from_numpy(A)

							def get_encoder_attention_layer_array(A						:		int		,     A						:		str		,     A						:		List[str]):
														UpperCamelCase   			=     f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)
														UpperCamelCase   			=     array.reshape(A)

														if "kernel" in name:
																					UpperCamelCase   			=     array.transpose()

														return torch.from_numpy(A)

							print(f'''Loading model based on config from {config_path}...''')
							UpperCamelCase   			=     BertConfig.from_json_file(A)
							UpperCamelCase   			=     BertForMaskedLM(A)

							# Layers
							for layer_index in range(0		,     config.num_hidden_layers):
														UpperCamelCase   			=     model.bert.encoder.layer[layer_index]

														# Self-attention
														UpperCamelCase   			=     layer.attention.self

														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_query_dense/kernel'		,     self_attn.query.weight.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_query_dense/bias'		,     self_attn.query.bias.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_key_dense/kernel'		,     self_attn.key.weight.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_key_dense/bias'		,     self_attn.key.bias.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_value_dense/kernel'		,     self_attn.value.weight.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_value_dense/bias'		,     self_attn.value.bias.data.shape)

														# Self-attention Output
														UpperCamelCase   			=     layer.attention.output

														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_output_dense/kernel'		,     self_output.dense.weight.data.shape)
														UpperCamelCase   			=     get_encoder_attention_layer_array(
														    A		,     '_output_dense/bias'		,     self_output.dense.bias.data.shape)

														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_attention_layer_norm/gamma')
														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_attention_layer_norm/beta')

														# Intermediate
														UpperCamelCase   			=     layer.intermediate

														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_intermediate_dense/kernel')
														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_intermediate_dense/bias')

														# Output
														UpperCamelCase   			=     layer.output

														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_output_dense/kernel')
														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_output_dense/bias')

														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_output_layer_norm/gamma')
														UpperCamelCase   			=     get_encoder_layer_array(A		,     '_output_layer_norm/beta')

							# Embeddings
							UpperCamelCase   			=     get_encoder_array('_position_embedding_layer/embeddings')
							UpperCamelCase   			=     get_encoder_array('_type_embedding_layer/embeddings')
							UpperCamelCase   			=     get_encoder_array('_embedding_norm_layer/gamma')
							UpperCamelCase   			=     get_encoder_array('_embedding_norm_layer/beta')

							# LM Head
							UpperCamelCase   			=     model.cls.predictions.transform

							UpperCamelCase   			=     get_masked_lm_array('dense/kernel')
							UpperCamelCase   			=     get_masked_lm_array('dense/bias')

							UpperCamelCase   			=     get_masked_lm_array('layer_norm/gamma')
							UpperCamelCase   			=     get_masked_lm_array('layer_norm/beta')

							UpperCamelCase   			=     get_masked_lm_array('embedding_table')

							# Pooling
							UpperCamelCase   			=     BertPooler(config=A)
							UpperCamelCase   			=     get_encoder_array('_pooler_layer/kernel')
							UpperCamelCase   			=     get_encoder_array('_pooler_layer/bias')

							# Export final model
							model.save_pretrained(A)

							# Integration test - should load without any errors ;)
							UpperCamelCase   			=     BertForMaskedLM.from_pretrained(A)
							print(new_model.eval())

							print('Model conversion was done sucessfully!')


if __name__ == "__main__":
						lowerCAmelCase					:    int           = argparse.ArgumentParser()
						parser.add_argument(
						    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
						)
						parser.add_argument(
						    '--bert_config_file',
						    type=str,
						    required=True,
						    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path',
						    type=str,
						    required=True,
						    help='Path to the output PyTorch model.',
						)
						lowerCAmelCase					:    str           = parser.parse_args()
						convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




