




"""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   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):







				def get_masked_lm_array(lowercase__	):
								_lowerCamelCase  :    Any					=					f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								_lowerCamelCase  :    Union[str, Any]					=					tf.train.load_variable(__UpperCamelCase							,					__UpperCamelCase	)

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

								return torch.from_numpy(__UpperCamelCase	)

				def get_encoder_array(lowercase__	):
								_lowerCamelCase  :    Dict					=					f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								_lowerCamelCase  :    List[str]					=					tf.train.load_variable(__UpperCamelCase							,					__UpperCamelCase	)

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

								return torch.from_numpy(__UpperCamelCase	)

				def get_encoder_layer_array(lowercase__							,					lowercase__	):
								_lowerCamelCase  :    Dict					=					f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								_lowerCamelCase  :    List[str]					=					tf.train.load_variable(__UpperCamelCase							,					__UpperCamelCase	)

								if "kernel" in name:
												_lowerCamelCase  :    List[str]					=					array.transpose()

								return torch.from_numpy(__UpperCamelCase	)

				def get_encoder_attention_layer_array(lowercase__							,					lowercase__							,					lowercase__	):
								_lowerCamelCase  :    Any					=					f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								_lowerCamelCase  :    List[Any]					=					tf.train.load_variable(__UpperCamelCase							,					__UpperCamelCase	)
								_lowerCamelCase  :    Optional[int]					=					array.reshape(__UpperCamelCase	)

								if "kernel" in name:
												_lowerCamelCase  :    Optional[Any]					=					array.transpose()

								return torch.from_numpy(__UpperCamelCase	)

				print(f'''Loading model based on config from {config_path}...'''	)
				_lowerCamelCase  :    List[Any]					=					BertConfig.from_json_file(__UpperCamelCase	)
				_lowerCamelCase  :    Tuple					=					BertForMaskedLM(__UpperCamelCase	)

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

								# Self-attention
								_lowerCamelCase  :    BertSelfAttention					=					layer.attention.self

								_lowerCamelCase  :    str					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_query_dense/kernel'							,					self_attn.query.weight.data.shape	)
								_lowerCamelCase  :    Optional[Any]					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_query_dense/bias'							,					self_attn.query.bias.data.shape	)
								_lowerCamelCase  :    Dict					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_key_dense/kernel'							,					self_attn.key.weight.data.shape	)
								_lowerCamelCase  :    Optional[Any]					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_key_dense/bias'							,					self_attn.key.bias.data.shape	)
								_lowerCamelCase  :    Dict					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_value_dense/kernel'							,					self_attn.value.weight.data.shape	)
								_lowerCamelCase  :    int					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_value_dense/bias'							,					self_attn.value.bias.data.shape	)

								# Self-attention Output
								_lowerCamelCase  :    BertSelfOutput					=					layer.attention.output

								_lowerCamelCase  :    Optional[int]					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_output_dense/kernel'							,					self_output.dense.weight.data.shape	)
								_lowerCamelCase  :    Tuple					=					get_encoder_attention_layer_array(
								    __UpperCamelCase							,					'_output_dense/bias'							,					self_output.dense.bias.data.shape	)

								_lowerCamelCase  :    Tuple					=					get_encoder_layer_array(__UpperCamelCase							,					'_attention_layer_norm/gamma'	)
								_lowerCamelCase  :    Union[str, Any]					=					get_encoder_layer_array(__UpperCamelCase							,					'_attention_layer_norm/beta'	)

								# Intermediate
								_lowerCamelCase  :    BertIntermediate					=					layer.intermediate

								_lowerCamelCase  :    Tuple					=					get_encoder_layer_array(__UpperCamelCase							,					'_intermediate_dense/kernel'	)
								_lowerCamelCase  :    int					=					get_encoder_layer_array(__UpperCamelCase							,					'_intermediate_dense/bias'	)

								# Output
								_lowerCamelCase  :    BertOutput					=					layer.output

								_lowerCamelCase  :    Any					=					get_encoder_layer_array(__UpperCamelCase							,					'_output_dense/kernel'	)
								_lowerCamelCase  :    Union[str, Any]					=					get_encoder_layer_array(__UpperCamelCase							,					'_output_dense/bias'	)

								_lowerCamelCase  :    Optional[Any]					=					get_encoder_layer_array(__UpperCamelCase							,					'_output_layer_norm/gamma'	)
								_lowerCamelCase  :    Union[str, Any]					=					get_encoder_layer_array(__UpperCamelCase							,					'_output_layer_norm/beta'	)

				# Embeddings
				_lowerCamelCase  :    Dict					=					get_encoder_array('_position_embedding_layer/embeddings'	)
				_lowerCamelCase  :    int					=					get_encoder_array('_type_embedding_layer/embeddings'	)
				_lowerCamelCase  :    Any					=					get_encoder_array('_embedding_norm_layer/gamma'	)
				_lowerCamelCase  :    Tuple					=					get_encoder_array('_embedding_norm_layer/beta'	)

				# LM Head
				_lowerCamelCase  :    str					=					model.cls.predictions.transform

				_lowerCamelCase  :    List[Any]					=					get_masked_lm_array('dense/kernel'	)
				_lowerCamelCase  :    Tuple					=					get_masked_lm_array('dense/bias'	)

				_lowerCamelCase  :    List[Any]					=					get_masked_lm_array('layer_norm/gamma'	)
				_lowerCamelCase  :    str					=					get_masked_lm_array('layer_norm/beta'	)

				_lowerCamelCase  :    Any					=					get_masked_lm_array('embedding_table'	)

				# Pooling
				_lowerCamelCase  :    Optional[int]					=					BertPooler(config=__UpperCamelCase	)
				_lowerCamelCase  :    BertPooler					=					get_encoder_array('_pooler_layer/kernel'	)
				_lowerCamelCase  :    BertPooler					=					get_encoder_array('_pooler_layer/bias'	)

				# Export final model
				model.save_pretrained(__UpperCamelCase	)

				# Integration test - should load without any errors ;)
				_lowerCamelCase  :    Any					=					BertForMaskedLM.from_pretrained(__UpperCamelCase	)
				print(new_model.eval()	)

				print('Model conversion was done sucessfully!'	)


if __name__ == "__main__":
			lowercase__  		=		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.""",
			)
			lowercase__  		=		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 lowerCAmelCase__			:






			'''simple docstring'''



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






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


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()