




'''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_   (UpperCamelCase	:  str			,						UpperCamelCase	:  str			,						UpperCamelCase	:  str				):





							'''simple docstring'''


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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														return torch.from_numpy(UpperCamelCase				)

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

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

														# Self-attention
														_a     					=    layer.attention.self

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

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

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

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

														# Intermediate
														_a     					=    layer.intermediate

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

														# Output
														_a     					=    layer.output

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

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

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

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

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

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

							_a     					=    get_masked_lm_array('''embedding_table'''				)

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

							# Export final model
							model.save_pretrained(UpperCamelCase				)

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

							print('''Model conversion was done sucessfully!'''				)


if __name__ == "__main__":
				_snake_case				:				List[Any]       							=		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.',
				)
				_snake_case				:				Optional[int]       							=		parser.parse_args()
				convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

