




'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 						snake_case_       (UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							_a      	=							os.path.join(args.tf_model_dir					,		'''parameters.json''' )
							_a      	=							json.loads(open(UpperCamelCase ).read() )
							if not params:
														raise ValueError(
														    f'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
							if not args.output.endswith('''.pt''' ):
														_a      	=							args.output + '''.pt'''
							_a      	=							OrderedDict()
							with tf.device('''/CPU:0''' ):
														_a      	=							tf.train.load_checkpoint(args.tf_model_dir )
														_a      	=							reader.get_variable_to_shape_map()
														for key_name in shapes.keys():
																					_a      	=							reader.get_tensor(UpperCamelCase ).astype(np.floataa )
																					if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
																												continue
																					if key_name.startswith('''pasts/''' ):
																												if key_name.startswith('''pasts/mlp''' ):
																																			_a      	=							int(key_name[9] )
																												elif key_name.startswith('''pasts/out''' ):
																																			_a      	=							8
																												_a      	=							'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																												_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																												_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/moe''' ):
																												_a      	=							int(key_name[9:].split('''/''' )[0] )
																												if key_name.endswith('''/switch_gating/kernel''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																																			_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/softmlp/kernel''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																																			_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
																																			_a      	=							key_name[-9:-7]
																																			for i in range(16 ):
																																										_a      	=							'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																																										_a      	=							(
																																										    vnp[i].transpose([1, 0] ).copy()
																																										)  # In Mesh-Tensorflow, it is one array, so it is divided
																																										_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/mlp''' ):
																												_a      	=							int(key_name[9:].split('''/''' )[0] )
																												if key_name.endswith('''/p1/kernel''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																																			_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/p1/bias''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/p2/kernel''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																																			_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/p2/bias''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/ln''' ):
																												_a      	=							int(key_name[8:].split('''/''' )[0] )
																												if key_name.endswith('''/b''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.norm.bias''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/g''' ):
																																			_a      	=							'''model.blocks.%d.feed_forward.norm.weight''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/att''' ):
																												_a      	=							int(key_name[9:].split('''/''' )[0] )
																												if key_name.endswith('''/qkv/kernel''' ):
																																			_a      	=							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																																			_a      	=							state[:, 0, :, :]
																																			_a      	=							state[:, 1, :, :]
																																			_a      	=							state[:, 2, :, :]
																																			_a      	=							(
																																			    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
																																			    .transpose([1, 0] )
																																			    .copy()
																																			)  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							(
																																			    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
																																			    .transpose([1, 0] )
																																			    .copy()
																																			)  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							(
																																			    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
																																			    .transpose([1, 0] )
																																			    .copy()
																																			)  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																																			_a      	=							torch.tensor(UpperCamelCase )
																																			_a      	=							'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																																			_a      	=							torch.tensor(UpperCamelCase )
																																			_a      	=							'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/o/kernel''' ):
																																			_a      	=							'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																																			_a      	=							(
																																			    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																																			)  # Mesh-Tensorflow is a diagonal matrix
																																			_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/an''' ):
																												_a      	=							int(key_name[8:].split('''/''' )[0] )
																												if key_name.endswith('''/b''' ):
																																			_a      	=							'''model.blocks.%d.self_attn.norm.bias''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																												elif key_name.endswith('''/g''' ):
																																			_a      	=							'''model.blocks.%d.self_attn.norm.weight''' % player
																																			_a      	=							vnp.copy()  # same because it is one dimensional
																																			_a      	=							torch.tensor(UpperCamelCase )
																					elif (
																					    key_name.startswith('''model/wte''' )
																					    or key_name.startswith('''model/wpe''' )
																					    or key_name.startswith('''model/ete''' )
																					):
																												_a      	=							{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																												    key_name[-3:]
																												]
																												_a      	=							'''model.%s.weight''' % nlayer
																												_a      	=							vnp.copy()  # same in embedded
																												_a      	=							torch.tensor(UpperCamelCase )
																												if key_name.startswith('''model/wte''' ):
																																			_a      	=							'''lm_head.weight'''
																																			_a      	=							vnp.copy()  # same in embedded
																																			_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name.startswith('''model/wob''' ):
																												_a      	=							'''final_logits_bias'''
																												_a      	=							vnp.copy()  # same in embedded
																												_a      	=							state.reshape((1, -1) )
																												_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name == "model/dense/kernel":
																												_a      	=							'''model.last_project.weight'''
																												_a      	=							vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																												_a      	=							torch.tensor(UpperCamelCase )
																					elif key_name == "model/dense_1/bias":
																												_a      	=							'''model.last_project.bias'''
																												_a      	=							vnp.copy()  # same because it is one dimensional
																												_a      	=							torch.tensor(UpperCamelCase )
							torch.save(UpperCamelCase					,		args.output )


if __name__ == "__main__":
				_snake_case : str  				=						argparse.ArgumentParser(
				    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
				)
				parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
				parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
				_snake_case : List[str]  				=						parser.parse_args()
				convert_tf_gptsan_to_pt(args)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

