




"""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    (       lowercase__ ):
				_lowerCamelCase  :    Dict					=					os.path.join(args.tf_model_dir							,					'parameters.json' )
				_lowerCamelCase  :    Union[str, Any]					=					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' ):
								_lowerCamelCase  :    List[str]					=					args.output + '.pt'
				_lowerCamelCase  :    int					=					OrderedDict()
				with tf.device('/CPU:0' ):
								_lowerCamelCase  :    Union[str, Any]					=					tf.train.load_checkpoint(args.tf_model_dir )
								_lowerCamelCase  :    Optional[Any]					=					reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_lowerCamelCase  :    Dict					=					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' ):
																				_lowerCamelCase  :    List[str]					=					int(key_name[9] )
																elif key_name.startswith('pasts/out' ):
																				_lowerCamelCase  :    int					=					8
																_lowerCamelCase  :    str					=					'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_lowerCamelCase  :    str					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_lowerCamelCase  :    List[Any]					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/moe' ):
																_lowerCamelCase  :    Union[str, Any]					=					int(key_name[9:].split('/' )[0] )
																if key_name.endswith('/switch_gating/kernel' ):
																				_lowerCamelCase  :    str					=					'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																				_lowerCamelCase  :    Optional[Any]					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_lowerCamelCase  :    int					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/softmlp/kernel' ):
																				_lowerCamelCase  :    List[Any]					=					'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																				_lowerCamelCase  :    Union[str, Any]					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_lowerCamelCase  :    Union[str, Any]					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
																				_lowerCamelCase  :    str					=					key_name[-9:-7]
																				for i in range(16 ):
																								_lowerCamelCase  :    str					=					'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																								_lowerCamelCase  :    Any					=					(
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_lowerCamelCase  :    Tuple					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/mlp' ):
																_lowerCamelCase  :    Any					=					int(key_name[9:].split('/' )[0] )
																if key_name.endswith('/p1/kernel' ):
																				_lowerCamelCase  :    Tuple					=					'model.blocks.%d.feed_forward.mlp.wi.weight' % player
																				_lowerCamelCase  :    Tuple					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_lowerCamelCase  :    Dict					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/p1/bias' ):
																				_lowerCamelCase  :    List[str]					=					'model.blocks.%d.feed_forward.mlp.wi.bias' % player
																				_lowerCamelCase  :    List[str]					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    Dict					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/p2/kernel' ):
																				_lowerCamelCase  :    Any					=					'model.blocks.%d.feed_forward.mlp.wo.weight' % player
																				_lowerCamelCase  :    List[str]					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_lowerCamelCase  :    Any					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/p2/bias' ):
																				_lowerCamelCase  :    Optional[Any]					=					'model.blocks.%d.feed_forward.mlp.wo.bias' % player
																				_lowerCamelCase  :    Union[str, Any]					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    Tuple					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/ln' ):
																_lowerCamelCase  :    List[Any]					=					int(key_name[8:].split('/' )[0] )
																if key_name.endswith('/b' ):
																				_lowerCamelCase  :    List[Any]					=					'model.blocks.%d.feed_forward.norm.bias' % player
																				_lowerCamelCase  :    Dict					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    Dict					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/g' ):
																				_lowerCamelCase  :    int					=					'model.blocks.%d.feed_forward.norm.weight' % player
																				_lowerCamelCase  :    Optional[Any]					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    Optional[int]					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/att' ):
																_lowerCamelCase  :    Tuple					=					int(key_name[9:].split('/' )[0] )
																if key_name.endswith('/qkv/kernel' ):
																				_lowerCamelCase  :    Optional[Any]					=					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_lowerCamelCase  :    Optional[int]					=					state[:, 0, :, :]
																				_lowerCamelCase  :    List[Any]					=					state[:, 1, :, :]
																				_lowerCamelCase  :    Any					=					state[:, 2, :, :]
																				_lowerCamelCase  :    Dict					=					(
																				    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
																				_lowerCamelCase  :    List[Any]					=					(
																				    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
																				_lowerCamelCase  :    int					=					(
																				    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
																				_lowerCamelCase  :    int					=					'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																				_lowerCamelCase  :    Any					=					torch.tensor(_UpperCamelCase )
																				_lowerCamelCase  :    Union[str, Any]					=					'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																				_lowerCamelCase  :    Union[str, Any]					=					torch.tensor(_UpperCamelCase )
																				_lowerCamelCase  :    Union[str, Any]					=					'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																				_lowerCamelCase  :    Any					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/o/kernel' ):
																				_lowerCamelCase  :    Optional[int]					=					'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																				_lowerCamelCase  :    List[Any]					=					(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_lowerCamelCase  :    Dict					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/an' ):
																_lowerCamelCase  :    Tuple					=					int(key_name[8:].split('/' )[0] )
																if key_name.endswith('/b' ):
																				_lowerCamelCase  :    int					=					'model.blocks.%d.self_attn.norm.bias' % player
																				_lowerCamelCase  :    Union[str, Any]					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    Optional[int]					=					torch.tensor(_UpperCamelCase )
																elif key_name.endswith('/g' ):
																				_lowerCamelCase  :    List[str]					=					'model.blocks.%d.self_attn.norm.weight' % player
																				_lowerCamelCase  :    Dict					=					vnp.copy()  # same because it is one dimensional
																				_lowerCamelCase  :    List[Any]					=					torch.tensor(_UpperCamelCase )
												elif (
												    key_name.startswith('model/wte' )
												    or key_name.startswith('model/wpe' )
												    or key_name.startswith('model/ete' )
												):
																_lowerCamelCase  :    Dict					=					{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																    key_name[-3:]
																]
																_lowerCamelCase  :    Optional[Any]					=					'model.%s.weight' % nlayer
																_lowerCamelCase  :    List[str]					=					vnp.copy()  # same in embedded
																_lowerCamelCase  :    Optional[Any]					=					torch.tensor(_UpperCamelCase )
																if key_name.startswith('model/wte' ):
																				_lowerCamelCase  :    List[str]					=					'lm_head.weight'
																				_lowerCamelCase  :    List[Any]					=					vnp.copy()  # same in embedded
																				_lowerCamelCase  :    List[Any]					=					torch.tensor(_UpperCamelCase )
												elif key_name.startswith('model/wob' ):
																_lowerCamelCase  :    Tuple					=					'final_logits_bias'
																_lowerCamelCase  :    Union[str, Any]					=					vnp.copy()  # same in embedded
																_lowerCamelCase  :    List[str]					=					state.reshape((1, -1) )
																_lowerCamelCase  :    List[str]					=					torch.tensor(_UpperCamelCase )
												elif key_name == "model/dense/kernel":
																_lowerCamelCase  :    int					=					'model.last_project.weight'
																_lowerCamelCase  :    Optional[int]					=					vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_lowerCamelCase  :    int					=					torch.tensor(_UpperCamelCase )
												elif key_name == "model/dense_1/bias":
																_lowerCamelCase  :    int					=					'model.last_project.bias'
																_lowerCamelCase  :    Any					=					vnp.copy()  # same because it is one dimensional
																_lowerCamelCase  :    str					=					torch.tensor(_UpperCamelCase )
				torch.save(_UpperCamelCase							,					args.output )


if __name__ == "__main__":
			lowercase__  		=		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""")
			lowercase__  		=		parser.parse_args()
			convert_tf_gptsan_to_pt(args)





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4