





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    _A     =							os.path.join(args.tf_model_dir							,     'parameters.json'				)
    _A     =							json.loads(open(__a				).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(__a				).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(__a				)
            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(__a				)
                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(__a				)
                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(__a				)
            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(__a				)
                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(__a				)
                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(__a				)
                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(__a				)
            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(__a				)
                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(__a				)
            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(__a				)
                    _A     =							'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                    _A     =							torch.tensor(__a				)
                    _A     =							'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                    _A     =							torch.tensor(__a				)
                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(__a				)
            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(__a				)
                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(__a				)
            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(__a				)
                if key_name.startswith('model/wte'				):
                    _A     =							'''lm_head.weight'''
                    _A     =							vnp.copy()  # same in embedded
                    _A     =							torch.tensor(__a				)
            elif key_name.startswith('model/wob'				):
                _A     =							'''final_logits_bias'''
                _A     =							vnp.copy()  # same in embedded
                _A     =							state.reshape((1, -1)				)
                _A     =							torch.tensor(__a				)
            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(__a				)
            elif key_name == "model/dense_1/bias":
                _A     =							'''model.last_project.bias'''
                _A     =							vnp.copy()  # same because it is one dimensional
                _A     =							torch.tensor(__a				)
    torch.save(__a							,     args.output				)


if __name__ == "__main__":
 __A							:			int         =			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")
 __A							:			List[str]         =			parser.parse_args()
 convert_tf_gptsan_to_pt(args)








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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



