






'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 						__UpperCAmelCase							(			__magic_name__    )->     Union[str, Any]:



			"""simple docstring"""



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


if __name__ == "__main__":
		__lowerCamelCase :       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''')
		__lowerCamelCase :       str 				=       parser.parse_args()
		convert_tf_gptsan_to_pt(args)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

