


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Dict:





	"""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__":
				_lowercase							:   Optional[Any]						=						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							:   List[str]						=						parser.parse_args()
				convert_tf_gptsan_to_pt(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

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



