






"""simple docstring"""

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     os.path.join(args.tf_model_dir ,						"parameters.json"   )
						__magic_name__            =     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"   ):
												__magic_name__            =     args.output + ".pt"
						__magic_name__            =     OrderedDict()
						with tf.device("/CPU:0"   ):
												__magic_name__            =     tf.train.load_checkpoint(args.tf_model_dir   )
												__magic_name__            =     reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		__magic_name__            =     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"   ):
																														__magic_name__            =     int(key_name[9]   )
																								elif key_name.startswith("pasts/out"   ):
																														__magic_name__            =     8
																								__magic_name__            =     "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/moe"   ):
																								__magic_name__            =     int(key_name[9:].split("/"   )[0]   )
																								if key_name.endswith("/switch_gating/kernel"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																														__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/softmlp/kernel"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																														__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
																														__magic_name__            =     key_name[-9:-7]
																														for i in range(1_6   ):
																																				__magic_name__            =     "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																																				__magic_name__            =     (
																																				    vnp[i].transpose([1, 0]   ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/mlp"   ):
																								__magic_name__            =     int(key_name[9:].split("/"   )[0]   )
																								if key_name.endswith("/p1/kernel"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																														__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/p1/bias"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/p2/kernel"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																														__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/p2/bias"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/ln"   ):
																								__magic_name__            =     int(key_name[8:].split("/"   )[0]   )
																								if key_name.endswith("/b"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.norm.bias" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/g"   ):
																														__magic_name__            =     "model.blocks.%d.feed_forward.norm.weight" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/att"   ):
																								__magic_name__            =     int(key_name[9:].split("/"   )[0]   )
																								if key_name.endswith("/qkv/kernel"   ):
																														__magic_name__            =     vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														__magic_name__            =     state[:, 0, :, :]
																														__magic_name__            =     state[:, 1, :, :]
																														__magic_name__            =     state[:, 2, :, :]
																														__magic_name__            =     (
																														    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
																														__magic_name__            =     (
																														    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
																														__magic_name__            =     (
																														    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
																														__magic_name__            =     "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																														__magic_name__            =     "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																														__magic_name__            =     "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/o/kernel"   ):
																														__magic_name__            =     "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																														__magic_name__            =     (
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/an"   ):
																								__magic_name__            =     int(key_name[8:].split("/"   )[0]   )
																								if key_name.endswith("/b"   ):
																														__magic_name__            =     "model.blocks.%d.self_attn.norm.bias" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								elif key_name.endswith("/g"   ):
																														__magic_name__            =     "model.blocks.%d.self_attn.norm.weight" % player
																														__magic_name__            =     vnp.copy()  # same because it is one dimensional
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif (
																		    key_name.startswith("model/wte"   )
																		    or key_name.startswith("model/wpe"   )
																		    or key_name.startswith("model/ete"   )
																		):
																								__magic_name__            =     {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																								    key_name[-3:]
																								]
																								__magic_name__            =     "model.%s.weight" % nlayer
																								__magic_name__            =     vnp.copy()  # same in embedded
																								__magic_name__            =     torch.tensor(__lowerCamelCase   )
																								if key_name.startswith("model/wte"   ):
																														__magic_name__            =     "lm_head.weight"
																														__magic_name__            =     vnp.copy()  # same in embedded
																														__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name.startswith("model/wob"   ):
																								__magic_name__            =     "final_logits_bias"
																								__magic_name__            =     vnp.copy()  # same in embedded
																								__magic_name__            =     state.reshape((1, -1)   )
																								__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name == "model/dense/kernel":
																								__magic_name__            =     "model.last_project.weight"
																								__magic_name__            =     vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								__magic_name__            =     torch.tensor(__lowerCamelCase   )
																		elif key_name == "model/dense_1/bias":
																								__magic_name__            =     "model.last_project.bias"
																								__magic_name__            =     vnp.copy()  # same because it is one dimensional
																								__magic_name__            =     torch.tensor(__lowerCamelCase   )
						torch.save(__lowerCamelCase ,						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 typing import TYPE_CHECKING

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


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowercase    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



