'''simple docstring'''




from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__lowercase        =    logging.get_logger(__name__)


__lowercase        =    OrderedDict(
    [
        # Base model mapping
        ('''albert''', '''FlaxAlbertModel'''),
        ('''bart''', '''FlaxBartModel'''),
        ('''beit''', '''FlaxBeitModel'''),
        ('''bert''', '''FlaxBertModel'''),
        ('''big_bird''', '''FlaxBigBirdModel'''),
        ('''blenderbot''', '''FlaxBlenderbotModel'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
        ('''clip''', '''FlaxCLIPModel'''),
        ('''distilbert''', '''FlaxDistilBertModel'''),
        ('''electra''', '''FlaxElectraModel'''),
        ('''gpt-sw3''', '''FlaxGPT2Model'''),
        ('''gpt2''', '''FlaxGPT2Model'''),
        ('''gpt_neo''', '''FlaxGPTNeoModel'''),
        ('''gptj''', '''FlaxGPTJModel'''),
        ('''longt5''', '''FlaxLongT5Model'''),
        ('''marian''', '''FlaxMarianModel'''),
        ('''mbart''', '''FlaxMBartModel'''),
        ('''mt5''', '''FlaxMT5Model'''),
        ('''opt''', '''FlaxOPTModel'''),
        ('''pegasus''', '''FlaxPegasusModel'''),
        ('''regnet''', '''FlaxRegNetModel'''),
        ('''resnet''', '''FlaxResNetModel'''),
        ('''roberta''', '''FlaxRobertaModel'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
        ('''roformer''', '''FlaxRoFormerModel'''),
        ('''t5''', '''FlaxT5Model'''),
        ('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
        ('''vit''', '''FlaxViTModel'''),
        ('''wav2vec2''', '''FlaxWav2Vec2Model'''),
        ('''whisper''', '''FlaxWhisperModel'''),
        ('''xglm''', '''FlaxXGLMModel'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for pre-training mapping
        ('''albert''', '''FlaxAlbertForPreTraining'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForPreTraining'''),
        ('''big_bird''', '''FlaxBigBirdForPreTraining'''),
        ('''electra''', '''FlaxElectraForPreTraining'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
        ('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Masked LM mapping
        ('''albert''', '''FlaxAlbertForMaskedLM'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForMaskedLM'''),
        ('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
        ('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
        ('''electra''', '''FlaxElectraForMaskedLM'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
        ('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''marian''', '''FlaxMarianMTModel'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Image-classsification
        ('''beit''', '''FlaxBeitForImageClassification'''),
        ('''regnet''', '''FlaxRegNetForImageClassification'''),
        ('''resnet''', '''FlaxResNetForImageClassification'''),
        ('''vit''', '''FlaxViTForImageClassification'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Causal LM mapping
        ('''bart''', '''FlaxBartForCausalLM'''),
        ('''bert''', '''FlaxBertForCausalLM'''),
        ('''big_bird''', '''FlaxBigBirdForCausalLM'''),
        ('''electra''', '''FlaxElectraForCausalLM'''),
        ('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt2''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
        ('''gptj''', '''FlaxGPTJForCausalLM'''),
        ('''opt''', '''FlaxOPTForCausalLM'''),
        ('''roberta''', '''FlaxRobertaForCausalLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
        ('''xglm''', '''FlaxXGLMForCausalLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('''albert''', '''FlaxAlbertForSequenceClassification'''),
        ('''bart''', '''FlaxBartForSequenceClassification'''),
        ('''bert''', '''FlaxBertForSequenceClassification'''),
        ('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
        ('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
        ('''electra''', '''FlaxElectraForSequenceClassification'''),
        ('''mbart''', '''FlaxMBartForSequenceClassification'''),
        ('''roberta''', '''FlaxRobertaForSequenceClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
        ('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Question Answering mapping
        ('''albert''', '''FlaxAlbertForQuestionAnswering'''),
        ('''bart''', '''FlaxBartForQuestionAnswering'''),
        ('''bert''', '''FlaxBertForQuestionAnswering'''),
        ('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
        ('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
        ('''electra''', '''FlaxElectraForQuestionAnswering'''),
        ('''mbart''', '''FlaxMBartForQuestionAnswering'''),
        ('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
        ('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Token Classification mapping
        ('''albert''', '''FlaxAlbertForTokenClassification'''),
        ('''bert''', '''FlaxBertForTokenClassification'''),
        ('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
        ('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
        ('''electra''', '''FlaxElectraForTokenClassification'''),
        ('''roberta''', '''FlaxRobertaForTokenClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
        ('''roformer''', '''FlaxRoFormerForTokenClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('''albert''', '''FlaxAlbertForMultipleChoice'''),
        ('''bert''', '''FlaxBertForMultipleChoice'''),
        ('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
        ('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
        ('''electra''', '''FlaxElectraForMultipleChoice'''),
        ('''roberta''', '''FlaxRobertaForMultipleChoice'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
        ('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

__lowercase        =    OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

__lowercase        =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__lowercase        =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__lowercase        =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__lowercase        =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__lowercase        =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			FLAX_MODEL_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModel)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Union[str, Any]						=			FLAX_MODEL_FOR_PRETRAINING_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Tuple						=			FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			FLAX_MODEL_FOR_MASKED_LM_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Any						=			FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	int						=			FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







__lowercase        =    auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')






class 							a__(   _BaseAutoModelClass      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Tuple						=			FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







__lowercase        =    auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)





'''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__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


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




