



"""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_		:		List[str]):





				'''simple docstring'''

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


if __name__ == "__main__":
							__snake_case    :		Dict  =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')
							__snake_case    :		List[Any]  =parser.parse_args()
							convert_tf_gptsan_to_pt(args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

