from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


UpperCamelCase__											=					logging.get_logger(__name__)

# General docstring
UpperCamelCase__											=					'ResNetConfig'

# Base docstring
UpperCamelCase__											=					'microsoft/resnet-50'
UpperCamelCase__											=					[1, 2_0_4_8, 7, 7]

# Image classification docstring
UpperCamelCase__											=					'microsoft/resnet-50'
UpperCamelCase__											=					'tiger cat'

UpperCamelCase__											=					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class      A							(				nn.Module     ):


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int = 3       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						str = "relu"		)						->				int:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  nn.Convad(
             __UpperCAmelCase       ,  __UpperCAmelCase       ,  kernel_size=__UpperCAmelCase       ,  stride=__UpperCAmelCase       ,  padding=kernel_size // 2       ,  bias=__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.BatchNormad(__UpperCAmelCase		)
         UpperCAmelCase__								=  ACTaFN[activation] if activation is not None else nn.Identity()





   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Tensor		)						->				Tensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.convolution(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.normalization(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.activation(__UpperCAmelCase		)
         return hidden_state





class      A							(				nn.Module     ):


   def __init__(self     :						str       ,  __UpperCAmelCase     :						ResNetConfig		)						->				Any:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  ResNetConvLayer(
             config.num_channels       ,  config.embedding_size       ,  kernel_size=7       ,  stride=2       ,  activation=config.hidden_act		)
         UpperCAmelCase__								=  nn.MaxPoolad(kernel_size=3       ,  stride=2       ,  padding=1		)
         UpperCAmelCase__								=  config.num_channels





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tensor		)						->				Tensor:
         """simple docstring"""



         UpperCAmelCase__								=  pixel_values.shape[1]
         if num_channels != self.num_channels:
               raise ValueError(
                   "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)
         UpperCAmelCase__								=  self.embedder(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.pooler(__UpperCAmelCase		)
         return embedding





class      A							(				nn.Module     ):


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int = 2		)						->				Union[str, Any]:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  nn.Convad(__UpperCAmelCase       ,  __UpperCAmelCase       ,  kernel_size=1       ,  stride=__UpperCAmelCase       ,  bias=__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.BatchNormad(__UpperCAmelCase		)





   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Tensor		)						->				Tensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.convolution(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.normalization(__UpperCAmelCase		)
         return hidden_state





class      A							(				nn.Module     ):


   def __init__(self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						str = "relu"		)						->				Dict:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  in_channels != out_channels or stride != 1
         UpperCAmelCase__								=  (
             ResNetShortCut(__UpperCAmelCase       ,  __UpperCAmelCase       ,  stride=__UpperCAmelCase		) if should_apply_shortcut else nn.Identity()
         )
         UpperCAmelCase__								=  nn.Sequential(
             ResNetConvLayer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  stride=__UpperCAmelCase		)       ,  ResNetConvLayer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  activation=__UpperCAmelCase		)       ,  )
         UpperCAmelCase__								=  ACTaFN[activation]





   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  hidden_state
         UpperCAmelCase__								=  self.layer(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.shortcut(__UpperCAmelCase		)
         hidden_state += residual
         UpperCAmelCase__								=  self.activation(__UpperCAmelCase		)
         return hidden_state





class      A							(				nn.Module     ):


   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						str = "relu"       ,  __UpperCAmelCase     :						int = 4		)						->				Optional[Any]:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  in_channels != out_channels or stride != 1
         UpperCAmelCase__								=  out_channels // reduction
         UpperCAmelCase__								=  (
             ResNetShortCut(__UpperCAmelCase       ,  __UpperCAmelCase       ,  stride=__UpperCAmelCase		) if should_apply_shortcut else nn.Identity()
         )
         UpperCAmelCase__								=  nn.Sequential(
             ResNetConvLayer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  kernel_size=1		)       ,  ResNetConvLayer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  stride=__UpperCAmelCase		)       ,  ResNetConvLayer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  kernel_size=1       ,  activation=__UpperCAmelCase		)       ,  )
         UpperCAmelCase__								=  ACTaFN[activation]





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  hidden_state
         UpperCAmelCase__								=  self.layer(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.shortcut(__UpperCAmelCase		)
         hidden_state += residual
         UpperCAmelCase__								=  self.activation(__UpperCAmelCase		)
         return hidden_state





class      A							(				nn.Module     ):


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						ResNetConfig       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 2       ,  )						->				Dict:
         """simple docstring"""



         super().__init__()

         UpperCAmelCase__								=  ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

         UpperCAmelCase__								=  nn.Sequential(
             # downsampling is done in the first layer with stride of 2
             layer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  stride=__UpperCAmelCase       ,  activation=config.hidden_act		)       ,  *[layer(__UpperCAmelCase       ,  __UpperCAmelCase       ,  activation=config.hidden_act		) for _ in range(depth - 1		)]       ,  )





   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Tensor		)						->				Tensor:
         """simple docstring"""



         UpperCAmelCase__								=  input
         for layer in self.layers:
               UpperCAmelCase__								=  layer(__UpperCAmelCase		)
         return hidden_state





class      A							(				nn.Module     ):


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						ResNetConfig		)						->				Union[str, Any]:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__								=  nn.ModuleList([]		)
         # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
         self.stages.append(
             ResNetStage(
                 __UpperCAmelCase       ,  config.embedding_size       ,  config.hidden_sizes[0]       ,  stride=2 if config.downsample_in_first_stage else 1       ,  depth=config.depths[0]       ,  )		)
         UpperCAmelCase__								=  zip(config.hidden_sizes       ,  config.hidden_sizes[1:]		)
         for (in_channels, out_channels), depth in zip(__UpperCAmelCase       ,  config.depths[1:]		):
               self.stages.append(ResNetStage(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  depth=__UpperCAmelCase		)		)





   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Tensor       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						bool = True		)						->				BaseModelOutputWithNoAttention:
         """simple docstring"""



         UpperCAmelCase__								=  () if output_hidden_states else None

         for stage_module in self.stages:
               if output_hidden_states:
                     UpperCAmelCase__								=  hidden_states + (hidden_state,)

               UpperCAmelCase__								=  stage_module(__UpperCAmelCase		)

         if output_hidden_states:
               UpperCAmelCase__								=  hidden_states + (hidden_state,)

         if not return_dict:
               return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

         return BaseModelOutputWithNoAttention(
             last_hidden_state=__UpperCAmelCase       ,  hidden_states=__UpperCAmelCase       ,  )





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			ResNetConfig
   __UpperCAmelCase : List[Any]      =			'resnet'
   __UpperCAmelCase : List[str]      =			'pixel_values'
   __UpperCAmelCase : int      =			True


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int		)						->				Any:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  nn.Convad		):
               nn.init.kaiming_normal_(module.weight       ,  mode="fan_out"       ,  nonlinearity="relu"		)
         elif isinstance(__UpperCAmelCase       ,  (nn.BatchNormad, nn.GroupNorm)		):
               nn.init.constant_(module.weight       ,  1		)
               nn.init.constant_(module.bias       ,  0		)





   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple=False		)						->				Optional[int]:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               UpperCAmelCase__								=  value




UpperCamelCase__											=					R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCamelCase__											=					R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'		,				UpperCAmelCase_		,				)
class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						str       ,  __UpperCAmelCase     :						Optional[Any]		)						->				List[Any]:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  config
         UpperCAmelCase__								=  ResNetEmbeddings(__UpperCAmelCase		)
         UpperCAmelCase__								=  ResNetEncoder(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.AdaptiveAvgPoolad((1, 1)		)
         # Initialize weights and apply final processing
         self.post_init()





   @add_start_docstrings_to_model_forward(__UpperCAmelCase		)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC       ,  output_type=__UpperCAmelCase       ,  config_class=_CONFIG_FOR_DOC       ,  modality="vision"       ,  expected_output=_EXPECTED_OUTPUT_SHAPE       ,  )
   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Tensor       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None		)						->				BaseModelOutputWithPoolingAndNoAttention:
         """simple docstring"""



         UpperCAmelCase__								=  (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.embedder(__UpperCAmelCase		)

         UpperCAmelCase__								=  self.encoder(
             __UpperCAmelCase       ,  output_hidden_states=__UpperCAmelCase       ,  return_dict=__UpperCAmelCase		)

         UpperCAmelCase__								=  encoder_outputs[0]

         UpperCAmelCase__								=  self.pooler(__UpperCAmelCase		)

         if not return_dict:
               return (last_hidden_state, pooled_output) + encoder_outputs[1:]

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__UpperCAmelCase       ,  pooler_output=__UpperCAmelCase       ,  hidden_states=encoder_outputs.hidden_states       ,  )





@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '		,				UpperCAmelCase_		,				)
class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				List[str]:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  config.num_labels
         UpperCAmelCase__								=  ResNetModel(__UpperCAmelCase		)
         # classification head
         UpperCAmelCase__								=  nn.Sequential(
             nn.Flatten()       ,  nn.Linear(config.hidden_sizes[-1]       ,  config.num_labels		) if config.num_labels > 0 else nn.Identity()       ,  )
         # initialize weights and apply final processing
         self.post_init()





   @add_start_docstrings_to_model_forward(__UpperCAmelCase		)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT       ,  output_type=__UpperCAmelCase       ,  config_class=_CONFIG_FOR_DOC       ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,  )
   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Optional[torch.FloatTensor] = None       ,  __UpperCAmelCase     :						Optional[torch.LongTensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				ImageClassifierOutputWithNoAttention:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.resnet(__UpperCAmelCase       ,  output_hidden_states=__UpperCAmelCase       ,  return_dict=__UpperCAmelCase		)

         UpperCAmelCase__								=  outputs.pooler_output if return_dict else outputs[1]

         UpperCAmelCase__								=  self.classifier(__UpperCAmelCase		)

         UpperCAmelCase__								=  None

         if labels is not None:
               if self.config.problem_type is None:
                     if self.num_labels == 1:
                           UpperCAmelCase__								=  "regression"
                     elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                           UpperCAmelCase__								=  "single_label_classification"
                     else:
                           UpperCAmelCase__								=  "multi_label_classification"
               if self.config.problem_type == "regression":
                     UpperCAmelCase__								=  MSELoss()
                     if self.num_labels == 1:
                           UpperCAmelCase__								=  loss_fct(logits.squeeze()       ,  labels.squeeze()		)
                     else:
                           UpperCAmelCase__								=  loss_fct(__UpperCAmelCase       ,  __UpperCAmelCase		)
               elif self.config.problem_type == "single_label_classification":
                     UpperCAmelCase__								=  CrossEntropyLoss()
                     UpperCAmelCase__								=  loss_fct(logits.view(-1       ,  self.num_labels		)       ,  labels.view(-1		)		)
               elif self.config.problem_type == "multi_label_classification":
                     UpperCAmelCase__								=  BCEWithLogitsLoss()
                     UpperCAmelCase__								=  loss_fct(__UpperCAmelCase       ,  __UpperCAmelCase		)

         if not return_dict:
               UpperCAmelCase__								=  (logits,) + outputs[2:]
               return (loss,) + output if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase       ,  logits=__UpperCAmelCase       ,  hidden_states=outputs.hidden_states		)





@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '		,				UpperCAmelCase_		,				)
class      A							(				UpperCAmelCase_		,				UpperCAmelCase_     ):


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Any		)						->				str:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         super()._init_backbone(__UpperCAmelCase		)

         UpperCAmelCase__								=  [config.embedding_size] + config.hidden_sizes
         UpperCAmelCase__								=  ResNetEmbeddings(__UpperCAmelCase		)
         UpperCAmelCase__								=  ResNetEncoder(__UpperCAmelCase		)

         # initialize weights and apply final processing
         self.post_init()





   @add_start_docstrings_to_model_forward(__UpperCAmelCase		)
   @replace_return_docstrings(output_type=__UpperCAmelCase       ,  config_class=_CONFIG_FOR_DOC		)
   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Tensor       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None		)						->				BackboneOutput:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict
         UpperCAmelCase__								=  (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         UpperCAmelCase__								=  self.embedder(__UpperCAmelCase		)

         UpperCAmelCase__								=  self.encoder(__UpperCAmelCase       ,  output_hidden_states=__UpperCAmelCase       ,  return_dict=__UpperCAmelCase		)

         UpperCAmelCase__								=  outputs.hidden_states

         UpperCAmelCase__								=  ()
         for idx, stage in enumerate(self.stage_names		):
               if stage in self.out_features:
                     feature_maps += (hidden_states[idx],)

         if not return_dict:
               UpperCAmelCase__								=  (feature_maps,)
               if output_hidden_states:
                     output += (outputs.hidden_states,)
               return output

         return BackboneOutput(
             feature_maps=__UpperCAmelCase       ,  hidden_states=outputs.hidden_states if output_hidden_states else None       ,  attentions=__UpperCAmelCase       ,  )








"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


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


if __name__ == "__main__":
						__a      =				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")
						__a      =				parser.parse_args()
						convert_tf_gptsan_to_pt(args)
