
'''simple docstring'''






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_       :   str   =       'RegNetConfig'

# Base docstring
UpperCAmelCase_       :   Tuple   =       'facebook/regnet-y-040'
UpperCAmelCase_       :   Optional[int]   =       [1, 1088, 7, 7]

# Image classification docstring
UpperCAmelCase_       :   Dict   =       'facebook/regnet-y-040'
UpperCAmelCase_       :   Tuple   =       'tabby, tabby cat'

UpperCAmelCase_       :   Optional[Any]   =       [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Any,__A      :       int,__A      :       int = 3,__A      :       int = 1,__A      :       int = 1,__A      :       Optional[str] = "relu",**__A      :       str,):
          super().__init__(**__A     )
          # The padding and conv has been verified in
          # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
          _lowerCamelCase				:   Optional[int]								=   tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2     )
          _lowerCamelCase				:   str								=   tf.keras.layers.ConvaD(
              filters=__A,kernel_size=__A,strides=__A,padding="VALID",groups=__A,use_bias=__A,name="convolution",)
          _lowerCamelCase				:   List[str]								=   tf.keras.layers.BatchNormalization(epsilon=1e-5,momentum=0.9,name="normalization"     )
          _lowerCamelCase				:   Tuple								=   ACTaFN[activation] if activation is not None else tf.identity



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[int]     ):
          _lowerCamelCase				:   int								=   self.convolution(self.padding(__A     )     )
          _lowerCamelCase				:   List[str]								=   self.normalization(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Tuple,__A      :       RegNetConfig,**__A      :       List[Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Tuple								=   config.num_channels
          _lowerCamelCase				:   int								=   TFRegNetConvLayer(
              out_channels=config.embedding_size,kernel_size=3,stride=2,activation=config.hidden_act,name="embedder",)



    def   lowerCamelCase_  (	self      :       Dict,__A      :       str     ):
          _lowerCamelCase				:   List[str]								=   shape_list(__A     )[1]
          if tf.executing_eagerly() and 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."     )

          # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
          # So change the input format from `NCHW` to `NHWC`.
          # shape = (batch_size, in_height, in_width, in_channels=num_channels)
          _lowerCamelCase				:   str								=   tf.transpose(__A,perm=(0, 2, 3, 1)     )
          _lowerCamelCase				:   Optional[int]								=   self.embedder(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       int,__A      :       int = 2,**__A      :       List[str]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[str]								=   tf.keras.layers.ConvaD(
              filters=__A,kernel_size=1,strides=__A,use_bias=__A,name="convolution"     )
          _lowerCamelCase				:   int								=   tf.keras.layers.BatchNormalization(epsilon=1e-5,momentum=0.9,name="normalization"     )



    def   lowerCamelCase_  (	self      :       str,__A      :       tf.Tensor,__A      :       bool = False     ):
          return self.normalization(self.convolution(__A     ),training=__A     )


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       int,__A      :       int,**__A      :       Optional[Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[Any]								=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A,name="pooler"     )
          _lowerCamelCase				:   Optional[Any]								=   [
              tf.keras.layers.ConvaD(filters=__A,kernel_size=1,activation="relu",name="attention.0"     ),
              tf.keras.layers.ConvaD(filters=__A,kernel_size=1,activation="sigmoid",name="attention.2"     ),
          ]



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
          _lowerCamelCase				:   Union[str, Any]								=   self.pooler(__A     )
          for layer_module in self.attention:
                _lowerCamelCase				:   List[Any]								=   layer_module(__A     )
          _lowerCamelCase				:   List[str]								=   hidden_state * pooled
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Any,__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 1,**__A      :       str     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Dict								=   in_channels != out_channels or stride != 1
          _lowerCamelCase				:   Dict								=   max(1,out_channels // config.groups_width     )
          _lowerCamelCase				:   Any								=   (
              TFRegNetShortCut(__A,stride=__A,name="shortcut"     )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear",name="shortcut"     )
          )
          # `self.layers` instead of `self.layer` because that is a reserved argument.
          _lowerCamelCase				:   Optional[int]								=   [
              TFRegNetConvLayer(__A,kernel_size=1,activation=config.hidden_act,name="layer.0"     ),
              TFRegNetConvLayer(
                  __A,stride=__A,groups=__A,activation=config.hidden_act,name="layer.1"     ),
              TFRegNetConvLayer(__A,kernel_size=1,activation=__A,name="layer.2"     ),
          ]
          _lowerCamelCase				:   Union[str, Any]								=   ACTaFN[config.hidden_act]



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   hidden_state
          for layer_module in self.layers:
                _lowerCamelCase				:   int								=   layer_module(__A     )
          _lowerCamelCase				:   int								=   self.shortcut(__A     )
          hidden_state += residual
          _lowerCamelCase				:   int								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 1,**__A      :       List[str]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   str								=   in_channels != out_channels or stride != 1
          _lowerCamelCase				:   str								=   max(1,out_channels // config.groups_width     )
          _lowerCamelCase				:   Optional[Any]								=   (
              TFRegNetShortCut(__A,stride=__A,name="shortcut"     )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear",name="shortcut"     )
          )
          _lowerCamelCase				:   Dict								=   [
              TFRegNetConvLayer(__A,kernel_size=1,activation=config.hidden_act,name="layer.0"     ),
              TFRegNetConvLayer(
                  __A,stride=__A,groups=__A,activation=config.hidden_act,name="layer.1"     ),
              TFRegNetSELayer(__A,reduced_channels=int(round(in_channels / 4     )     ),name="layer.2"     ),
              TFRegNetConvLayer(__A,kernel_size=1,activation=__A,name="layer.3"     ),
          ]
          _lowerCamelCase				:   Any								=   ACTaFN[config.hidden_act]



    def   lowerCamelCase_  (	self      :       str,__A      :       str     ):
          _lowerCamelCase				:   Any								=   hidden_state
          for layer_module in self.layers:
                _lowerCamelCase				:   Optional[int]								=   layer_module(__A     )
          _lowerCamelCase				:   Optional[Any]								=   self.shortcut(__A     )
          hidden_state += residual
          _lowerCamelCase				:   Optional[int]								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       str,__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 2,__A      :       int = 2,**__A      :       Dict     ):
          super().__init__(**__A     )

          _lowerCamelCase				:   List[str]								=   TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
          _lowerCamelCase				:   int								=   [
              # downsampling is done in the first layer with stride of 2
              layer(__A,__A,__A,stride=__A,name="layers.0"     ),
              *[layer(__A,__A,__A,name=f'layers.{i+1}'     ) for i in range(depth - 1     )],
          ]



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Dict     ):
          for layer_module in self.layers:
                _lowerCamelCase				:   str								=   layer_module(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       str,__A      :       RegNetConfig,**__A      :       Union[str, Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Union[str, Any]								=   []
          # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
          self.stages.append(
              TFRegNetStage(
                  __A,config.embedding_size,config.hidden_sizes[0],stride=2 if config.downsample_in_first_stage else 1,depth=config.depths[0],name="stages.0",)     )
          _lowerCamelCase				:   str								=   zip(config.hidden_sizes,config.hidden_sizes[1:]     )
          for i, ((in_channels, out_channels), depth) in enumerate(zip(__A,config.depths[1:]     )     ):
                self.stages.append(TFRegNetStage(__A,__A,__A,depth=__A,name=f'stages.{i+1}'     )     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       tf.Tensor,__A      :       bool = False,__A      :       bool = True     ):
          _lowerCamelCase				:   Dict								=   () if output_hidden_states else None

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

                _lowerCamelCase				:   List[Any]								=   stage_module(__A     )

          if output_hidden_states:
                _lowerCamelCase				:   List[str]								=   hidden_states + (hidden_state,)

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

          return TFBaseModelOutputWithNoAttention(last_hidden_state=__A,hidden_states=__A     )


@keras_serializable
class  UpperCAmelCase__						(       tf.keras.layers.Layer ):
    lowerCAmelCase_				    =		RegNetConfig



    def __init__(	self      :       Dict,__A      :       List[str],**__A      :       Union[str, Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[Any]								=   config
          _lowerCamelCase				:   str								=   TFRegNetEmbeddings(__A,name="embedder"     )
          _lowerCamelCase				:   str								=   TFRegNetEncoder(__A,name="encoder"     )
          _lowerCamelCase				:   Optional[Any]								=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A,name="pooler"     )



    @unpack_inputs
    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       tf.Tensor,__A      :       Optional[bool] = None,__A      :       Optional[bool] = None,__A      :       bool = False,):
          _lowerCamelCase				:   str								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   Optional[int]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   Union[str, Any]								=   self.embedder(__A,training=__A     )

          _lowerCamelCase				:   Any								=   self.encoder(
              __A,output_hidden_states=__A,return_dict=__A,training=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   encoder_outputs[0]
          _lowerCamelCase				:   str								=   self.pooler(__A     )

          # Change to NCHW output format have uniformity in the modules
          _lowerCamelCase				:   Optional[Any]								=   tf.transpose(__A,perm=(0, 3, 1, 2)     )
          _lowerCamelCase				:   str								=   tf.transpose(__A,perm=(0, 3, 1, 2)     )

          # Change the other hidden state outputs to NCHW as well
          if output_hidden_states:
                _lowerCamelCase				:   Tuple								=   tuple([tf.transpose(__A,perm=(0, 3, 1, 2)     ) for h in encoder_outputs[1]]     )

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

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=__A,pooler_output=__A,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,)


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		RegNetConfig
    lowerCAmelCase_				    =		'regnet'
    lowerCAmelCase_				    =		'pixel_values'



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4),dtype=tf.floataa     )}


UpperCAmelCase_       :   str   =       R'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCAmelCase_       :   int   =       R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\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 RegNet model outputting raw features without any specific head on top.'						,	A						,	)
class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       Optional[Any],__A      :       RegNetConfig,*__A      :       int,**__A      :       Optional[Any]     ):
          super().__init__(__A,*__A,**__A     )
          _lowerCamelCase				:   Union[str, Any]								=   TFRegNetMainLayer(__A,name="regnet"     )



    @unpack_inputs
    @add_start_docstrings_to_model_forward(__A     )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,output_type=__A,config_class=_CONFIG_FOR_DOC,modality="vision",expected_output=_EXPECTED_OUTPUT_SHAPE,)
    def   lowerCamelCase_  (	self      :       str,__A      :       tf.Tensor,__A      :       Optional[bool] = None,__A      :       Optional[bool] = None,__A      :       Optional[int]=False,):
          _lowerCamelCase				:   str								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   List[Any]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   str								=   self.regnet(
              pixel_values=__A,output_hidden_states=__A,return_dict=__A,training=__A,)
          if not return_dict:
                return (outputs[0],) + outputs[1:]

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=outputs.last_hidden_state,pooler_output=outputs.pooler_output,hidden_states=outputs.hidden_states,)


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



    def __init__(	self      :       Union[str, Any],__A      :       RegNetConfig,*__A      :       Optional[int],**__A      :       Tuple     ):
          super().__init__(__A,*__A,**__A     )
          _lowerCamelCase				:   List[Any]								=   config.num_labels
          _lowerCamelCase				:   Union[str, Any]								=   TFRegNetMainLayer(__A,name="regnet"     )
          # classification head
          _lowerCamelCase				:   Dict								=   [
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(config.num_labels,name="classifier.1"     ) if config.num_labels > 0 else tf.identity,
          ]



    @unpack_inputs
    @add_start_docstrings_to_model_forward(__A     )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,output_type=__A,config_class=_CONFIG_FOR_DOC,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,)
    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       tf.Tensor = None,__A      :       tf.Tensor = None,__A      :       bool = None,__A      :       bool = None,__A      :       Union[str, Any]=False,):
          _lowerCamelCase				:   Dict								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   Optional[Any]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   Tuple								=   self.regnet(
              __A,output_hidden_states=__A,return_dict=__A,training=__A     )

          _lowerCamelCase				:   Optional[int]								=   outputs.pooler_output if return_dict else outputs[1]

          _lowerCamelCase				:   int								=   self.classifier[0](__A     )
          _lowerCamelCase				:   Optional[int]								=   self.classifier[1](__A     )

          _lowerCamelCase				:   List[str]								=   None if labels is None else self.hf_compute_loss(labels=__A,logits=__A     )

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

          return TFSequenceClassifierOutput(loss=__A,logits=__A,hidden_states=outputs.hidden_states     )

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

      # pop unnecessary weights
      _lowerCamelCase				:   Optional[Any]								=   [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Union[str, Any]								=   {
          "decoder.project_in_dim.weight": "decoder.project_in.weight",
          "decoder.project_out_dim.weight": "decoder.project_out.weight",
          "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
          "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Any								=   list(sd.keys()							)
      for key in keys:
            if ".qkv_proj." in key:
                  _lowerCamelCase				:   Optional[Any]								=   sd[key]
                  # We split QKV in separate Q,K,V

                  _lowerCamelCase				:   int								=   key.replace(".qkv_proj."				,	".q_proj."							)
                  _lowerCamelCase				:   List[Any]								=   key.replace(".qkv_proj."				,	".k_proj."							)
                  _lowerCamelCase				:   Dict								=   key.replace(".qkv_proj."				,	".v_proj."							)

                  _lowerCamelCase				:   Any								=   value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

                  _lowerCamelCase				:   str								=   q
                  _lowerCamelCase				:   Dict								=   k
                  _lowerCamelCase				:   int								=   v
                  del sd[key]

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

      if config is not None:
            _lowerCamelCase				:   str								=   OPTConfig.from_pretrained(_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Any								=   OPTConfig()

      _lowerCamelCase				:   Optional[int]								=   OPTModel(_lowerCAmelCase							).half().eval()
      model.load_state_dict(_lowerCAmelCase							)

      # Check results
      Path(_lowerCAmelCase							).mkdir(exist_ok=_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   List[str]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fairseq_path',
     type=str,
     help=(
         'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
         ' https://huggingface.co/models?other=opt_metasq'
     ),
 )
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)