


'''simple docstring'''







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


a__    :   Tuple								=			logging.get_logger(__name__)

# General docstring
a__    :   Union[str, Any]								=			'ResNetConfig'

# Base docstring
a__    :   Tuple								=			'microsoft/resnet-50'
a__    :   str								=			[1, 2_0_4_8, 7, 7]

# Image classification docstring
a__    :   int								=			'microsoft/resnet-50'
a__    :   int								=			'tiger cat'

a__    :   List[str]								=			[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class    lowercase_	(       tf.keras.layers.Layer  ):


    def __init__(	self    ,   a    ,   a = 3    ,   a = 1    ,   a = "relu"    ,   **a       ):
          super().__init__(**a       )
          UpperCamelCase__      =							kernel_size // 2
          UpperCamelCase__      =							tf.keras.layers.ConvaD(
              a    ,   kernel_size=a    ,   strides=a    ,   padding="valid"    ,   use_bias=a    ,   name="convolution"       )
          # Use same default momentum and epsilon as PyTorch equivalent
          UpperCamelCase__      =							tf.keras.layers.BatchNormalization(epsilon=1e-5    ,   momentum=0.9    ,   name="normalization"       )
          UpperCamelCase__      =							ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"       )


    def   __a				(	self    ,   a       ):
          # Pad to match that done in the PyTorch Conv2D model
          UpperCamelCase__      =							UpperCamelCase__      =							(self.pad_value, self.pad_value)
          UpperCamelCase__      =							tf.pad(a    ,   [(0, 0), height_pad, width_pad, (0, 0)]       )
          UpperCamelCase__      =							self.conv(a       )
          return hidden_state


    def   __a				(	self    ,   a    ,   a = False       ):
          UpperCamelCase__      =							self.convolution(a       )
          UpperCamelCase__      =							self.normalization(a    ,   training=a       )
          UpperCamelCase__      =							self.activation(a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   **a       ):
          super().__init__(**a       )
          UpperCamelCase__      =							TFResNetConvLayer(
              config.embedding_size    ,   kernel_size=7    ,   stride=2    ,   activation=config.hidden_act    ,   name="embedder"    ,   )
          UpperCamelCase__      =							tf.keras.layers.MaxPoolaD(pool_size=3    ,   strides=2    ,   padding="valid"    ,   name="pooler"       )
          UpperCamelCase__      =							config.num_channels


    def   __a				(	self    ,   a    ,   a = False       ):
          UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							shape_list(a       )
          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."       )
          UpperCamelCase__      =							pixel_values
          UpperCamelCase__      =							self.embedder(a       )
          UpperCamelCase__      =							tf.pad(a    ,   [[0, 0], [1, 1], [1, 1], [0, 0]]       )
          UpperCamelCase__      =							self.pooler(a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   a = 2    ,   **a       ):
          super().__init__(**a       )
          UpperCamelCase__      =							tf.keras.layers.ConvaD(
              a    ,   kernel_size=1    ,   strides=a    ,   use_bias=a    ,   name="convolution"       )
          # Use same default momentum and epsilon as PyTorch equivalent
          UpperCamelCase__      =							tf.keras.layers.BatchNormalization(epsilon=1e-5    ,   momentum=0.9    ,   name="normalization"       )


    def   __a				(	self    ,   a    ,   a = False       ):
          UpperCamelCase__      =							x
          UpperCamelCase__      =							self.convolution(a       )
          UpperCamelCase__      =							self.normalization(a    ,   training=a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   a    ,   a = 1    ,   a = "relu"    ,   **a       ):
          super().__init__(**a       )
          UpperCamelCase__      =							in_channels != out_channels or stride != 1
          UpperCamelCase__      =							TFResNetConvLayer(a    ,   stride=a    ,   name="layer.0"       )
          UpperCamelCase__      =							TFResNetConvLayer(a    ,   activation=a    ,   name="layer.1"       )
          UpperCamelCase__      =							(
              TFResNetShortCut(a    ,   stride=a    ,   name="shortcut"       )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"    ,   name="shortcut"       )
          )
          UpperCamelCase__      =							ACTaFN[activation]


    def   __a				(	self    ,   a    ,   a = False       ):
          UpperCamelCase__      =							hidden_state
          UpperCamelCase__      =							self.conva(a    ,   training=a       )
          UpperCamelCase__      =							self.conva(a    ,   training=a       )
          UpperCamelCase__      =							self.shortcut(a    ,   training=a       )
          hidden_state += residual
          UpperCamelCase__      =							self.activation(a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   a    ,   a = 1    ,   a = "relu"    ,   a = 4    ,   **a    ,   ):
          super().__init__(**a       )
          UpperCamelCase__      =							in_channels != out_channels or stride != 1
          UpperCamelCase__      =							out_channels // reduction
          UpperCamelCase__      =							TFResNetConvLayer(a    ,   kernel_size=1    ,   name="layer.0"       )
          UpperCamelCase__      =							TFResNetConvLayer(a    ,   stride=a    ,   name="layer.1"       )
          UpperCamelCase__      =							TFResNetConvLayer(a    ,   kernel_size=1    ,   activation=a    ,   name="layer.2"       )
          UpperCamelCase__      =							(
              TFResNetShortCut(a    ,   stride=a    ,   name="shortcut"       )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"    ,   name="shortcut"       )
          )
          UpperCamelCase__      =							ACTaFN[activation]


    def   __a				(	self    ,   a    ,   a = False       ):
          UpperCamelCase__      =							hidden_state
          UpperCamelCase__      =							self.conva(a    ,   training=a       )
          UpperCamelCase__      =							self.conva(a    ,   training=a       )
          UpperCamelCase__      =							self.conva(a    ,   training=a       )
          UpperCamelCase__      =							self.shortcut(a    ,   training=a       )
          hidden_state += residual
          UpperCamelCase__      =							self.activation(a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   a    ,   a    ,   a = 2    ,   a = 2    ,   **a       ):
          super().__init__(**a       )

          UpperCamelCase__      =							TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

          UpperCamelCase__      =							[layer(a    ,   a    ,   stride=a    ,   activation=config.hidden_act    ,   name="layers.0"       )]
          layers += [
              layer(a    ,   a    ,   activation=config.hidden_act    ,   name=f'''layers.{i + 1}'''       )
              for i in range(depth - 1       )
          ]
          UpperCamelCase__      =							layers


    def   __a				(	self    ,   a    ,   a = False       ):
          for layer in self.stage_layers:
                UpperCamelCase__      =							layer(a    ,   training=a       )
          return hidden_state

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


    def __init__(	self    ,   a    ,   **a       ):
          super().__init__(**a       )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          UpperCamelCase__      =							[
              TFResNetStage(
                  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"    ,   )
          ]
          for i, (in_channels, out_channels, depth) in enumerate(
              zip(config.hidden_sizes    ,   config.hidden_sizes[1:]    ,   config.depths[1:]       )       ):
                self.stages.append(TFResNetStage(a    ,   a    ,   a    ,   depth=a    ,   name=f'''stages.{i + 1}'''       )       )


    def   __a				(	self    ,   a    ,   a = False    ,   a = True    ,   a = False    ,   ):
          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(a    ,   training=a       )

          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 TFBaseModelOutputWithNoAttention(last_hidden_state=a    ,   hidden_states=a       )

class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      ResNetConfig
    __UpperCAmelCase       =      'resnet'
    __UpperCAmelCase       =      'pixel_values'


    @property
    def   __a				(	self       ):
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)    ,   dtype=tf.floataa       )}

a__    :   List[str]								=			R'\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\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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


a__    :   Tuple								=			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            [`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'
@keras_serializable
class    lowercase_	(       tf.keras.layers.Layer  ):
    __UpperCAmelCase       =      ResNetConfig


    def __init__(	self    ,   a    ,   **a       ):
          super().__init__(**a       )
          UpperCamelCase__      =							config
          UpperCamelCase__      =							TFResNetEmbeddings(a    ,   name="embedder"       )
          UpperCamelCase__      =							TFResNetEncoder(a    ,   name="encoder"       )
          UpperCamelCase__      =							tf.keras.layers.GlobalAveragePoolingaD(keepdims=a       )


    @unpack_inputs
    def   __a				(	self    ,   a    ,   a = None    ,   a = None    ,   a = False    ,   ):
          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

          # TF 2.0 image layers can't use NCHW format when running on CPU.
          # We transpose to NHWC format and then transpose back after the full forward pass.
          # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
          UpperCamelCase__      =							tf.transpose(a    ,   perm=[0, 2, 3, 1]       )
          UpperCamelCase__      =							self.embedder(a    ,   training=a       )

          UpperCamelCase__      =							self.encoder(
              a    ,   output_hidden_states=a    ,   return_dict=a    ,   training=a       )

          UpperCamelCase__      =							encoder_outputs[0]

          UpperCamelCase__      =							self.pooler(a       )

          # Transpose all the outputs to the NCHW format
          # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
          UpperCamelCase__      =							tf.transpose(a    ,   (0, 3, 1, 2)       )
          UpperCamelCase__      =							tf.transpose(a    ,   (0, 3, 1, 2)       )
          UpperCamelCase__      =							()
          for hidden_state in encoder_outputs[1:]:
                UpperCamelCase__      =							hidden_states + tuple(tf.transpose(a    ,   (0, 3, 1, 2)       ) for h in hidden_state       )

          if not return_dict:
                return (last_hidden_state, pooled_output) + hidden_states

          UpperCamelCase__      =							hidden_states if output_hidden_states else None

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=a    ,   pooler_output=a    ,   hidden_states=a    ,   )

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


    def __init__(	self    ,   a    ,   **a       ):
          super().__init__(a    ,   **a       )
          UpperCamelCase__      =							TFResNetMainLayer(config=a    ,   name="resnet"       )


    @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    ,   )
    @unpack_inputs
    def   __a				(	self    ,   a    ,   a = None    ,   a = None    ,   a = False    ,   ):
          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.resnet(
              pixel_values=a    ,   output_hidden_states=a    ,   return_dict=a    ,   training=a    ,   )
          return resnet_outputs

@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    '    ,		a__    ,		)
class    lowercase_	(       a__    ,		a__  ):


    def __init__(	self    ,   a    ,   **a       ):
          super().__init__(a    ,   **a       )
          UpperCamelCase__      =							config.num_labels
          UpperCamelCase__      =							TFResNetMainLayer(a    ,   name="resnet"       )
          # classification head
          UpperCamelCase__      =							(
              tf.keras.layers.Dense(config.num_labels    ,   name="classifier.1"       )
              if config.num_labels > 0
              else tf.keras.layers.Activation("linear"    ,   name="classifier.1"       )
          )


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							tf.keras.layers.Flatten()(a       )
          UpperCamelCase__      =							self.classifier_layer(a       )
          return logits


    @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    ,   )
    @unpack_inputs
    def   __a				(	self    ,   a = None    ,   a = None    ,   a = None    ,   a = None    ,   a = False    ,   ):
          UpperCamelCase__      =							return_dict if return_dict is not None else self.config.use_return_dict

          UpperCamelCase__      =							self.resnet(
              a    ,   output_hidden_states=a    ,   return_dict=a    ,   training=a       )

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

          UpperCamelCase__      =							self.classifier(a       )

          UpperCamelCase__      =							None if labels is None else self.hf_compute_loss(a    ,   a       )

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

          return TFImageClassifierOutputWithNoAttention(loss=a    ,   logits=a    ,   hidden_states=outputs.hidden_states       )









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





