






"""simple docstring"""



import argparse
import json
import re
import string
import sys

import numpy as np


a  :						Dict					       =							0.5




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Any:
 UpperCAmelCase__ 						=				['.', ',', ';', ':']

 for token in remove_tokens:
  UpperCAmelCase__ 						=				ground_truth.replace(_SCREAMING_SNAKE_CASE       ,	""""""			)
  UpperCAmelCase__ 						=				prediction.replace(_SCREAMING_SNAKE_CASE       ,	""""""			)

 UpperCAmelCase__ 						=				ground_truth.lower(), prediction.lower()
 UpperCAmelCase__ 						=				ground_truth.replace("""/"""       ,	""" """			), prediction.replace("""/"""       ,	""" """			)
 UpperCAmelCase__ 						=				set(ground_truth.split(""" """			)			), set(prediction.split(""" """			)			)

 UpperCAmelCase__ 						=				ground_truth.intersection(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				ground_truth.union(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			) / len(_SCREAMING_SNAKE_CASE			)
 return jaccard




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Dict:

 def remove_articles(_SCREAMING_SNAKE_CASE			):
  return re.sub(r"""\b(a|an|the)\b"""       ,	""" """       ,	_SCREAMING_SNAKE_CASE			)

 def white_space_fix(_SCREAMING_SNAKE_CASE			):
  return " ".join(text.split()			)

 def remove_punc(_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__ 						=				set(string.punctuation			)
  return "".join(ch for ch in text if ch not in exclude			)

 def lower(_SCREAMING_SNAKE_CASE			):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE			)			)			)			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Tuple:
 UpperCAmelCase__ 						=				0, 0, 0

 UpperCAmelCase__ 						=				'Parties' in qa_id

 # first check if ground truth is empty
 if len(_SCREAMING_SNAKE_CASE			) == 0:
  if len(_SCREAMING_SNAKE_CASE			) > 0:
   fp += len(_SCREAMING_SNAKE_CASE			)  # false positive for each one
 else:
  for ground_truth in ground_truths:
   assert len(_SCREAMING_SNAKE_CASE			) > 0
   # check if there is a match
   UpperCAmelCase__ 						=				False
   for pred in predictions:
    if substr_ok:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH or ground_truth in pred
    else:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH
    if is_match:
     UpperCAmelCase__ 						=				True

   if match_found:
    tp += 1
   else:
    fn += 1

        # now also get any fps by looping through preds
  for pred in predictions:
   # Check if there's a match. if so, don't count (don't want to double count based on the above)
   # but if there's no match, then this is a false positive.
   # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
   # multiple predictions that are matched with the same answer.)
   UpperCAmelCase__ 						=				False
   for ground_truth in ground_truths:
    assert len(_SCREAMING_SNAKE_CASE			) > 0
    if substr_ok:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH or ground_truth in pred
    else:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH
    if is_match:
     UpperCAmelCase__ 						=				True

   if not match_found:
    fp += 1

 UpperCAmelCase__ 						=				tp / (tp + fp) if tp + fp > 0 else np.nan
 UpperCAmelCase__ 						=				tp / (tp + fn) if tp + fn > 0 else np.nan

 return precision, recall




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Any]:
 UpperCAmelCase__ 						=				precisions[::-1]
 for i in range(1       ,	len(_SCREAMING_SNAKE_CASE			)			):
  UpperCAmelCase__ 						=				max(precision_best[i - 1]       ,	precision_best[i]			)
 UpperCAmelCase__ 						=				precision_best[::-1]
 return precisions




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Optional[Any]:
 UpperCAmelCase__ 						=				process_precisions(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				np.trapz(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 if np.isnan(_SCREAMING_SNAKE_CASE			):
  return 0
 return aupr




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				process_precisions(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				0
 for prec, recall in zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			):
  if recall >= recall_thresh:
   UpperCAmelCase__ 						=				prec
   break
 return prec_at_recall




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->str:
 return normalize_answer(_SCREAMING_SNAKE_CASE			) == normalize_answer(_SCREAMING_SNAKE_CASE			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				0
 for pred in predictions:
  for ground_truth in ground_truths:
   UpperCAmelCase__ 						=				metric_fn(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
   if score == 1:  # break the loop when one prediction matches the ground truth
    break
  if score == 1:
   break
 return score




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->List[Any]:
 UpperCAmelCase__ 						=				0
 UpperCAmelCase__ 						=				[]
 UpperCAmelCase__ 						=				[]
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     UpperCAmelCase__ 						=				'Unanswered question ' + qa['id'] + ' will receive score 0.'
     print(_SCREAMING_SNAKE_CASE       ,	file=sys.stderr			)
     continue
    UpperCAmelCase__ 						=				[x['text'] for x in qa['answers']]
    UpperCAmelCase__ 						=				predictions[qa['id']]
    UpperCAmelCase__ 						=				compute_precision_recall(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	qa["""id"""]			)

    precisions.append(_SCREAMING_SNAKE_CASE			)
    recalls.append(_SCREAMING_SNAKE_CASE			)

    if precision == 0 and recall == 0:
     fa += 0
    else:
     fa += 2 * (precision * recall) / (precision + recall)

    exact_match += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)

 UpperCAmelCase__ 						=				[x for _, x in sorted(zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)			)]
 recalls.sort()

 UpperCAmelCase__ 						=				100.0 * fa / total
 UpperCAmelCase__ 						=				100.0 * exact_match / total
 UpperCAmelCase__ 						=				get_aupr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)

 UpperCAmelCase__ 						=				get_prec_at_recall(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	recall_thresh=0.9			)
 UpperCAmelCase__ 						=				get_prec_at_recall(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	recall_thresh=0.8			)

 return {
     "exact_match": exact_match,
     "f1": fa,
     "aupr": aupr,
     "prec_at_80_recall": prec_at_aa_recall,
     "prec_at_90_recall": prec_at_aa_recall,
 }


if __name__ == "__main__":
 a  :						List[str]					       =							argparse.ArgumentParser(description='''Evaluation for CUAD''')
 parser.add_argument('''dataset_file''', help='''Dataset file''')
 parser.add_argument('''prediction_file''', help='''Prediction File''')
 a  :						int					       =							parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  a  :						Optional[int]					       =							json.load(dataset_file)
  a  :						List[Any]					       =							dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  a  :						Tuple					       =							json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))






'''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


lowerCAmelCase__      				=			logging.get_logger(__name__)

# General docstring
lowerCAmelCase__      				=			"ResNetConfig"

# Base docstring
lowerCAmelCase__      				=			"microsoft/resnet-50"
lowerCAmelCase__      				=			[1, 2048, 7, 7]

# Image classification docstring
lowerCAmelCase__      				=			"microsoft/resnet-50"
lowerCAmelCase__      				=			"tiger cat"

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



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


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


							def     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								# Pad to match that done in the PyTorch Conv2D model
								UpperCAmelCase_		:   List[str]													=       (self.pad_value, self.pad_value)
								UpperCAmelCase_		:   Dict													=       tf.pad(_snake_case     ,      [(0, 0), height_pad, width_pad, (0, 0)])
								UpperCAmelCase_		:   str													=       self.conv(_snake_case)
								return hidden_state





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.normalization(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Any													=       self.activation(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       TFResNetConvLayer(
								    config.embedding_size     ,      kernel_size=7     ,      stride=2     ,      activation=config.hidden_act     ,      name='embedder'     ,      )
								UpperCAmelCase_		:   str													=       tf.keras.layers.MaxPoolaD(pool_size=3     ,      strides=2     ,      padding='valid'     ,      name='pooler')
								UpperCAmelCase_		:   Any													=       config.num_channels





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       shape_list(_snake_case)
								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_		:   Any													=       pixel_values
								UpperCAmelCase_		:   int													=       self.embedder(_snake_case)
								UpperCAmelCase_		:   Tuple													=       tf.pad(_snake_case     ,      [[0, 0], [1, 1], [1, 1], [0, 0]])
								UpperCAmelCase_		:   Any													=       self.pooler(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   Any													=       tf.keras.layers.ConvaD(
								    _snake_case     ,      kernel_size=1     ,      strides=_snake_case     ,      use_bias=_snake_case     ,      name='convolution')
								# Use same default momentum and epsilon as PyTorch equivalent
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='normalization')





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       x
								UpperCAmelCase_		:   List[str]													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.normalization(_snake_case     ,      training=_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       in_channels != out_channels or stride != 1
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      stride=_snake_case     ,      name='layer.0')
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      activation=_snake_case     ,      name='layer.1')
								UpperCAmelCase_		:   Union[str, Any]													=       (
								    TFResNetShortCut(_snake_case     ,      stride=_snake_case     ,      name='shortcut')
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('linear'     ,      name='shortcut')
								)
								UpperCAmelCase_		:   int													=       ACTaFN[activation]





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   List[Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   int													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      _snake_case = 4     ,      **_snake_case     ,      )				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   int													=       in_channels != out_channels or stride != 1
								UpperCAmelCase_		:   Tuple													=       out_channels // reduction
								UpperCAmelCase_		:   int													=       TFResNetConvLayer(_snake_case     ,      kernel_size=1     ,      name='layer.0')
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      stride=_snake_case     ,      name='layer.1')
								UpperCAmelCase_		:   List[Any]													=       TFResNetConvLayer(_snake_case     ,      kernel_size=1     ,      activation=_snake_case     ,      name='layer.2')
								UpperCAmelCase_		:   Optional[int]													=       (
								    TFResNetShortCut(_snake_case     ,      stride=_snake_case     ,      name='shortcut')
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('linear'     ,      name='shortcut')
								)
								UpperCAmelCase_		:   Tuple													=       ACTaFN[activation]





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = 2     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:   Dict													=       TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

								UpperCAmelCase_		:   List[Any]													=       [layer(_snake_case     ,      _snake_case     ,      stride=_snake_case     ,      activation=config.hidden_act     ,      name='layers.0')]
								layers += [
								    layer(_snake_case     ,      _snake_case     ,      activation=config.hidden_act     ,      name=F"""layers.{i + 1}""")
								    for i in range(depth - 1)
								]
								UpperCAmelCase_		:   Optional[Any]													=       layers





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								for layer in self.stage_layers:
									UpperCAmelCase_		:   List[Any]													=       layer(_snake_case     ,      training=_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								UpperCAmelCase_		:   Optional[int]													=       [
								    TFResNetStage(
								        _snake_case     ,      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(_snake_case     ,      _snake_case     ,      _snake_case     ,      depth=_snake_case     ,      name=F"""stages.{i + 1}"""))





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False     ,      _snake_case = True     ,      _snake_case = False     ,      )				->					TFBaseModelOutputWithNoAttention:
								UpperCAmelCase_		:   Optional[Any]													=       () if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										UpperCAmelCase_		:   Optional[Any]													=       hidden_states + (hidden_state,)

									UpperCAmelCase_		:   List[Any]													=       stage_module(_snake_case     ,      training=_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:   int													=       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=_snake_case     ,      hidden_states=_snake_case)



class        lowercase    (	a_			):
							_lowerCamelCase		:   Optional[int]= ResNetConfig
							_lowerCamelCase		:   str= "resnet"
							_lowerCamelCase		:   Optional[int]= "pixel_values"


							@property
							def     _snake_case				(      self)				->					int:
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)     ,      dtype=tf.floataa)}


lowerCAmelCase__      				=			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"


lowerCAmelCase__      				=			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			):
							_lowerCamelCase		:   Any= ResNetConfig


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       config
								UpperCAmelCase_		:   Dict													=       TFResNetEmbeddings(_snake_case     ,      name='embedder')
								UpperCAmelCase_		:   Optional[Any]													=       TFResNetEncoder(_snake_case     ,      name='encoder')
								UpperCAmelCase_		:   Optional[Any]													=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=_snake_case)





							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Any													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   Tuple													=       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_		:   Any													=       tf.transpose(_snake_case     ,      perm=[0, 2, 3, 1])
								UpperCAmelCase_		:   Any													=       self.embedder(_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   str													=       self.encoder(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   List[Any]													=       encoder_outputs[0]

								UpperCAmelCase_		:   Union[str, Any]													=       self.pooler(_snake_case)

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

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

								UpperCAmelCase_		:   Any													=       hidden_states if output_hidden_states else None

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



@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top.",       a_,       )
class        lowercase    (	a_			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   str													=       TFResNetMainLayer(config=_snake_case     ,      name='resnet')





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC     ,      output_type=_snake_case     ,      config_class=_CONFIG_FOR_DOC     ,      modality='vision'     ,      expected_output=_EXPECTED_OUTPUT_SHAPE     ,      )
							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Optional[Any]													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   Tuple													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   str													=       self.resnet(
								    pixel_values=_snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case     ,      )
								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     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   Tuple													=       config.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       TFResNetMainLayer(_snake_case     ,      name='resnet')
								# classification head
								UpperCAmelCase_		:   str													=       (
								    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     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.Flatten()(_snake_case)
								UpperCAmelCase_		:   List[Any]													=       self.classifier_layer(_snake_case)
								return logits





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,      output_type=_snake_case     ,      config_class=_CONFIG_FOR_DOC     ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,      )
							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
								UpperCAmelCase_		:   Dict													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   List[Any]													=       self.resnet(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   Any													=       outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:   Union[str, Any]													=       self.classifier(_snake_case)

								UpperCAmelCase_		:   Optional[int]													=       None if labels is None else self.hf_compute_loss(_snake_case     ,      _snake_case)

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

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






