



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


UpperCamelCase_      			=  logging.get_logger(__name__)

# General docstring
UpperCamelCase_      			=  "ResNetConfig"

# Base docstring
UpperCamelCase_      			=  "microsoft/resnet-50"
UpperCamelCase_      			=  [1, 2_048, 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_    (						tf.keras.layers.Layer							):

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :int = 3  ,		_lowercase      :int = 1  ,		_lowercase      :str = "relu"  ,		**_lowercase      :Optional[Any])						->   None:
												super().__init__(**_lowercase)
												UpperCAmelCase_					=	kernel_size // 2
												UpperCAmelCase_					=	tf.keras.layers.ConvaD(
												    _lowercase  ,		kernel_size=_lowercase  ,		strides=_lowercase  ,		padding='''valid'''  ,		use_bias=_lowercase  ,		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      :Optional[Any]  ,		_lowercase      :tf.Tensor)						->   tf.Tensor:
												# Pad to match that done in the PyTorch Conv2D model
												UpperCAmelCase_					=	UpperCAmelCase_					=	(self.pad_value, self.pad_value)
												UpperCAmelCase_					=	tf.pad(_lowercase  ,		[(0, 0), height_pad, width_pad, (0, 0)])
												UpperCAmelCase_					=	self.conv(_lowercase)
												return hidden_state




						def       __a			(       self      :Tuple  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												UpperCAmelCase_					=	self.convolution(_lowercase)
												UpperCAmelCase_					=	self.normalization(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.activation(_lowercase)
												return hidden_state







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

						def __init__(       self      :Dict  ,		_lowercase      :ResNetConfig  ,		**_lowercase      :Union[str, Any])						->   None:
												super().__init__(**_lowercase)
												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      :Dict  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	shape_list(_lowercase)
												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(_lowercase)
												UpperCAmelCase_					=	tf.pad(_lowercase  ,		[[0, 0], [1, 1], [1, 1], [0, 0]])
												UpperCAmelCase_					=	self.pooler(_lowercase)
												return hidden_state







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

						def __init__(       self      :Dict  ,		_lowercase      :int  ,		_lowercase      :int = 2  ,		**_lowercase      :str)						->   None:
												super().__init__(**_lowercase)
												UpperCAmelCase_					=	tf.keras.layers.ConvaD(
												    _lowercase  ,		kernel_size=1  ,		strides=_lowercase  ,		use_bias=_lowercase  ,		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      :Union[str, Any]  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	self.convolution(_lowercase)
												UpperCAmelCase_					=	self.normalization(_lowercase  ,		training=_lowercase)
												return hidden_state







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

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




						def       __a			(       self      :Any  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												UpperCAmelCase_					=	hidden_state
												UpperCAmelCase_					=	self.conva(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.conva(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.shortcut(_lowercase  ,		training=_lowercase)
												hidden_state += residual
												UpperCAmelCase_					=	self.activation(_lowercase)
												return hidden_state







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

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




						def       __a			(       self      :List[Any]  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												UpperCAmelCase_					=	hidden_state
												UpperCAmelCase_					=	self.conva(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.conva(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.conva(_lowercase  ,		training=_lowercase)
												UpperCAmelCase_					=	self.shortcut(_lowercase  ,		training=_lowercase)
												hidden_state += residual
												UpperCAmelCase_					=	self.activation(_lowercase)
												return hidden_state







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

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :ResNetConfig  ,		_lowercase      :int  ,		_lowercase      :int  ,		_lowercase      :int = 2  ,		_lowercase      :int = 2  ,		**_lowercase      :Dict)						->   None:
												super().__init__(**_lowercase)

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

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




						def       __a			(       self      :str  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False)						->   tf.Tensor:
												for layer in self.stage_layers:
																		UpperCAmelCase_					=	layer(_lowercase  ,		training=_lowercase)
												return hidden_state







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

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




						def       __a			(       self      :str  ,		_lowercase      :tf.Tensor  ,		_lowercase      :bool = False  ,		_lowercase      :bool = True  ,		_lowercase      :bool = False  ,		)						->   TFBaseModelOutputWithNoAttention:
												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(_lowercase  ,		training=_lowercase)

												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=_lowercase  ,		hidden_states=_lowercase)







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		int										=ResNetConfig
						UpperCamelCase__ :		Dict										="resnet"
						UpperCamelCase__ :		Any										="pixel_values"

						@property
						def       __a			(       self      :Any)						->   List[str]:
												return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,		dtype=tf.floataa)}

UpperCamelCase_      			=  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"


UpperCamelCase_      			=  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 				a_    (						tf.keras.layers.Layer							):
						UpperCamelCase__ :		Any										=ResNetConfig

						def __init__(       self      :Tuple  ,		_lowercase      :ResNetConfig  ,		**_lowercase      :str)						->   None:
												super().__init__(**_lowercase)
												UpperCAmelCase_					=	config
												UpperCAmelCase_					=	TFResNetEmbeddings(_lowercase  ,		name='''embedder''')
												UpperCAmelCase_					=	TFResNetEncoder(_lowercase  ,		name='''encoder''')
												UpperCAmelCase_					=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase)




						@unpack_inputs
						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :tf.Tensor  ,		_lowercase      :Optional[bool] = None  ,		_lowercase      :Optional[bool] = None  ,		_lowercase      :bool = False  ,		)						->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
												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(_lowercase  ,		perm=[0, 2, 3, 1])
												UpperCAmelCase_					=	self.embedder(_lowercase  ,		training=_lowercase)

												UpperCAmelCase_					=	self.encoder(
												    _lowercase  ,		output_hidden_states=_lowercase  ,		return_dict=_lowercase  ,		training=_lowercase)

												UpperCAmelCase_					=	encoder_outputs[0]

												UpperCAmelCase_					=	self.pooler(_lowercase)

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







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

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :ResNetConfig  ,		**_lowercase      :Optional[int])						->   None:
												super().__init__(_lowercase  ,		**_lowercase)
												UpperCAmelCase_					=	TFResNetMainLayer(config=_lowercase  ,		name='''resnet''')




						@add_start_docstrings_to_model_forward(_lowercase)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC  ,		output_type=_lowercase  ,		config_class=_CONFIG_FOR_DOC  ,		modality='''vision'''  ,		expected_output=_EXPECTED_OUTPUT_SHAPE  ,		)
						@unpack_inputs
						def       __a			(       self      :Dict  ,		_lowercase      :tf.Tensor  ,		_lowercase      :Optional[bool] = None  ,		_lowercase      :Optional[bool] = None  ,		_lowercase      :bool = False  ,		)						->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
												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=_lowercase  ,		output_hidden_states=_lowercase  ,		return_dict=_lowercase  ,		training=_lowercase  ,		)
												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    "   ,					_snake_case   ,					)
class 				a_    (						_snake_case   ,					_snake_case							):

						def __init__(       self      :List[str]  ,		_lowercase      :ResNetConfig  ,		**_lowercase      :str)						->   None:
												super().__init__(_lowercase  ,		**_lowercase)
												UpperCAmelCase_					=	config.num_labels
												UpperCAmelCase_					=	TFResNetMainLayer(_lowercase  ,		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      :List[Any]  ,		_lowercase      :tf.Tensor)						->   tf.Tensor:
												UpperCAmelCase_					=	tf.keras.layers.Flatten()(_lowercase)
												UpperCAmelCase_					=	self.classifier_layer(_lowercase)
												return logits




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

												UpperCAmelCase_					=	self.resnet(
												    _lowercase  ,		output_hidden_states=_lowercase  ,		return_dict=_lowercase  ,		training=_lowercase)

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

												UpperCAmelCase_					=	self.classifier(_lowercase)

												UpperCAmelCase_					=	None if labels is None else self.hf_compute_loss(_lowercase  ,		_lowercase)

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

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










import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def 							A     (						__UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						class 				a_    :

												def __init__(       self      :Optional[int]  ,		_lowercase      :Any)						->   None:
																		assert isinstance(_lowercase  ,		(bytes, str))
																		UpperCAmelCase_					=	content




												def       __a			(       self      :List[str])						->   Tuple:
																		return json.loads(self.content)

						def mock_response(*__UpperCAmelCase				,   **__UpperCAmelCase      ):
												assert args[0] == AUTHENTICATED_USER_ENDPOINT
												assert "Authorization" in kwargs["headers"]
												assert kwargs["headers"]["Authorization"].startswith('''token '''      )
												assert "Accept" in kwargs["headers"]
												return FakeResponse(b'''{"login":"test","id":1}'''      )

						monkeypatch.setattr(__UpperCAmelCase				,   '''get'''				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	fetch_github_info('''token'''      )
						assert result["login"] == "test"
						assert result["id"] == 1





