



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 			__snake_case   ( tf.keras.layers.Layer):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_ = 3	,	a_ = 1	,	a_ = "relu"	,	**a_		):
											super().__init__(**a_		)
											a__					   =		kernel_size // 2
											a__					   =		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
											a__					   =		tf.keras.layers.BatchNormalization(epsilon=1E-5	,	momentum=0.9	,	name="""normalization"""		)
											a__					   =		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
											a__					   =		a__					   =		(self.pad_value, self.pad_value)
											a__					   =		tf.pad(a_	,	[(0, 0), height_pad, width_pad, (0, 0)]		)
											a__					   =		self.conv(a_		)
											return hidden_state







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		self.convolution(a_		)
											a__					   =		self.normalization(a_	,	training=a_		)
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






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







							def _a   (				self	,	a_	,	a_ = False		):
											a__   ,      a__   ,      a__   ,      a__					   =		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."""		)
											a__					   =		pixel_values
											a__					   =		self.embedder(a_		)
											a__					   =		tf.pad(a_	,	[[0, 0], [1, 1], [1, 1], [0, 0]]		)
											a__					   =		self.pooler(a_		)
											return hidden_state




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







							'''simple docstring'''






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







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		x
											a__					   =		self.convolution(a_		)
											a__					   =		self.normalization(a_	,	training=a_		)
											return hidden_state




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







							'''simple docstring'''






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







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		hidden_state
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.shortcut(a_	,	training=a_		)
											hidden_state += residual
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






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







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		hidden_state
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.shortcut(a_	,	training=a_		)
											hidden_state += residual
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






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

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

											a__					   =		[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		)
											]
											a__					   =		layers







							def _a   (				self	,	a_	,	a_ = False		):
											for layer in self.stage_layers:
															a__					   =		layer(a_	,	training=a_		)
											return hidden_state




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







							'''simple docstring'''






							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
											a__					   =		[
											    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	,	):
											a__					   =		() if output_hidden_states else None

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

															a__					   =		stage_module(a_	,	training=a_		)

											if output_hidden_states:
															a__					   =		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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   int       =       ResNetConfig
							UpperCamelCase__					:   Tuple       =       """resnet"""
							UpperCamelCase__					:   Tuple       =       """pixel_values"""


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




UpperCAmelCase							=       R"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


UpperCAmelCase							=       R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@keras_serializable
class 			__snake_case   ( tf.keras.layers.Layer):







							'''simple docstring'''






							UpperCamelCase__					:   Union[str, Any]       =       ResNetConfig


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







							@unpack_inputs
							def _a   (				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = False	,	):
											a__					   =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											a__					   =		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)
											a__					   =		tf.transpose(a_	,	perm=[0, 2, 3, 1]		)
											a__					   =		self.embedder(a_	,	training=a_		)

											a__					   =		self.encoder(
											    a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_		)

											a__					   =		encoder_outputs[0]

											a__					   =		self.pooler(a_		)

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

											a__					   =		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."""      ,SCREAMING_SNAKE_CASE      ,)
class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(a_	,	**a_		)
											a__					   =		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	,	):
											a__					   =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											a__					   =		return_dict if return_dict is not None else self.config.use_return_dict

											a__					   =		self.resnet(
											    pixel_values=a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_	,	)
											return resnet_outputs




@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      ,SCREAMING_SNAKE_CASE      ,)
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(a_	,	**a_		)
											a__					   =		config.num_labels
											a__					   =		TFResNetMainLayer(a_	,	name="""resnet"""		)
											# classification head
											a__					   =		(
											    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_		):
											a__					   =		tf.keras.layers.Flatten()(a_		)
											a__					   =		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	,	):
											a__					   =		return_dict if return_dict is not None else self.config.use_return_dict

											a__					   =		self.resnet(
											    a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_		)

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

											a__					   =		self.classifier(a_		)

											a__					   =		None if labels is None else self.hf_compute_loss(a_	,	a_		)

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

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






from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

											# Number of decision variables x1, x2, x3...
											a__					   =		n_vars

											# Number of artificial variables to be minimised
											a__					   =		len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											a__					   =		(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											a__					   =		self.n_rows - self.n_stages

											# Objectives for each stage
											a__					   =		["""max"""]

											# In two stage simplex, first minimise then maximise
											if self.n_art_vars:
															self.objectives.append("""min"""		)

											a__					   =		[""""""]

											# Index of current pivot row and column
											a__					   =		None
											a__					   =		None

											# Does objective row only contain (non)-negative values?
											a__					   =		False


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

											if not all(x >= 0 and isinstance(a_	,	a_		) for x in args		):
															raise ValueError("""All arguments must be non-negative integers"""		)

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											for i in range(3		):
															for j in range(args[i]		):
																			titles.append(string_starts[i] + str(j + 1		)		)
											titles.append("""RHS"""		)
											return titles


							def _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											a__					   =		(objective == """min""") - (objective == """max""")
											a__					   =		np.argmax(sign * tableau[0, : self.n_vars]		)

											# Choice is only valid if below 0 for maximise, and above for minimise
											if sign * self.tableau[0, col_idx] <= 0:
															a__					   =		True
															return 0, 0

											# Pivot row is chosen as having the lowest quotient when elements of
											# the pivot column divide the right-hand side

											# Slice excluding the objective rows
											a__					   =		slice(self.n_stages	,	self.n_rows		)

											# RHS
											a__					   =		tableau[s, -1]

											# Elements of pivot column within slice
											a__					   =		tableau[s, col_idx]

											# Array filled with nans
											a__					   =		np.full(self.n_rows - self.n_stages	,	np.nan		)

											# If element in pivot column is greater than zeron_stages, return
											# quotient or nan otherwise
											a__					   =		np.divide(a_	,	a_	,	out=a_	,	where=divisor > 0		)

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		piv_row[col_idx]

											# Entry becomes 1
											piv_row *= 1 / piv_val

											# Variable in pivot column becomes basic, ie the only non-zero entry
											for idx, coeff in enumerate(tableau[:, col_idx]		):
															tableau[idx] += -coeff * piv_row
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

											# Slice containing ids for artificial columns
											a__					   =		slice(-self.n_art_vars - 1	,	-1		)

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

											# Delete the objective row of the first stage
											a__					   =		np.delete(a_	,	0	,	axis=0		)

											a__					   =		1
											self.n_rows -= 1
											a__					   =		0
											a__					   =		False
											return tableau


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		self.generate_col_titles(
																			    self.n_vars	,	self.n_slack	,	self.n_art_vars		)

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau	,	self.col_titles		)

															a__   ,      a__					   =		self.find_pivot(self.tableau		)

															# If there are no more negative values in objective row
															if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

											for i in range(self.n_vars		):
															# Gives ids of nonzero entries in the ith column
															a__					   =		np.nonzero(tableau[:, i]		)
															a__					   =		len(nonzero[0]		)

															# First entry in the nonzero ids
															a__					   =		nonzero[0][0]
															a__					   =		tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__					   =		tableau[nonzero_rowidx, -1]
																			a__					   =		rhs_val

        # Check for basic variables
											for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																			output_dict.setdefault(a_	,	0		)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()

