





"""simple docstring"""




from typing import Optional, Tuple, Union

import tensorflow as tf

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


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

# General docstring
a		:				Tuple  			=    '''RegNetConfig'''

# Base docstring
a		:				Any  			=    '''facebook/regnet-y-040'''
a		:				int  			=    [1, 1088, 7, 7]

# Image classification docstring
a		:				Any  			=    '''facebook/regnet-y-040'''
a		:				Union[str, Any]  			=    '''tabby, tabby cat'''

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


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






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






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	Any			=       self.convolution(self.padding(lowerCAmelCase__   )   )
								a   :	Optional[int]			=       self.normalization(lowerCAmelCase__   )
								a   :	List[str]			=       self.activation(lowerCAmelCase__   )
								return hidden_state


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






							def __init__(     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								super().__init__(**lowerCAmelCase__   )
								a   :	Tuple			=       config.num_channels
								a   :	Optional[Any]			=       TFRegNetConvLayer(
								    out_channels=config.embedding_size   ,      kernel_size=3   ,      stride=2   ,      activation=config.hidden_act   ,      name="embedder"   ,      )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	Union[str, Any]			=       shape_list(lowerCAmelCase__   )[1]
								if tf.executing_eagerly() and num_channels != self.num_channels:
									raise ValueError(
									    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."   )

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


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






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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = False   )			->				tf.Tensor:
								return self.normalization(self.convolution(lowerCAmelCase__   )   ,      training=lowerCAmelCase__   )


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






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[int]:
								super().__init__(**lowerCAmelCase__   )
								a   :	str			=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase__   ,      name="pooler"   )
								a   :	Optional[int]			=       [
								    tf.keras.layers.ConvaD(filters=lowerCAmelCase__   ,      kernel_size=1   ,      activation="relu"   ,      name="attention.0"   ),
								    tf.keras.layers.ConvaD(filters=lowerCAmelCase__   ,      kernel_size=1   ,      activation="sigmoid"   ,      name="attention.2"   ),
								]






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


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






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






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a   :	Any			=       hidden_state
								for layer_module in self.layers:
									a   :	Tuple			=       layer_module(lowerCAmelCase__   )
								a   :	Dict			=       self.shortcut(lowerCAmelCase__   )
								hidden_state += residual
								a   :	int			=       self.activation(lowerCAmelCase__   )
								return hidden_state


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






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






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								a   :	Union[str, Any]			=       hidden_state
								for layer_module in self.layers:
									a   :	str			=       layer_module(lowerCAmelCase__   )
								a   :	Union[str, Any]			=       self.shortcut(lowerCAmelCase__   )
								hidden_state += residual
								a   :	Optional[int]			=       self.activation(lowerCAmelCase__   )
								return hidden_state


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






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = 2   ,      lowerCAmelCase__ = 2   ,      **lowerCAmelCase__   )			->				List[Any]:
								super().__init__(**lowerCAmelCase__   )

								a   :	Tuple			=       TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
								a   :	Optional[Any]			=       [
								    # downsampling is done in the first layer with stride of 2
								    layer(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      stride=lowerCAmelCase__   ,      name="layers.0"   ),
								    *[layer(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      name=f"""layers.{i+1}"""   ) for i in range(depth - 1   )],
								]






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
								for layer_module in self.layers:
									a   :	Dict			=       layer_module(lowerCAmelCase__   )
								return hidden_state


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






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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = True   )			->				TFBaseModelOutputWithNoAttention:
								a   :	int			=       () if output_hidden_states else None

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

									a   :	Any			=       stage_module(lowerCAmelCase__   )

								if output_hidden_states:
									a   :	Union[str, Any]			=       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=lowerCAmelCase__   ,      hidden_states=lowerCAmelCase__   )


@keras_serializable
class  __UpperCamelCase       (		tf.keras.layers.Layer		):
							lowerCamelCase     :      Tuple         =RegNetConfig






							def __init__(     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								super().__init__(**lowerCAmelCase__   )
								a   :	int			=       config
								a   :	str			=       TFRegNetEmbeddings(lowerCAmelCase__   ,      name="embedder"   )
								a   :	Union[str, Any]			=       TFRegNetEncoder(lowerCAmelCase__   ,      name="encoder"   )
								a   :	Union[str, Any]			=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase__   ,      name="pooler"   )






							@unpack_inputs
							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      )			->				TFBaseModelOutputWithPoolingAndNoAttention:
								a   :	Any			=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								a   :	int			=       return_dict if return_dict is not None else self.config.use_return_dict

								a   :	Any			=       self.embedder(lowerCAmelCase__   ,      training=lowerCAmelCase__   )

								a   :	Tuple			=       self.encoder(
								    lowerCAmelCase__   ,      output_hidden_states=lowerCAmelCase__   ,      return_dict=lowerCAmelCase__   ,      training=lowerCAmelCase__   )

								a   :	int			=       encoder_outputs[0]
								a   :	List[Any]			=       self.pooler(lowerCAmelCase__   )

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

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

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

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


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[str]         =RegNetConfig
							lowerCamelCase     :      Any         ="""regnet"""
							lowerCamelCase     :      int         ="""pixel_values"""






							@property
							def 					__a     (     self   )			->				str:
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)   ,      dtype=tf.floataa   )}
a		:				Optional[Any]  			=    R'''
    Parameters:
    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.
        config ([`RegNetConfig`]): 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.
'''

a		:				Dict  			=    R'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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.
'''


@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""	, a__	, )
class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				List[str]:
								super().__init__(lowerCAmelCase__   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Union[str, Any]			=       TFRegNetMainLayer(lowerCAmelCase__   ,      name="regnet"   )






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

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

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


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






							def __init__(     self   ,      lowerCAmelCase__   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				str:
								super().__init__(lowerCAmelCase__   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Optional[Any]			=       config.num_labels
								a   :	Any			=       TFRegNetMainLayer(lowerCAmelCase__   ,      name="regnet"   )
								# classification head
								a   :	List[str]			=       [
								    tf.keras.layers.Flatten(),
								    tf.keras.layers.Dense(config.num_labels   ,      name="classifier.1"   ) if config.num_labels > 0 else tf.identity,
								]






							@unpack_inputs
							@add_start_docstrings_to_model_forward(lowerCAmelCase__   )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,      output_type=lowerCAmelCase__   ,      config_class=_CONFIG_FOR_DOC   ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,      )
							def 					__a     (     self   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__=False   ,      )			->				Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
								a   :	Any			=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								a   :	List[str]			=       return_dict if return_dict is not None else self.config.use_return_dict

								a   :	Union[str, Any]			=       self.regnet(
								    lowerCAmelCase__   ,      output_hidden_states=lowerCAmelCase__   ,      return_dict=lowerCAmelCase__   ,      training=lowerCAmelCase__   )

								a   :	Dict			=       outputs.pooler_output if return_dict else outputs[1]

								a   :	List[Any]			=       self.classifier[0](lowerCAmelCase__   )
								a   :	Dict			=       self.classifier[1](lowerCAmelCase__   )

								a   :	Tuple			=       None if labels is None else self.hf_compute_loss(labels=lowerCAmelCase__   ,      logits=lowerCAmelCase__   )

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

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









"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


