
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


snake_case_      :					int				      =     logging.get_logger(__name__)

# General docstring
snake_case_      :					Any				      =     """ResNetConfig"""

# Base docstring
snake_case_      :					str				      =     """microsoft/resnet-50"""
snake_case_      :					Union[str, Any]				      =     [1, 2_0_4_8, 7, 7]

# Image classification docstring
snake_case_      :					Union[str, Any]				      =     """microsoft/resnet-50"""
snake_case_      :					int				      =     """tiger cat"""

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





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



							def __init__(     self	,				__snake_case	,				__snake_case = 3	,				__snake_case = 1	,				__snake_case = "relu"	,				**__snake_case							)						->     List[Any]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					Union[str, Any] 						=     kernel_size // 2
								UpperCAmelCase:					Union[str, Any] 						=     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:					Optional[Any] 						=     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     A__ (     self	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     (self.pad_value, self.pad_value)
								UpperCAmelCase:					Any 						=     tf.pad(__lowercase	,				[(0, 0), height_pad, width_pad, (0, 0)]							)
								UpperCAmelCase:					Optional[int] 						=     self.conv(__lowercase							)
								return hidden_state




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self.convolution(__lowercase							)
								UpperCAmelCase:					Union[str, Any] 						=     self.normalization(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					int 						=     self.activation(__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				**__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					int 						=     TFResNetConvLayer(
								    config.embedding_size	,				kernel_size=7	,				stride=2	,				activation=config.hidden_act	,				name="embedder"	,				)
								UpperCAmelCase:					Tuple 						=     tf.keras.layers.MaxPoolaD(pool_size=3	,				strides=2	,				padding="valid"	,				name="pooler"							)
								UpperCAmelCase:					Optional[int] 						=     config.num_channels




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     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:					Any 						=     pixel_values
								UpperCAmelCase:					Union[str, Any] 						=     self.embedder(__lowercase							)
								UpperCAmelCase:					Tuple 						=     tf.pad(__lowercase	,				[[0, 0], [1, 1], [1, 1], [0, 0]]							)
								UpperCAmelCase:					Any 						=     self.pooler(__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				__snake_case = 2	,				**__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					Tuple 						=     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:					str 						=     tf.keras.layers.BatchNormalization(epsilon=1e-5	,				momentum=0.9	,				name="normalization"							)




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     x
								UpperCAmelCase:					List[Any] 						=     self.convolution(__lowercase							)
								UpperCAmelCase:					Tuple 						=     self.normalization(__lowercase	,				training=__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case = 1	,				__snake_case = "relu"	,				**__snake_case							)						->     List[Any]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					Any 						=     in_channels != out_channels or stride != 1
								UpperCAmelCase:					int 						=     TFResNetConvLayer(__lowercase	,				stride=__lowercase	,				name="layer.0"							)
								UpperCAmelCase:					Optional[int] 						=     TFResNetConvLayer(__lowercase	,				activation=__lowercase	,				name="layer.1"							)
								UpperCAmelCase:					int 						=     (
								    TFResNetShortCut(__lowercase	,				stride=__lowercase	,				name="shortcut"							)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation("linear"	,				name="shortcut"							)
								)
								UpperCAmelCase:					int 						=     ACTaFN[activation]




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     hidden_state
								UpperCAmelCase:					Tuple 						=     self.conva(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					Optional[Any] 						=     self.conva(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					str 						=     self.shortcut(__lowercase	,				training=__lowercase							)
								hidden_state += residual
								UpperCAmelCase:					Union[str, Any] 						=     self.activation(__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case = 1	,				__snake_case = "relu"	,				__snake_case = 4	,				**__snake_case	,				)						->     str:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					int 						=     in_channels != out_channels or stride != 1
								UpperCAmelCase:					Optional[Any] 						=     out_channels // reduction
								UpperCAmelCase:					Tuple 						=     TFResNetConvLayer(__lowercase	,				kernel_size=1	,				name="layer.0"							)
								UpperCAmelCase:					str 						=     TFResNetConvLayer(__lowercase	,				stride=__lowercase	,				name="layer.1"							)
								UpperCAmelCase:					Dict 						=     TFResNetConvLayer(__lowercase	,				kernel_size=1	,				activation=__lowercase	,				name="layer.2"							)
								UpperCAmelCase:					List[Any] 						=     (
								    TFResNetShortCut(__lowercase	,				stride=__lowercase	,				name="shortcut"							)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation("linear"	,				name="shortcut"							)
								)
								UpperCAmelCase:					Dict 						=     ACTaFN[activation]




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     hidden_state
								UpperCAmelCase:					Optional[Any] 						=     self.conva(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					List[str] 						=     self.conva(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					Tuple 						=     self.conva(__lowercase	,				training=__lowercase							)
								UpperCAmelCase:					Tuple 						=     self.shortcut(__lowercase	,				training=__lowercase							)
								hidden_state += residual
								UpperCAmelCase:					Tuple 						=     self.activation(__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case = 2	,				__snake_case = 2	,				**__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								super().__init__(**__lowercase							)

								UpperCAmelCase:					Optional[Any] 						=     TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

								UpperCAmelCase:					str 						=     [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:					str 						=     layers




							def     A__ (     self	,				__snake_case	,				__snake_case = False							)						->     Dict:






								"""simple docstring"""



								for layer in self.stage_layers:
									UpperCAmelCase:					Any 						=     layer(__lowercase	,				training=__lowercase							)
								return hidden_state





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



							def __init__(     self	,				__snake_case	,				**__snake_case							)						->     List[Any]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								UpperCAmelCase:					Any 						=     [
								    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	,				__snake_case	,				__snake_case = False	,				__snake_case = True	,				__snake_case = False	,				)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     () if output_hidden_states else None

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

									UpperCAmelCase:					int 						=     stage_module(__lowercase	,				training=__lowercase							)

								if output_hidden_states:
									UpperCAmelCase:					List[str] 						=     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 		__lowerCamelCase     (   __A		):
							lowerCamelCase__:				Any      =							ResNetConfig
							lowerCamelCase__:				Optional[Any]      =							'resnet'
							lowerCamelCase__:				Optional[int]      =							'pixel_values'



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)	,				dtype=tf.floataa							)}


snake_case_      :					str				      =     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.
"""


snake_case_      :					Optional[Any]				      =     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 		__lowerCamelCase     (   tf.keras.layers.Layer		):
							lowerCamelCase__:				Any      =							ResNetConfig



							def __init__(     self	,				__snake_case	,				**__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								super().__init__(**__lowercase							)
								UpperCAmelCase:					List[Any] 						=     config
								UpperCAmelCase:					int 						=     TFResNetEmbeddings(__lowercase	,				name="embedder"							)
								UpperCAmelCase:					Union[str, Any] 						=     TFResNetEncoder(__lowercase	,				name="encoder"							)
								UpperCAmelCase:					Dict 						=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase							)




							@unpack_inputs
							def     A__ (     self	,				__snake_case	,				__snake_case = None	,				__snake_case = None	,				__snake_case = False	,				)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase:					str 						=     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:					Union[str, Any] 						=     tf.transpose(__lowercase	,				perm=[0, 2, 3, 1]							)
								UpperCAmelCase:					Tuple 						=     self.embedder(__lowercase	,				training=__lowercase							)

								UpperCAmelCase:					Any 						=     self.encoder(
								    __lowercase	,				output_hidden_states=__lowercase	,				return_dict=__lowercase	,				training=__lowercase							)

								UpperCAmelCase:					Optional[Any] 						=     encoder_outputs[0]

								UpperCAmelCase:					Dict 						=     self.pooler(__lowercase							)

								# Transpose all the outputs to the NCHW format
								# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
								UpperCAmelCase:					Union[str, Any] 						=     tf.transpose(__lowercase	,				(0, 3, 1, 2)							)
								UpperCAmelCase:					Optional[Any] 						=     tf.transpose(__lowercase	,				(0, 3, 1, 2)							)
								UpperCAmelCase:					List[str] 						=     ()
								for hidden_state in encoder_outputs[1:]:
									UpperCAmelCase:					Optional[int] 						=     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:					Optional[Any] 						=     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.'''							, __A							, )
class 		__lowerCamelCase     (   __A		):



							def __init__(     self	,				__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								super().__init__(__lowercase	,				**__lowercase							)
								UpperCAmelCase:					str 						=     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	,				__snake_case	,				__snake_case = None	,				__snake_case = None	,				__snake_case = False	,				)						->     Union[str, Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase:					Dict 						=     return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase:					Dict 						=     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    '''							, __A							, )
class 		__lowerCamelCase     (   __A							, __A		):



							def __init__(     self	,				__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								super().__init__(__lowercase	,				**__lowercase							)
								UpperCAmelCase:					Union[str, Any] 						=     config.num_labels
								UpperCAmelCase:					List[Any] 						=     TFResNetMainLayer(__lowercase	,				name="resnet"							)
								# classification head
								UpperCAmelCase:					Optional[int] 						=     (
								    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	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     tf.keras.layers.Flatten()(__lowercase							)
								UpperCAmelCase:					Optional[int] 						=     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	,				__snake_case = None	,				__snake_case = None	,				__snake_case = None	,				__snake_case = None	,				__snake_case = False	,				)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase:					int 						=     self.resnet(
								    __lowercase	,				output_hidden_states=__lowercase	,				return_dict=__lowercase	,				training=__lowercase							)

								UpperCAmelCase:					str 						=     outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase:					Optional[Any] 						=     self.classifier(__lowercase							)

								UpperCAmelCase:					Optional[Any] 						=     None if labels is None else self.hf_compute_loss(__lowercase	,				__lowercase							)

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

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




'''simple docstring'''

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class    a__					(			__A				,    unittest.TestCase   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						SpeechaTextaTokenizer
				__UpperCamelCase     :		str						=						False




				def        _snake_case	(self		):
										super().setUp()

										__lowerCAmelCase											=					'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''		)
										__lowerCAmelCase											=					['''he re</w> 123''', '''here a 1456''']
										__lowerCAmelCase											=					dict(zip(__lowercase							,      range(len(__lowercase		)		)		)		)

										__lowerCAmelCase											=					{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__lowerCAmelCase											=					tempfile.mkdtemp()
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''merges_file''']		)

										with open(self.vocab_file							,      '''w'''							,      encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)

										with open(self.merges_file							,      '''w'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]							,      '''<s>'''		)
										self.assertEqual(vocab_keys[1]							,      '''<pad>'''		)
										self.assertEqual(vocab_keys[-1]							,      '''vocab'''		)
										self.assertEqual(len(__lowercase		)							,      14		)




				def        _snake_case	(self		):
										self.assertEqual(self.get_tokenizer().vocab_size							,      14		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										# make sure @@ is correctly concatenated
										__lowerCAmelCase											=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__lowerCAmelCase											=					tokenizer.decode(__lowercase		)

										self.assertTrue(output_string == '''herecouple words ofthe'''		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(__lowercase		)
																os.remove(os.path.join(__lowercase							,      '''merges.txt'''		)		)

																# load tokenizer without merges file should not throw an error
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(__lowercase		)
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										self.assertIsNotNone(__lowercase		)




				def        _snake_case	(self		):
										if not self.test_slow_tokenizer:
																return

										__lowerCAmelCase											=					inspect.signature(self.tokenizer_class.__init__		)
										__lowerCAmelCase											=					self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(__lowercase							,      tokenizer.init_kwargs		)

