

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


A_	:			int  	=      logging.get_logger(__name__)


# General docstring
A_	:			Tuple  	=      'MobileNetV1Config'

# Base docstring
A_	:			Any  	=      'google/mobilenet_v1_1.0_224'
A_	:			List[str]  	=      [1, 1024, 7, 7]

# Image classification docstring
A_	:			Dict  	=      'google/mobilenet_v1_1.0_224'
A_	:			List[Any]  	=      'tabby, tabby cat'


A_	:			Dict  	=      [
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]

def 						UpperCamelCase (lowercase_:			Tuple   ,				lowercase_:			List[str]   ,				lowercase_:			List[Any]=None )					->				Optional[Any]:
	A__							:   Optional[int]					       =		{}

	if isinstance(lowercase_   ,				lowercase_ ):
		A__							:   List[Any]					       =		model.mobilenet_va
	else:
		A__							:   Tuple					       =		model

	A__							:   Tuple					       =		"""MobilenetV1/Conv2d_0/"""
	A__							:   List[Any]					       =		backbone.conv_stem.convolution.weight
	A__							:   Dict					       =		backbone.conv_stem.normalization.bias
	A__							:   Dict					       =		backbone.conv_stem.normalization.weight
	A__							:   str					       =		backbone.conv_stem.normalization.running_mean
	A__							:   int					       =		backbone.conv_stem.normalization.running_var

	for i in range(13 ):
		A__							:   str					       =		i + 1
		A__							:   Union[str, Any]					       =		i * 2

		A__							:   int					       =		backbone.layer[pt_index]
		A__							:   Optional[Any]					       =		f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
		A__							:   Tuple					       =		pointer.convolution.weight
		A__							:   int					       =		pointer.normalization.bias
		A__							:   Dict					       =		pointer.normalization.weight
		A__							:   Optional[Any]					       =		pointer.normalization.running_mean
		A__							:   List[str]					       =		pointer.normalization.running_var

		A__							:   Optional[int]					       =		backbone.layer[pt_index + 1]
		A__							:   str					       =		f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
		A__							:   Dict					       =		pointer.convolution.weight
		A__							:   Dict					       =		pointer.normalization.bias
		A__							:   List[Any]					       =		pointer.normalization.weight
		A__							:   Any					       =		pointer.normalization.running_mean
		A__							:   Tuple					       =		pointer.normalization.running_var

	if isinstance(lowercase_   ,				lowercase_ ):
		A__							:   List[Any]					       =		"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
		A__							:   int					       =		model.classifier.weight
		A__							:   Any					       =		model.classifier.bias

	return tf_to_pt_map

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Any   ,				lowercase_:			Dict )					->				List[Any]:
	try:
		import numpy as np
		import tensorflow as tf
	except ImportError:
		logger.error(
		    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
		    """https://www.tensorflow.org/install/ for installation instructions.""" )
		raise

	# Load weights from TF model
	A__							:   int					       =		tf.train.list_variables(lowercase_ )
	A__							:   Optional[int]					       =		{}
	for name, shape in init_vars:
		logger.info(f"""Loading TF weight {name} with shape {shape}""" )
		A__							:   int					       =		tf.train.load_variable(lowercase_   ,				lowercase_ )
		A__							:   Optional[int]					       =		array

	# Build TF to PyTorch weights loading map
	A__							:   Dict					       =		_build_tf_to_pytorch_map(lowercase_   ,				lowercase_   ,				lowercase_ )

	for name, pointer in tf_to_pt_map.items():
		logger.info(f"""Importing {name}""" )
		if name not in tf_weights:
			logger.info(f"""{name} not in tf pre-trained weights, skipping""" )
			continue

		A__							:   str					       =		tf_weights[name]

		if "depthwise_weights" in name:
			logger.info("""Transposing depthwise""" )
			A__							:   Tuple					       =		np.transpose(lowercase_   ,				(2, 3, 0, 1) )
		elif "weights" in name:
			logger.info("""Transposing""" )
			if len(pointer.shape ) == 2:  # copying into linear layer
				A__							:   Optional[int]					       =		array.squeeze().transpose()
			else:
				A__							:   int					       =		np.transpose(lowercase_   ,				(3, 2, 0, 1) )

		if pointer.shape != array.shape:
			raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )

		logger.info(f"""Initialize PyTorch weight {name} {array.shape}""" )
		A__							:   int					       =		torch.from_numpy(lowercase_ )

		tf_weights.pop(lowercase_   ,				lowercase_ )
		tf_weights.pop(name + """/RMSProp"""   ,				lowercase_ )
		tf_weights.pop(name + """/RMSProp_1"""   ,				lowercase_ )
		tf_weights.pop(name + """/ExponentialMovingAverage"""   ,				lowercase_ )

	logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
	return model

def 						UpperCamelCase (lowercase_:			torch.Tensor   ,				lowercase_:			nn.Convad )					->				torch.Tensor:
	A__							:   List[Any]					       =		features.shape[-2:]
	A__							:   Tuple					       =		conv_layer.stride
	A__							:   str					       =		conv_layer.kernel_size

	if in_height % stride_height == 0:
		A__							:   Tuple					       =		max(kernel_height - stride_height   ,				0 )
	else:
		A__							:   Any					       =		max(kernel_height - (in_height % stride_height)   ,				0 )

	if in_width % stride_width == 0:
		A__							:   Optional[Any]					       =		max(kernel_width - stride_width   ,				0 )
	else:
		A__							:   Tuple					       =		max(kernel_width - (in_width % stride_width)   ,				0 )

	A__							:   Optional[Any]					       =		pad_along_width // 2
	A__							:   List[Any]					       =		pad_along_width - pad_left
	A__							:   Optional[int]					       =		pad_along_height // 2
	A__							:   Union[str, Any]					       =		pad_along_height - pad_top

	A__							:   Tuple					       =		(pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(lowercase_   ,				lowercase_   ,				"""constant"""   ,				0.0 )



class    _a						(nn.Module    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__ = 1   ,	A__ = 1   ,	A__ = False   ,	A__ = True   ,	A__ = True   ,	):
						super().__init__()
						A__							:   Dict					       =		config

						if in_channels % groups != 0:
							raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups."""      )
						if out_channels % groups != 0:
							raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups."""      )

						A__							:   int					       =		0 if config.tf_padding else int((kernel_size - 1) / 2      )

						A__							:   Any					       =		nn.Convad(
						    in_channels=A__   ,	out_channels=A__   ,	kernel_size=A__   ,	stride=A__   ,	padding=A__   ,	groups=A__   ,	bias=A__   ,	padding_mode="""zeros"""   ,	)

						if use_normalization:
							A__							:   Any					       =		nn.BatchNormad(
							    num_features=A__   ,	eps=config.layer_norm_eps   ,	momentum=0.9_9_9_7   ,	affine=A__   ,	track_running_stats=A__   ,	)
						else:
							A__							:   int					       =		None

						if use_activation:
							if isinstance(A__   ,	A__      ):
								A__							:   Optional[Any]					       =		ACTaFN[use_activation]
							elif isinstance(config.hidden_act   ,	A__      ):
								A__							:   Optional[Any]					       =		ACTaFN[config.hidden_act]
							else:
								A__							:   str					       =		config.hidden_act
						else:
							A__							:   Optional[int]					       =		None






					def   __A      (			self   ,	A__      ):
						if self.config.tf_padding:
							A__							:   str					       =		apply_tf_padding(A__   ,	self.convolution      )
						A__							:   Optional[int]					       =		self.convolution(A__      )
						if self.normalization is not None:
							A__							:   Union[str, Any]					       =		self.normalization(A__      )
						if self.activation is not None:
							A__							:   str					       =		self.activation(A__      )
						return features



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	MobileNetVaConfig
					UpperCAmelCase__:       Dict  							=	load_tf_weights_in_mobilenet_va
					UpperCAmelCase__:       List[str]  							=	'''mobilenet_v1'''
					UpperCAmelCase__:       Any  							=	'''pixel_values'''
					UpperCAmelCase__:       Optional[Any]  							=	False






					def   __A      (			self   ,	A__      ):
						if isinstance(A__   ,	(nn.Linear, nn.Convad)      ):
							module.weight.data.normal_(mean=0.0   ,	std=self.config.initializer_range      )
							if module.bias is not None:
								module.bias.data.zero_()
						elif isinstance(A__   ,	nn.BatchNormad      ):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0      )



A_	:			List[Any]  	=      r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

A_	:			Union[str, Any]  	=      r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\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'



@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''		,       __magic_name__		,       )
class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__ = True      ):
						super().__init__(A__      )
						A__							:   Optional[Any]					       =		config

						A__							:   int					       =		32
						A__							:   Any					       =		max(int(depth * config.depth_multiplier      )   ,	config.min_depth      )

						A__							:   int					       =		MobileNetVaConvLayer(
						    A__   ,	in_channels=config.num_channels   ,	out_channels=A__   ,	kernel_size=3   ,	stride=2   ,	)

						A__							:   Dict					       =		[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						A__							:   Optional[Any]					       =		nn.ModuleList()
						for i in range(13      ):
							A__							:   Optional[int]					       =		out_channels

							if strides[i] == 2 or i == 0:
								depth *= 2
								A__							:   int					       =		max(int(depth * config.depth_multiplier      )   ,	config.min_depth      )

							self.layer.append(
							    MobileNetVaConvLayer(
							        A__   ,	in_channels=A__   ,	out_channels=A__   ,	kernel_size=3   ,	stride=strides[i]   ,	groups=A__   ,	)      )

							self.layer.append(
							    MobileNetVaConvLayer(
							        A__   ,	in_channels=A__   ,	out_channels=A__   ,	kernel_size=1   ,	)      )

						A__							:   Optional[Any]					       =		nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

						# Initialize weights and apply final processing
						self.post_init()






					def   __A      (			self   ,	A__      ):
						raise NotImplementedError






					@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   ,	)
					def   __A      (			self   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	):
						A__							:   Union[str, 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

						if pixel_values is None:
							raise ValueError("""You have to specify pixel_values"""      )

						A__							:   Optional[Any]					       =		self.conv_stem(A__      )

						A__							:   Any					       =		() if output_hidden_states else None

						for i, layer_module in enumerate(self.layer      ):
							A__							:   List[str]					       =		layer_module(A__      )

							if output_hidden_states:
								A__							:   Tuple					       =		all_hidden_states + (hidden_states,)

						A__							:   Any					       =		hidden_states

						if self.pooler is not None:
							A__							:   Dict					       =		torch.flatten(self.pooler(A__      )   ,	start_dim=1      )
						else:
							A__							:   str					       =		None

						if not return_dict:
							return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None      )

						return BaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=A__   ,	pooler_output=A__   ,	hidden_states=A__   ,	)



@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,       __magic_name__		,       )
class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__      ):
						super().__init__(A__      )

						A__							:   Dict					       =		config.num_labels
						A__							:   List[Any]					       =		MobileNetVaModel(A__      )

						A__							:   Union[str, Any]					       =		self.mobilenet_va.layer[-1].convolution.out_channels

						# Classifier head
						A__							:   Union[str, Any]					       =		nn.Dropout(config.classifier_dropout_prob   ,	inplace=A__      )
						A__							:   Optional[Any]					       =		nn.Linear(A__   ,	config.num_labels      ) if config.num_labels > 0 else nn.Identity()

						# Initialize weights and apply final processing
						self.post_init()






					@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   ,	)
					def   __A      (			self   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	):
						A__							:   List[Any]					       =		return_dict if return_dict is not None else self.config.use_return_dict

						A__							:   List[str]					       =		self.mobilenet_va(A__   ,	output_hidden_states=A__   ,	return_dict=A__      )

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

						A__							:   Union[str, Any]					       =		self.classifier(self.dropout(A__      )      )

						A__							:   str					       =		None
						if labels is not None:
							if self.config.problem_type is None:
								if self.num_labels == 1:
									A__							:   Dict					       =		"""regression"""
								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
									A__							:   Union[str, Any]					       =		"""single_label_classification"""
								else:
									A__							:   List[Any]					       =		"""multi_label_classification"""

							if self.config.problem_type == "regression":
								A__							:   int					       =		MSELoss()
								if self.num_labels == 1:
									A__							:   Optional[int]					       =		loss_fct(logits.squeeze()   ,	labels.squeeze()      )
								else:
									A__							:   Tuple					       =		loss_fct(A__   ,	A__      )
							elif self.config.problem_type == "single_label_classification":
								A__							:   Tuple					       =		CrossEntropyLoss()
								A__							:   Optional[int]					       =		loss_fct(logits.view(-1   ,	self.num_labels      )   ,	labels.view(-1      )      )
							elif self.config.problem_type == "multi_label_classification":
								A__							:   Optional[int]					       =		BCEWithLogitsLoss()
								A__							:   Tuple					       =		loss_fct(A__   ,	A__      )

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

						return ImageClassifierOutputWithNoAttention(
						    loss=A__   ,	logits=A__   ,	hidden_states=outputs.hidden_states   ,	)



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	):
						A__							:   Any					       =		parent
						A__							:   Any					       =		13
						A__							:   Optional[Any]					       =		7
						A__							:   Union[str, Any]					       =		30
						A__							:   str					       =		self.seq_length + self.mem_len
						A__							:   Dict					       =		15
						A__							:   int					       =		True
						A__							:   Tuple					       =		True
						A__							:   Union[str, Any]					       =		99
						A__							:   Optional[Any]					       =		[10, 50, 80]
						A__							:   str					       =		32
						A__							:   Tuple					       =		32
						A__							:   Union[str, Any]					       =		4
						A__							:   Optional[Any]					       =		8
						A__							:   int					       =		128
						A__							:   List[Any]					       =		2
						A__							:   List[str]					       =		2
						A__							:   int					       =		None
						A__							:   List[str]					       =		1
						A__							:   Union[str, Any]					       =		0
						A__							:   List[str]					       =		3
						A__							:   int					       =		self.vocab_size - 1
						A__							:   Optional[Any]					       =		0.0_1






					def   __A      (			self      ):
						A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Optional[Any]					       =		None
						if self.use_labels:
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Any					       =		TransfoXLConfig(
						    vocab_size=self.vocab_size   ,	mem_len=self.mem_len   ,	clamp_len=self.clamp_len   ,	cutoffs=self.cutoffs   ,	d_model=self.hidden_size   ,	d_embed=self.d_embed   ,	n_head=self.num_attention_heads   ,	d_head=self.d_head   ,	d_inner=self.d_inner   ,	div_val=self.div_val   ,	n_layer=self.num_hidden_layers   ,	eos_token_id=self.eos_token_id   ,	pad_token_id=self.vocab_size - 1   ,	init_range=self.init_range   ,	num_labels=self.num_labels   ,	)

						return (config, input_ids_a, input_ids_a, lm_labels)






					def   __A      (			self      ):
						random.seed(self.seed      )
						tf.random.set_seed(self.seed      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Dict					       =		TFTransfoXLModel(A__      )

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						A__							:   List[str]					       =		{"""input_ids""": input_ids_a, """mems""": mems_a}

						A__       ,       A__							:   str					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertEqual(hidden_states_a.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Optional[int]					       =		TFTransfoXLLMHeadModel(A__      )

						A__       ,       A__							:   int					       =		model(A__      ).to_tuple()

						A__							:   int					       =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
						A__       ,       A__							:   Optional[Any]					       =		model(A__      ).to_tuple()

						A__       ,       A__							:   Union[str, Any]					       =		model([input_ids_a, mems_a]      ).to_tuple()

						A__							:   Any					       =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

						A__       ,       A__							:   Tuple					       =		model(A__      ).to_tuple()

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)

						self.parent.assertEqual(lm_logits_a.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )
						self.parent.assertListEqual(
						    [mem.shape for mem in mems_a]   ,	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Any					       =		TFTransfoXLForSequenceClassification(A__      )
						A__							:   Optional[Any]					       =		model(A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.prepare_config_and_inputs()
						((A__)       ,       (A__)       ,       (A__)       ,       (A__))							:   List[Any]					       =		config_and_inputs
						A__							:   int					       =		{"""input_ids""": input_ids_a}
						return config, inputs_dict



@require_tf
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					UpperCAmelCase__:       Optional[Any]  							=	() if is_tf_available() else ()
					UpperCAmelCase__:       int  							=	(
					    {
					        '''feature-extraction''': TFTransfoXLModel,
					        '''text-classification''': TFTransfoXLForSequenceClassification,
					        '''text-generation''': TFTransfoXLLMHeadModel,
					        '''zero-shot''': TFTransfoXLForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Optional[int]  							=	False
					UpperCAmelCase__:       Tuple  							=	False
					UpperCAmelCase__:       List[str]  							=	False






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
							# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
							# tokenizer.
							return True

						return False






					def   __A      (			self      ):
						A__							:   Tuple					       =		TFTransfoXLModelTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	d_embed=37      )






					def   __A      (			self      ):
						self.config_tester.run_common_tests()






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_model(*A__      )






					def   __A      (			self      ):
						self.model_tester.set_seed()
						A__							:   Any					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__      )






					def   __A      (			self      ):
						A__       ,       A__							:   int					       =		self.model_tester.prepare_config_and_inputs_for_common()
						A__							:   Union[str, Any]					       =		[TFTransfoXLForSequenceClassification]

						for model_class in self.all_model_classes:
							A__							:   Any					       =		model_class(A__      )
							assert isinstance(model.get_input_embeddings()   ,	tf.keras.layers.Layer      )
							if model_class in list_other_models_with_output_ebd:
								A__							:   Optional[Any]					       =		model.get_output_embeddings()
								assert isinstance(A__   ,	tf.keras.layers.Layer      )
								A__							:   Tuple					       =		model.get_bias()
								assert name is None
							else:
								A__							:   Dict					       =		model.get_output_embeddings()
								assert x is None
								A__							:   int					       =		model.get_bias()
								assert name is None






					def   __A      (			self      ):
						# TODO JP: Make TransfoXL XLA compliant
						pass






					@slow
					def   __A      (			self      ):
						for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   List[Any]					       =		TFTransfoXLModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""      )
					def   __A      (			self      ):
						pass



@require_tf
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@unittest.skip("""Skip test until #12651 is resolved."""      )
					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""      )
						# fmt: off
						A__							:   Tuple					       =		tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,	dtype=tf.intaa      )  # noqa: E231
						# fmt: on
						#  In 1991 , the remains of Russian Tsar Nicholas II and his family
						#  ( except for Alexei and Maria ) are discovered .
						#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
						#  remainder of the story . 1883 Western Siberia ,
						#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
						#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
						#  father initially slaps him for making such an accusation , Rasputin watches as the
						#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
						#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
						#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

						# fmt: off
						A__							:   Dict					       =		[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
						# fmt: on
						#  In 1991, the remains of Russian Tsar Nicholas II and his family (
						#  except for Alexei and Maria ) are discovered. The voice of young son,
						#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
						#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
						#  and a group of men to perform magic. Rasputin has a vision and
						#  denounces one of the men as a horse thief. Although his father initially
						#  slaps him for making such an accusation, Rasputin watches as the man
						#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
						#  of the Virgin Mary, prompting him to become a priest.
						#  Rasputin quickly becomes famous, with people, even a bishop, begging for
						#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
						# Nicholas II and his family were discovered. The voice of <unk> young son,
						# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

						A__							:   Any					       =		model.generate(A__   ,	max_length=200   ,	do_sample=A__      )
						self.assertListEqual(output_ids[0].numpy().tolist()   ,	A__      )



