

"""simple docstring"""




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_				:      Union[str, Any]           =			"MobileNetV1Config"

# Base docstring
A_				:      Any           =			"google/mobilenet_v1_1.0_224"
A_				:      int           =			[1, 1_024, 7, 7]

# Image classification docstring
A_				:      Dict           =			"google/mobilenet_v1_1.0_224"
A_				:      Tuple           =			"tabby, tabby cat"


A_				:      List[Any]           =			[
    "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 						A							(  snake_case__		,					snake_case__		,					snake_case__=None     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   {}

			if isinstance(snake_case__		,					snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   model.mobilenet_va
			else:
						SCREAMING_SNAKE_CASE__										=   model

			SCREAMING_SNAKE_CASE__										=   """MobilenetV1/Conv2d_0/"""
			SCREAMING_SNAKE_CASE__										=   backbone.conv_stem.convolution.weight
			SCREAMING_SNAKE_CASE__										=   backbone.conv_stem.normalization.bias
			SCREAMING_SNAKE_CASE__										=   backbone.conv_stem.normalization.weight
			SCREAMING_SNAKE_CASE__										=   backbone.conv_stem.normalization.running_mean
			SCREAMING_SNAKE_CASE__										=   backbone.conv_stem.normalization.running_var

			for i in range(13     ):
						SCREAMING_SNAKE_CASE__										=   i + 1
						SCREAMING_SNAKE_CASE__										=   i * 2

						SCREAMING_SNAKE_CASE__										=   backbone.layer[pt_index]
						SCREAMING_SNAKE_CASE__										=   f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
						SCREAMING_SNAKE_CASE__										=   pointer.convolution.weight
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.bias
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.weight
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.running_mean
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.running_var

						SCREAMING_SNAKE_CASE__										=   backbone.layer[pt_index + 1]
						SCREAMING_SNAKE_CASE__										=   f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
						SCREAMING_SNAKE_CASE__										=   pointer.convolution.weight
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.bias
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.weight
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.running_mean
						SCREAMING_SNAKE_CASE__										=   pointer.normalization.running_var

			if isinstance(snake_case__		,					snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   """MobilenetV1/Logits/Conv2d_1c_1x1/"""
						SCREAMING_SNAKE_CASE__										=   model.classifier.weight
						SCREAMING_SNAKE_CASE__										=   model.classifier.bias

			return tf_to_pt_map

def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			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
			SCREAMING_SNAKE_CASE__										=   tf.train.list_variables(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   {}
			for name, shape in init_vars:
						logger.info(f"""Loading TF weight {name} with shape {shape}"""     )
						SCREAMING_SNAKE_CASE__										=   tf.train.load_variable(snake_case__		,					snake_case__     )
						SCREAMING_SNAKE_CASE__										=   array

			# Build TF to PyTorch weights loading map
			SCREAMING_SNAKE_CASE__										=   _build_tf_to_pytorch_map(snake_case__		,					snake_case__		,					snake_case__     )

			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

						SCREAMING_SNAKE_CASE__										=   tf_weights[name]

						if "depthwise_weights" in name:
									logger.info("""Transposing depthwise"""     )
									SCREAMING_SNAKE_CASE__										=   np.transpose(snake_case__		,					(2, 3, 0, 1)     )
						elif "weights" in name:
									logger.info("""Transposing"""     )
									if len(pointer.shape     ) == 2:  # copying into linear layer
												SCREAMING_SNAKE_CASE__										=   array.squeeze().transpose()
									else:
												SCREAMING_SNAKE_CASE__										=   np.transpose(snake_case__		,					(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}"""     )
						SCREAMING_SNAKE_CASE__										=   torch.from_numpy(snake_case__     )

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

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

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   features.shape[-2:]
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   conv_layer.stride
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   conv_layer.kernel_size

			if in_height % stride_height == 0:
						SCREAMING_SNAKE_CASE__										=   max(kernel_height - stride_height		,					0     )
			else:
						SCREAMING_SNAKE_CASE__										=   max(kernel_height - (in_height % stride_height)		,					0     )

			if in_width % stride_width == 0:
						SCREAMING_SNAKE_CASE__										=   max(kernel_width - stride_width		,					0     )
			else:
						SCREAMING_SNAKE_CASE__										=   max(kernel_width - (in_width % stride_width)		,					0     )

			SCREAMING_SNAKE_CASE__										=   pad_along_width // 2
			SCREAMING_SNAKE_CASE__										=   pad_along_width - pad_left
			SCREAMING_SNAKE_CASE__										=   pad_along_height // 2
			SCREAMING_SNAKE_CASE__										=   pad_along_height - pad_top

			SCREAMING_SNAKE_CASE__										=   (pad_left, pad_right, pad_top, pad_bottom)
			return nn.functional.pad(snake_case__		,					snake_case__		,					"""constant"""		,					0.0     )





class  lowerCamelCase    (nn.Module      ):

			def __init__(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    MobileNetVaConfig     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Optional[int] = 1     ,					__UpperCAmelCase					:    Optional[int] = 1     ,					__UpperCAmelCase					:    bool = False     ,					__UpperCAmelCase					:    Optional[bool] = True     ,					__UpperCAmelCase					:    Optional[bool or str] = True     ,					) ->     None:
						super().__init__()
						SCREAMING_SNAKE_CASE__										=   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."""							)

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

						SCREAMING_SNAKE_CASE__										=   nn.Convad(
						    in_channels=__UpperCAmelCase     ,					out_channels=__UpperCAmelCase     ,					kernel_size=__UpperCAmelCase     ,					stride=__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					groups=__UpperCAmelCase     ,					bias=__UpperCAmelCase     ,					padding_mode="""zeros"""     ,					)

						if use_normalization:
									SCREAMING_SNAKE_CASE__										=   nn.BatchNormad(
									    num_features=__UpperCAmelCase     ,					eps=config.layer_norm_eps     ,					momentum=0.9_997     ,					affine=__UpperCAmelCase     ,					track_running_stats=__UpperCAmelCase     ,					)
						else:
									SCREAMING_SNAKE_CASE__										=   None

						if use_activation:
									if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   ACTaFN[use_activation]
									elif isinstance(config.hidden_act     ,					__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   ACTaFN[config.hidden_act]
									else:
												SCREAMING_SNAKE_CASE__										=   config.hidden_act
						else:
									SCREAMING_SNAKE_CASE__										=   None





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    torch.Tensor							) ->     torch.Tensor:
						if self.config.tf_padding:
									SCREAMING_SNAKE_CASE__										=   apply_tf_padding(__UpperCAmelCase     ,					self.convolution							)
						SCREAMING_SNAKE_CASE__										=   self.convolution(__UpperCAmelCase							)
						if self.normalization is not None:
									SCREAMING_SNAKE_CASE__										=   self.normalization(__UpperCAmelCase							)
						if self.activation is not None:
									SCREAMING_SNAKE_CASE__										=   self.activation(__UpperCAmelCase							)
						return features





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		List[str]       		=				MobileNetVaConfig
			lowerCamelCase__ :		Optional[Any]       		=				load_tf_weights_in_mobilenet_va
			lowerCamelCase__ :		Optional[Any]       		=				'mobilenet_v1'
			lowerCamelCase__ :		Optional[Any]       		=				'pixel_values'
			lowerCamelCase__ :		List[Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Union[nn.Linear, nn.Convad]							) ->     None:
						if isinstance(__UpperCAmelCase     ,					(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(__UpperCAmelCase     ,					nn.BatchNormad							):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0							)
A_				:      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_				:      List[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.'      ,A__      ,)
class  lowerCamelCase    (A__      ):

			def __init__(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    MobileNetVaConfig     ,					__UpperCAmelCase					:    bool = True							) ->     Optional[int]:
						super().__init__(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   config

						SCREAMING_SNAKE_CASE__										=   3_2
						SCREAMING_SNAKE_CASE__										=   max(int(depth * config.depth_multiplier							)     ,					config.min_depth							)

						SCREAMING_SNAKE_CASE__										=   MobileNetVaConvLayer(
						    __UpperCAmelCase     ,					in_channels=config.num_channels     ,					out_channels=__UpperCAmelCase     ,					kernel_size=3     ,					stride=2     ,					)

						SCREAMING_SNAKE_CASE__										=   [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						SCREAMING_SNAKE_CASE__										=   nn.ModuleList()
						for i in range(1_3							):
									SCREAMING_SNAKE_CASE__										=   out_channels

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

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

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

						SCREAMING_SNAKE_CASE__										=   nn.AdaptiveAvgPoolad((1, 1)							) if add_pooling_layer else None

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     Dict:
						raise NotImplementedError





			@add_start_docstrings_to_model_forward(__UpperCAmelCase							)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC     ,					output_type=__UpperCAmelCase     ,					config_class=_CONFIG_FOR_DOC     ,					modality="""vision"""     ,					expected_output=_EXPECTED_OUTPUT_SHAPE     ,					)
			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Optional[torch.Tensor] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					) ->     Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
						SCREAMING_SNAKE_CASE__										=   (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						SCREAMING_SNAKE_CASE__										=   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"""							)

						SCREAMING_SNAKE_CASE__										=   self.conv_stem(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   () if output_hidden_states else None

						for i, layer_module in enumerate(self.layer							):
									SCREAMING_SNAKE_CASE__										=   layer_module(__UpperCAmelCase							)

									if output_hidden_states:
												SCREAMING_SNAKE_CASE__										=   all_hidden_states + (hidden_states,)

						SCREAMING_SNAKE_CASE__										=   hidden_states

						if self.pooler is not None:
									SCREAMING_SNAKE_CASE__										=   torch.flatten(self.pooler(__UpperCAmelCase							)     ,					start_dim=1							)
						else:
									SCREAMING_SNAKE_CASE__										=   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=__UpperCAmelCase     ,					pooler_output=__UpperCAmelCase     ,					hidden_states=__UpperCAmelCase     ,					)





@add_start_docstrings(
    '\n    MobileNetV1 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__      ):

			def __init__(       self					:    int     ,					__UpperCAmelCase					:    MobileNetVaConfig							) ->     None:
						super().__init__(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   config.num_labels
						SCREAMING_SNAKE_CASE__										=   MobileNetVaModel(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   self.mobilenet_va.layer[-1].convolution.out_channels

						# Classifier head
						SCREAMING_SNAKE_CASE__										=   nn.Dropout(config.classifier_dropout_prob     ,					inplace=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   nn.Linear(__UpperCAmelCase     ,					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(__UpperCAmelCase							)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,					output_type=__UpperCAmelCase     ,					config_class=_CONFIG_FOR_DOC     ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,					)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    Optional[torch.Tensor] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					__UpperCAmelCase					:    Optional[torch.Tensor] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					) ->     Union[tuple, ImageClassifierOutputWithNoAttention]:
						SCREAMING_SNAKE_CASE__										=   return_dict if return_dict is not None else self.config.use_return_dict

						SCREAMING_SNAKE_CASE__										=   self.mobilenet_va(__UpperCAmelCase     ,					output_hidden_states=__UpperCAmelCase     ,					return_dict=__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   self.classifier(self.dropout(__UpperCAmelCase							)							)

						SCREAMING_SNAKE_CASE__										=   None
						if labels is not None:
									if self.config.problem_type is None:
												if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__										=   """regression"""
												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															SCREAMING_SNAKE_CASE__										=   """single_label_classification"""
												else:
															SCREAMING_SNAKE_CASE__										=   """multi_label_classification"""

									if self.config.problem_type == "regression":
												SCREAMING_SNAKE_CASE__										=   MSELoss()
												if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__										=   loss_fct(logits.squeeze()     ,					labels.squeeze()							)
												else:
															SCREAMING_SNAKE_CASE__										=   loss_fct(__UpperCAmelCase     ,					__UpperCAmelCase							)
									elif self.config.problem_type == "single_label_classification":
												SCREAMING_SNAKE_CASE__										=   CrossEntropyLoss()
												SCREAMING_SNAKE_CASE__										=   loss_fct(logits.view(-1     ,					self.num_labels							)     ,					labels.view(-1							)							)
									elif self.config.problem_type == "multi_label_classification":
												SCREAMING_SNAKE_CASE__										=   BCEWithLogitsLoss()
												SCREAMING_SNAKE_CASE__										=   loss_fct(__UpperCAmelCase     ,					__UpperCAmelCase							)

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

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





"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


