


"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__magic_name__						: List[Any]    =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

__magic_name__						: int    =				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__magic_name__						: Dict    =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def       a_ (	lowercase__ :str     ):
			with open(lowercase__,			"""rb"""     ) as f:
						__lowerCamelCase          =      Image.open(lowercase__     )
						return im.convert("""RGB"""     )




@dataclass
class 			__snake_case   :
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={
							        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
							    }			,		)
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
							__a					 =       field(default=lowerCamelCase			,		metadata={'''help''': '''A folder containing the training data.'''}  )
							__a					 =       field(default=lowerCamelCase			,		metadata={'''help''': '''A folder containing the validation data.'''}  )
							__a					 =       field(
							    default=0.15			,		metadata={'''help''': '''Percent to split off of train for validation.'''}  )
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }			,		)
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }			,		)




							def   __a   (     self:    int   ):
										if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
													raise ValueError(
													    """You must specify either a dataset name from the hub or a train and/or validation directory."""   )




@dataclass
class 			__snake_case   :
							__a					 =       field(
							    default='''google/vit-base-patch16-224-in21k'''			,		metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			,		)
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCamelCase  )}			,		)
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}  )
							__a					 =       field(
							    default='''main'''			,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,		)
							__a					 =       field(default=lowerCamelCase			,		metadata={'''help''': '''Name or path of preprocessor config.'''}  )
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }			,		)
							__a					 =       field(
							    default=lowerCamelCase			,		metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}			,		)





def       a_ (	lowercase__ :int     ):
			__lowerCamelCase          =      torch.stack([example["""pixel_values"""] for example in examples]     )
			__lowerCamelCase          =      torch.tensor([example["""labels"""] for example in examples]     )
			return {"pixel_values": pixel_values, "labels": labels}





def       a_ (	):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowerCamelCase          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
			if len(sys.argv     ) == 2 and sys.argv[1].endswith(""".json"""     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_image_classification""",			lowercase__,			lowercase__     )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",			datefmt="""%m/%d/%Y %H:%M:%S""",			handlers=[logging.StreamHandler(sys.stdout     )],			)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			__lowerCamelCase          =      training_args.get_process_log_level()
			logger.setLevel(lowercase__     )
			transformers.utils.logging.set_verbosity(lowercase__     )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
			    + f'distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'     )
			logger.info(f'Training/evaluation parameters {training_args}'     )

			# Detecting last checkpoint.
			__lowerCamelCase          =      None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowerCamelCase          =      get_last_checkpoint(training_args.output_dir     )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
									raise ValueError(
									    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
									    """Use --overwrite_output_dir to overcome."""     )
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
									    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""     )

    # Set seed before initializing model.
			set_seed(training_args.seed     )

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						__lowerCamelCase          =      load_dataset(
						    data_args.dataset_name,			data_args.dataset_config_name,			cache_dir=model_args.cache_dir,			task="""image-classification""",			use_auth_token=True if model_args.use_auth_token else None,			)
			else:
						__lowerCamelCase          =      {}
						if data_args.train_dir is not None:
									__lowerCamelCase          =      os.path.join(data_args.train_dir,			"""**"""     )
						if data_args.validation_dir is not None:
									__lowerCamelCase          =      os.path.join(data_args.validation_dir,			"""**"""     )
						__lowerCamelCase          =      load_dataset(
						    """imagefolder""",			data_files=lowercase__,			cache_dir=model_args.cache_dir,			task="""image-classification""",			)

			# If we don't have a validation split, split off a percentage of train as validation.
			__lowerCamelCase          =      None if """validation""" in dataset.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split,			lowercase__     ) and data_args.train_val_split > 0.0:
						__lowerCamelCase          =      dataset["""train"""].train_test_split(data_args.train_val_split     )
						__lowerCamelCase          =      split["""train"""]
						__lowerCamelCase          =      split["""test"""]

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			__lowerCamelCase          =      dataset["""train"""].features["""labels"""].names
			__lowerCamelCase     ,__lowerCamelCase          =      {}, {}
			for i, label in enumerate(lowercase__     ):
						__lowerCamelCase          =      str(lowercase__     )
						__lowerCamelCase          =      label

			# Load the accuracy metric from the datasets package
			__lowerCamelCase          =      evaluate.load("""accuracy"""     )

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(lowercase__ :Tuple     ):
						return metric.compute(predictions=np.argmax(p.predictions,			axis=1     ),			references=p.label_ids     )

			__lowerCamelCase          =      AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path,			num_labels=len(lowercase__     ),			labelaid=lowercase__,			idalabel=lowercase__,			finetuning_task="""image-classification""",			cache_dir=model_args.cache_dir,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			)
			__lowerCamelCase          =      AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path     ),			config=lowercase__,			cache_dir=model_args.cache_dir,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,			)
			__lowerCamelCase          =      AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path,			cache_dir=model_args.cache_dir,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			)

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						__lowerCamelCase          =      image_processor.size["""shortest_edge"""]
			else:
						__lowerCamelCase          =      (image_processor.size["""height"""], image_processor.size["""width"""])
			__lowerCamelCase          =      Normalize(mean=image_processor.image_mean,			std=image_processor.image_std     )
			__lowerCamelCase          =      Compose(
			    [
			        RandomResizedCrop(lowercase__     ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]     )
			__lowerCamelCase          =      Compose(
			    [
			        Resize(lowercase__     ),
			        CenterCrop(lowercase__     ),
			        ToTensor(),
			        normalize,
			    ]     )

			def train_transforms(lowercase__ :Any     ):
						__lowerCamelCase          =      [
						    _train_transforms(pil_img.convert("""RGB"""     )     ) for pil_img in example_batch["""image"""]
						]
						return example_batch

			def val_transforms(lowercase__ :int     ):
						__lowerCamelCase          =      [_val_transforms(pil_img.convert("""RGB"""     )     ) for pil_img in example_batch["""image"""]]
						return example_batch

			if training_args.do_train:
						if "train" not in dataset:
									raise ValueError("""--do_train requires a train dataset"""     )
						if data_args.max_train_samples is not None:
									__lowerCamelCase          =      (
									    dataset["""train"""].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
									)
						# Set the training transforms
						dataset["train"].set_transform(lowercase__     )

			if training_args.do_eval:
						if "validation" not in dataset:
									raise ValueError("""--do_eval requires a validation dataset"""     )
						if data_args.max_eval_samples is not None:
									__lowerCamelCase          =      (
									    dataset["""validation"""].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
									)
						# Set the validation transforms
						dataset["validation"].set_transform(lowercase__     )

			# Initalize our trainer
			__lowerCamelCase          =      Trainer(
			    model=lowercase__,			args=lowercase__,			train_dataset=dataset["""train"""] if training_args.do_train else None,			eval_dataset=dataset["""validation"""] if training_args.do_eval else None,			compute_metrics=lowercase__,			tokenizer=lowercase__,			data_collator=lowercase__,			)

			# Training
			if training_args.do_train:
						__lowerCamelCase          =      None
						if training_args.resume_from_checkpoint is not None:
									__lowerCamelCase          =      training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									__lowerCamelCase          =      last_checkpoint
						__lowerCamelCase          =      trainer.train(resume_from_checkpoint=lowercase__     )
						trainer.save_model()
						trainer.log_metrics("""train""",			train_result.metrics     )
						trainer.save_metrics("""train""",			train_result.metrics     )
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						__lowerCamelCase          =      trainer.evaluate()
						trainer.log_metrics("""eval""",			lowercase__     )
						trainer.save_metrics("""eval""",			lowercase__     )

			# Write model card and (optionally) push to hub
			__lowerCamelCase          =      {
			    """finetuned_from""": model_args.model_name_or_path,
			    """tasks""": """image-classification""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""image-classification""", """vision"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**lowercase__     )
			else:
						trainer.create_model_card(**lowercase__     )


if __name__ == "__main__":
					main()








"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)




class 			__snake_case   :




							def __init__(     self:    Any     ,							A_:    str     ,							A_:    str=13     ,							A_:    Dict=7     ,							A_:    Optional[int]=True     ,							A_:    int=True     ,							A_:    List[str]=True     ,							A_:    Any=True     ,							A_:    List[Any]=99     ,							A_:    Tuple=32     ,							A_:    Union[str, Any]=2     ,							A_:    Dict=4     ,							A_:    Tuple=37     ,							A_:    Dict="gelu"     ,							A_:    List[Any]=0.1     ,							A_:    int=0.1     ,							A_:    Union[str, Any]=5_12     ,							A_:    List[Any]=16     ,							A_:    Optional[Any]=2     ,							A_:    int=0.02     ,							A_:    List[str]=3     ,							A_:    Optional[int]=4     ,							A_:    List[str]=None     ,							):
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      13
										__lowerCamelCase          =      7
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      99
										__lowerCamelCase          =      32
										__lowerCamelCase          =      2
										__lowerCamelCase          =      4
										__lowerCamelCase          =      37
										__lowerCamelCase          =      """gelu"""
										__lowerCamelCase          =      0.1
										__lowerCamelCase          =      0.1
										__lowerCamelCase          =      5_12
										__lowerCamelCase          =      16
										__lowerCamelCase          =      2
										__lowerCamelCase          =      0.02
										__lowerCamelCase          =      3
										__lowerCamelCase          =      4
										__lowerCamelCase          =      None




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size   )

										__lowerCamelCase          =      None
										if self.use_input_mask:
													__lowerCamelCase          =      random_attention_mask([self.batch_size, self.seq_length]   )

										__lowerCamelCase          =      None
										if self.use_token_type_ids:
													__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size   )

										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										if self.use_labels:
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.type_sequence_label_size   )
													__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels   )
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.num_choices   )

										__lowerCamelCase          =      RoFormerConfig(
										    vocab_size=self.vocab_size     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							return_dict=A_     ,							)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def   __a   (     self:    Union[str, Any]     ,							A_:    str     ,							A_:    Dict     ,							A_:    Union[str, Any]     ,							A_:    Optional[int]     ,							A_:    Any     ,							A_:    Dict     ,							A_:    int   ):
										__lowerCamelCase          =      TFRoFormerModel(config=A_   )
										__lowerCamelCase          =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										__lowerCamelCase          =      [input_ids, input_mask]
										__lowerCamelCase          =      model(A_   )

										__lowerCamelCase          =      model(A_   )

										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)   )




							def   __a   (     self:    List[str]     ,							A_:    int     ,							A_:    str     ,							A_:    Any     ,							A_:    int     ,							A_:    str     ,							A_:    str     ,							A_:    Union[str, Any]   ):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      TFRoFormerForCausalLM(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )["""logits"""]
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape   )     ,							[self.batch_size, self.seq_length, self.vocab_size]   )




							def   __a   (     self:    Any     ,							A_:    Optional[Any]     ,							A_:    Optional[Any]     ,							A_:    Union[str, Any]     ,							A_:    str     ,							A_:    str     ,							A_:    Optional[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      TFRoFormerForMaskedLM(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)   )




							def   __a   (     self:    Optional[Any]     ,							A_:    Dict     ,							A_:    Union[str, Any]     ,							A_:    List[Any]     ,							A_:    str     ,							A_:    int     ,							A_:    Optional[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      TFRoFormerForSequenceClassification(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)   )




							def   __a   (     self:    List[str]     ,							A_:    Tuple     ,							A_:    List[str]     ,							A_:    Union[str, Any]     ,							A_:    Optional[Any]     ,							A_:    List[str]     ,							A_:    Optional[int]     ,							A_:    List[Any]   ):
										__lowerCamelCase          =      self.num_choices
										__lowerCamelCase          =      TFRoFormerForMultipleChoice(config=A_   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      {
										    """input_ids""": multiple_choice_inputs_ids,
										    """attention_mask""": multiple_choice_input_mask,
										    """token_type_ids""": multiple_choice_token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_choices)   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    Dict     ,							A_:    List[Any]     ,							A_:    List[Any]     ,							A_:    Union[str, Any]     ,							A_:    str     ,							A_:    Union[str, Any]     ,							A_:    List[str]   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      TFRoFormerForTokenClassification(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)   )




							def   __a   (     self:    int     ,							A_:    Union[str, Any]     ,							A_:    Any     ,							A_:    Any     ,							A_:    Dict     ,							A_:    str     ,							A_:    int     ,							A_:    Tuple   ):
										__lowerCamelCase          =      TFRoFormerForQuestionAnswering(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.start_logits.shape     ,							(self.batch_size, self.seq_length)   )
										self.parent.assertEqual(result.end_logits.shape     ,							(self.batch_size, self.seq_length)   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										(
										    (
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,
										)          =      config_and_inputs
										__lowerCamelCase          =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict




@require_tf
class 			__snake_case   (lowerCamelCase			,		lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							__a					 =       (
							    {
							        '''feature-extraction''': TFRoFormerModel,
							        '''fill-mask''': TFRoFormerForMaskedLM,
							        '''question-answering''': TFRoFormerForQuestionAnswering,
							        '''text-classification''': TFRoFormerForSequenceClassification,
							        '''text-generation''': TFRoFormerForCausalLM,
							        '''token-classification''': TFRoFormerForTokenClassification,
							        '''zero-shot''': TFRoFormerForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							__a					 =       False
							__a					 =       False




							def   __a   (     self:    List[str]     ,							A_:    List[Any]     ,							A_:    List[str]     ,							A_:    str     ,							A_:    Union[str, Any]     ,							A_:    Dict   ):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
													return True

										return False




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      TFRoFormerModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=A_     ,							hidden_size=37   )




							def   __a   (     self:    int   ):
										self.config_tester.run_common_tests()




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*A_   )




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*A_   )




							@slow
							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""   )
										self.assertIsNotNone(A_   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):




							@slow
							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
										__lowerCamelCase          =      tf.constant([[0, 1, 2, 3, 4, 5]]   )
										__lowerCamelCase          =      model(A_   )[0]

										# TODO Replace vocab size
										__lowerCamelCase          =      5_00_00

										__lowerCamelCase          =      [1, 6, vocab_size]
										self.assertEqual(output.shape     ,							A_   )

										print(output[:, :3, :3]   )

										# TODO Replace values below with what was printed above.
										__lowerCamelCase          =      tf.constant(
										    [
										        [
										            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
										            [-1.5_133_783, 0.197_433, 0.15_190_607],
										            [-5.0_135_403, -3.900_256, -0.84_038_764],
										        ]
										    ]   )
										tf.debugging.assert_near(output[:, :3, :3]     ,							A_     ,							atol=1E-4   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):
							__a					 =       1e-4




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      tf.constant([[4, 10]]   )
										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,							embedding_dim=6   )

										__lowerCamelCase          =      emba(input_ids.shape   )
										__lowerCamelCase          =      tf.constant(
										    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]   )

										tf.debugging.assert_near(A_     ,							A_     ,							atol=self.tolerance   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
										        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
										        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
										    ]   )
										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12     ,							embedding_dim=5_12   )
										emba([2, 16, 5_12]   )
										__lowerCamelCase          =      emba.weight[:3, :5]

										tf.debugging.assert_near(A_     ,							A_     ,							atol=self.tolerance   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):
							__a					 =       1e-4




							def   __a   (     self:    str   ):
										# 2,12,16,64
										__lowerCamelCase          =      tf.reshape(tf.range(2 * 12 * 16 * 64     ,							dtype=tf.floataa   )     ,							shape=(2, 12, 16, 64)   ) / 1_00

										__lowerCamelCase          =      -tf.reshape(tf.range(2 * 12 * 16 * 64     ,							dtype=tf.floataa   )     ,							shape=(2, 12, 16, 64)   ) / 1_00

										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,							embedding_dim=64   )
										__lowerCamelCase          =      embed_positions([2, 16, 7_68]   )[None, None, :, :]

										__lowerCamelCase     ,__lowerCamelCase          =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    A_     ,							A_     ,							A_   )

										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
										        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
										        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
										        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
										        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
										        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
										    ]   )
										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
										        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
										        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
										        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
										        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
										        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
										    ]   )

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,							A_     ,							atol=self.tolerance   )
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,							A_     ,							atol=self.tolerance   )




