
'''simple docstring'''

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=3							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=2_24							,		lowerCAmelCase_=10_00							,		lowerCAmelCase_=[3, 3, 6, 4]							,		lowerCAmelCase_=[48, 56, 1_12, 2_20]							,		):

       '''simple docstring'''

       a_      :	int      =							parent
       a_      :	List[str]      =							batch_size
       a_      :	int      =							num_channels
       a_      :	Tuple      =							is_training
       a_      :	Tuple      =							use_labels
       a_      :	Optional[int]      =							hidden_dropout_prob
       a_      :	int      =							attention_probs_dropout_prob
       a_      :	int      =							num_labels
       a_      :	Dict      =							image_size
       a_      :	Tuple      =							layer_depths
       a_      :	List[str]      =							embed_dims


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       a_      :	Dict      =							None
       if self.use_labels:
         a_      :	Optional[Any]      =							ids_tensor([self.batch_size]							,		self.num_labels )

       a_      :	Optional[Any]      =							self.get_config()

       return config, pixel_values, labels


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return SwiftFormerConfig(
           depths=self.layer_depths							,		embed_dims=self.embed_dims							,		mlp_ratio=4							,		downsamples=[True, True, True, True]							,		hidden_act="""gelu"""							,		num_labels=self.num_labels							,		down_patch_size=3							,		down_stride=2							,		down_pad=1							,		drop_rate=0.0							,		drop_path_rate=0.0							,		use_layer_scale=lowerCAmelCase_							,		layer_scale_init_value=1E-5							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Dict      =							SwiftFormerModel(config=lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	Dict      =							model(lowerCAmelCase_ )
       self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.embed_dims[-1], 7, 7) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							self.num_labels
       a_      :	Tuple      =							SwiftFormerForImageClassification(lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()
       a_      :	List[str]      =							model(lowerCAmelCase_							,		labels=lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )

       a_      :	int      =							SwiftFormerForImageClassification(lowerCAmelCase_ )
       model.to(lowerCAmelCase_ )
       model.eval()

       a_      :	List[Any]      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
       a_      :	Dict      =							model(lowerCAmelCase_ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       ((a_)      , (a_)      , (a_))      :	int      =							self.prepare_config_and_inputs()
       a_      :	Dict      =							{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     a_    	=					(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							SwiftFormerModelTester(self )
       a_      :	Tuple      =							ConfigTester(
           self							,		config_class=lowerCAmelCase_							,		has_text_modality=lowerCAmelCase_							,		hidden_size=37							,		num_attention_heads=12							,		num_hidden_layers=12							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.config_tester.run_common_tests()


     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	str      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	Dict      =							model_class(lowerCAmelCase_ )
         a_      :	Dict      =							model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCAmelCase_							,		nn.Linear ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	int      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	Optional[Any]      =							model_class(lowerCAmelCase_ )
         a_      :	Optional[int]      =							inspect.signature(model.forward )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         a_      :	Optional[int]      =							[*signature.parameters.keys()]

         a_      :	Any      =							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]							,		lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	str      =							SwiftFormerModel.from_pretrained(lowerCAmelCase_ )
         self.assertIsNotNone(lowerCAmelCase_ )


     @unittest.skip(reason="""SwiftFormer does not output attentions""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       def check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):
         a_      :	int      =							model_class(lowerCAmelCase_ )
         model.to(lowerCAmelCase_ )
         model.eval()

         with torch.no_grad():
           a_      :	Optional[int]      =							model(**self._prepare_for_class(lowerCAmelCase_							,		lowerCAmelCase_ ) )

         a_      :	Union[str, Any]      =							outputs.hidden_states

         a_      :	Dict      =							8
         self.assertEqual(len(lowerCAmelCase_ )							,		lowerCAmelCase_ )  # TODO

         # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
         # with the width and height being successively divided by 2, after every 2 blocks
         for i in range(len(lowerCAmelCase_ ) ):
           self.assertEqual(
               hidden_states[i].shape							,		torch.Size(
                   [
                       self.model_tester.batch_size,
                       self.model_tester.embed_dims[i // 2],
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                   ] )							,		)

       a_      , a_      :	List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	int      =							True
         check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a_      :	str      =							True

         check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       def _config_zero_init(lowerCAmelCase_ ):
         a_      :	Union[str, Any]      =							copy.deepcopy(lowerCAmelCase_ )
         for key in configs_no_init.__dict__.keys():
           if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
             setattr(lowerCAmelCase_							,		lowerCAmelCase_							,		1E-1_0 )
           if isinstance(getattr(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )							,		lowerCAmelCase_ ):
             a_      :	Union[str, Any]      =							_config_zero_init(getattr(lowerCAmelCase_							,		lowerCAmelCase_ ) )
             setattr(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )
         return configs_no_init

       a_      , a_      :	Union[str, Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

       a_      :	Dict      =							_config_zero_init(lowerCAmelCase_ )
       for model_class in self.all_model_classes:
         a_      :	int      =							model_class(config=lowerCAmelCase_ )
         for name, param in model.named_parameters():
           if param.requires_grad:
             self.assertIn(
                 ((param.data.mean() * 1E9) / 1E9).round().item()							,		[0.0, 1.0]							,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,		)






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass







def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	str      =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image




@require_torch
@require_vision
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(lowerCAmelCase_ )

       a_      :	Dict      =							self.default_image_processor
       a_      :	List[str]      =							prepare_img()
       a_      :	Union[str, Any]      =							image_processor(images=lowerCAmelCase_							,		return_tensors="""pt""" ).to(lowerCAmelCase_ )

       # forward pass
       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )

       # verify the logits
       a_      :	List[Any]      =							torch.Size((1, 10_00) )
       self.assertEqual(outputs.logits.shape							,		lowerCAmelCase_ )

       a_      :	Optional[int]      =							torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]] ).to(lowerCAmelCase_ )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,		lowerCAmelCase_							,		atol=1E-4 ) )




'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__snake_case:  Dict								=			logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		*lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(*lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							eval_examples
       a_      :	Optional[Any]      =							post_process_function
       a_      :	List[str]      =							quant_trainer_args
       a_      :	List[str]      =							1_28  # default number of calibration samples


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       a_      :	str      =							calib_dataset if calib_dataset is not None else self.calib_dataset

       a_      :	Dict      =							self._remove_unused_columns(lowerCAmelCase_							,		description="""Calibration""" )

       return DataLoader(
           lowerCAmelCase_							,		batch_size=self.args.eval_batch_size							,		collate_fn=self.data_collator							,		drop_last=self.args.dataloader_drop_last							,		num_workers=self.args.dataloader_num_workers							,		pin_memory=self.args.dataloader_pin_memory							,		shuffle=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.train_dataset if calib_dataset is None else calib_dataset
       a_      :	int      =							self.get_calib_dataloader(lowerCAmelCase_ )

       a_      :	Dict      =							self.model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args							,		calib=lowerCAmelCase_ )
       model.eval()
       quant_trainer.enable_calibration(lowerCAmelCase_ )

       logger.info("""***** Running calibration *****""" )
       logger.info(f'''  Num examples = {self.calib_num}''' )
       logger.info(f'''  Batch size = {calib_dataloader.batch_size}''' )

       for step, inputs in enumerate(lowerCAmelCase_ ):
         # Prediction step
         a_      , a_      , a_      :	Optional[int]      =							self.prediction_step(lowerCAmelCase_							,		lowerCAmelCase_							,		prediction_loss_only=lowerCAmelCase_ )
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
           break

       quant_trainer.finish_calibration(lowerCAmelCase_							,		self.quant_trainer_args )
       a_      :	Optional[int]      =							model


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "eval" ):

       '''simple docstring'''

       a_      :	str      =							self.eval_dataset if eval_dataset is None else eval_dataset
       a_      :	List[str]      =							self.get_eval_dataloader(lowerCAmelCase_ )
       a_      :	str      =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	List[str]      =							self.compute_metrics
       a_      :	Tuple      =							None
       a_      :	Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	List[str]      =							eval_loop(
             lowerCAmelCase_							,		description="""Evaluation"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
         a_      :	Optional[Any]      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions )
         a_      :	Tuple      =							self.compute_metrics(lowerCAmelCase_ )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys() ):
           if not key.startswith(f'''{metric_key_prefix}_''' ):
             a_      :	Optional[int]      =							metrics.pop(lowerCAmelCase_ )

         self.log(lowerCAmelCase_ )
       else:
         a_      :	Any      =							{}

       if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report() )

       a_      :	Optional[Any]      =							self.callback_handler.on_evaluate(self.args							,		self.state							,		self.control							,		lowerCAmelCase_ )
       return metrics


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "test" ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_test_dataloader(lowerCAmelCase_ )

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	Any      =							self.compute_metrics
       a_      :	Dict      =							None
       a_      :	Dict      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	Dict      =							eval_loop(
             lowerCAmelCase_							,		description="""Prediction"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is None or self.compute_metrics is None:
         return output

       a_      :	Tuple      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions							,		"""predict""" )
       a_      :	List[Any]      =							self.compute_metrics(lowerCAmelCase_ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f'''{metric_key_prefix}_''' ):
           a_      :	Tuple      =							metrics.pop(lowerCAmelCase_ )

       return PredictionOutput(predictions=predictions.predictions							,		label_ids=predictions.label_ids							,		metrics=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_="./" ):

       '''simple docstring'''

       a_      :	Any      =							self.eval_dataset
       a_      :	Tuple      =							self.get_eval_dataloader(lowerCAmelCase_ )

       a_      :	List[str]      =							next(iter(lowerCAmelCase_ ) )

       # saving device - to make it consistent
       a_      :	Dict      =							torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

       # convert to tuple
       a_      :	Optional[int]      =							tuple(v.to(lowerCAmelCase_ ) for k, v in batch.items() )

       logger.info("""Converting model to be onnx compatible""" )
       from pytorch_quantization.nn import TensorQuantizer

       a_      :	List[str]      =							True

       a_      :	str      =							self.model.to(lowerCAmelCase_ )

       model.eval()
       model.float()

       a_      :	Optional[int]      =							model.module if hasattr(lowerCAmelCase_							,		"""module""" ) else model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args )

       a_      :	Union[str, Any]      =							os.path.join(lowerCAmelCase_							,		"""model.onnx""" )
       logger.info(f'''exporting model to {output_model_file}''' )

       a_      :	Dict      =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		export_params=lowerCAmelCase_							,		opset_version=13							,		do_constant_folding=lowerCAmelCase_							,		input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]							,		output_names=["""output_start_logits""", """output_end_logits"""]							,		dynamic_axes={
               """input_ids""": axes,
               """attention_mask""": axes,
               """token_type_ids""": axes,
               """output_start_logits""": axes,
               """output_end_logits""": axes,
           }							,		verbose=lowerCAmelCase_							,		)
       logger.info("""onnx export finished""" )


