



"""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 				lowercase__       :







       '''simple docstring'''


       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  List[Any]=13		,    _UpperCAmelCase      :  Dict=3		,    _UpperCAmelCase      :  List[str]=True		,    _UpperCAmelCase      :  Optional[int]=True		,    _UpperCAmelCase      :  Optional[Any]=0.1		,    _UpperCAmelCase      :  List[str]=0.1		,    _UpperCAmelCase      :  Union[str, Any]=224		,    _UpperCAmelCase      :  Dict=1000		,    _UpperCAmelCase      :  Dict=[3, 3, 6, 4]		,    _UpperCAmelCase      :  List[Any]=[48, 56, 112, 220]		,    )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							parent
             UpperCAmelCase_      			=							batch_size
             UpperCAmelCase_      			=							num_channels
             UpperCAmelCase_      			=							is_training
             UpperCAmelCase_      			=							use_labels
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							num_labels
             UpperCAmelCase_      			=							image_size
             UpperCAmelCase_      			=							layer_depths
             UpperCAmelCase_      			=							embed_dims



       def 					lowercase__    (							self      :  Optional[int]   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

             UpperCAmelCase_      			=							None
             if self.use_labels:
                   UpperCAmelCase_      			=							ids_tensor([self.batch_size]		,    self.num_labels   )

             UpperCAmelCase_      			=							self.get_config()

             return config, pixel_values, labels



       def 					lowercase__    (							self      :  Optional[int]   )			->						Dict:


             '''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=_UpperCAmelCase		,    layer_scale_init_value=1e-5		,    )



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  Union[str, Any]		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Tuple   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							SwiftFormerModel(config=_UpperCAmelCase   )
             model.to(_UpperCAmelCase   )
             model.eval()
             UpperCAmelCase_      			=							model(_UpperCAmelCase   )
             self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.embed_dims[-1], 7, 7)   )



       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Dict   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.num_labels
             UpperCAmelCase_      			=							SwiftFormerForImageClassification(_UpperCAmelCase   )
             model.to(_UpperCAmelCase   )
             model.eval()
             UpperCAmelCase_      			=							model(_UpperCAmelCase		,    labels=_UpperCAmelCase   )
             self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)   )

             UpperCAmelCase_      			=							SwiftFormerForImageClassification(_UpperCAmelCase   )
             model.to(_UpperCAmelCase   )
             model.eval()

             UpperCAmelCase_      			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
             UpperCAmelCase_      			=							model(_UpperCAmelCase   )
             self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)   )







       def 					lowercase__    (							self      :  Optional[Any]   )			->						List[Any]:


             '''simple docstring'''




             ((UpperCAmelCase_)			,   (UpperCAmelCase_)			,   (UpperCAmelCase_))      			=							self.prepare_config_and_inputs()
             UpperCAmelCase_      			=							{"pixel_values": pixel_values}
             return config, inputs_dict



@require_torch
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


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

       UpperCamelCase						=					False
       UpperCamelCase						=					False
       UpperCamelCase						=					False
       UpperCamelCase						=					False
       UpperCamelCase						=					False



       def 					lowercase__    (							self      :  Union[str, Any]   )			->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_      			=							SwiftFormerModelTester(self   )
             UpperCAmelCase_      			=							ConfigTester(
                 self		,    config_class=_UpperCAmelCase		,    has_text_modality=_UpperCAmelCase		,    hidden_size=37		,    num_attention_heads=12		,    num_hidden_layers=12		,    )



       def 					lowercase__    (							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       @unittest.skip(reason="SwiftFormer does not use inputs_embeds"   )
       def 					lowercase__    (							self      :  Tuple   )			->						Tuple:


             '''simple docstring'''




             pass



       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

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



       def 					lowercase__    (							self      :  Union[str, Any]   )			->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_      			=							["pixel_values"]
                   self.assertListEqual(arg_names[:1]		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase   )



       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						int:


             '''simple docstring'''




             for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_      			=							SwiftFormerModel.from_pretrained(_UpperCAmelCase   )
                   self.assertIsNotNone(_UpperCAmelCase   )



       @unittest.skip(reason="SwiftFormer does not output attentions"   )
       def 					lowercase__    (							self      :  Tuple   )			->						Optional[int]:


             '''simple docstring'''




             pass



       def 					lowercase__    (							self      :  Optional[Any]   )			->						List[str]:


             '''simple docstring'''




             def check_hidden_states_output(_UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Union[str, Any]		,    _UpperCAmelCase      :  List[Any]   ):
                   UpperCAmelCase_      			=							model_class(_UpperCAmelCase   )
                   model.to(_UpperCAmelCase   )
                   model.eval()

                   with torch.no_grad():
                         UpperCAmelCase_      			=							model(**self._prepare_for_class(_UpperCAmelCase		,    _UpperCAmelCase   )   )

                   UpperCAmelCase_      			=							outputs.hidden_states

                   UpperCAmelCase_      			=							8
                   self.assertEqual(len(_UpperCAmelCase   )		,    _UpperCAmelCase   )  # 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(_UpperCAmelCase   )   ):
                         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),
                                 ]   )		,    )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_      			=							True
                   check_hidden_states_output(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   UpperCAmelCase_      			=							True

                   check_hidden_states_output(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						Any:


             '''simple docstring'''




             def _config_zero_init(_UpperCAmelCase      :  Dict   ):
                   UpperCAmelCase_      			=							copy.deepcopy(_UpperCAmelCase   )
                   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(_UpperCAmelCase		,    _UpperCAmelCase		,    1e-10   )
                         if isinstance(getattr(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )		,    _UpperCAmelCase   ):
                               UpperCAmelCase_      			=							_config_zero_init(getattr(_UpperCAmelCase		,    _UpperCAmelCase   )   )
                               setattr(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
                   return configs_no_init

             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

             UpperCAmelCase_      			=							_config_zero_init(_UpperCAmelCase   )
             for model_class in self.all_model_classes:
                   UpperCAmelCase_      			=							model_class(config=_UpperCAmelCase   )
                   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 					lowercase__    (							self      :  Optional[int]   )			->						Any:


             '''simple docstring'''




             pass



def        a__     (				):
      UpperCAmelCase_      			=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
      return image


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







       '''simple docstring'''


       @cached_property
       def 					lowercase__    (							self      :  List[str]   )			->						List[str]:


             '''simple docstring'''




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







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"   ).to(_UpperCAmelCase   )

             UpperCAmelCase_      			=							self.default_image_processor
             UpperCAmelCase_      			=							prepare_img()
             UpperCAmelCase_      			=							image_processor(images=_UpperCAmelCase		,    return_tensors="pt"   ).to(_UpperCAmelCase   )

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(**_UpperCAmelCase   )

             # verify the logits
             UpperCAmelCase_      			=							torch.Size((1, 1000)   )
             self.assertEqual(outputs.logits.shape		,    _UpperCAmelCase   )

             UpperCAmelCase_      			=							torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]   ).to(_UpperCAmelCase   )
             self.assertTrue(torch.allclose(outputs.logits[0, :3]		,    _UpperCAmelCase		,    atol=1e-4   )   )









"""simple docstring"""





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCamelCase       	=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 				lowercase__       (   datasets.BuilderConfig				):







       '''simple docstring'''


       UpperCamelCase						=					None
       UpperCamelCase						=					"utf-8"
       UpperCamelCase						=					None
       UpperCamelCase						=					None
       UpperCamelCase						=					True  # deprecated
       UpperCamelCase						=					None  # deprecated
       UpperCamelCase						=					10 << 20  # 10MB
       UpperCamelCase						=					None



class 				lowercase__       (   datasets.ArrowBasedBuilder				):







       '''simple docstring'''


       UpperCamelCase						=					JsonConfig



       def 					lowercase__    (							self      :  List[Any]   )			->						Dict:


             '''simple docstring'''




             if self.config.block_size is not None:
                   logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"   )
                   UpperCAmelCase_      			=							self.config.block_size
             if self.config.use_threads is not True:
                   logger.warning(
                       "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."   )
             if self.config.newlines_in_values is not None:
                   raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"   )
             return datasets.DatasetInfo(features=self.config.features   )



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




             if not self.config.data_files:
                   raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
             UpperCAmelCase_      			=							dl_manager.download_and_extract(self.config.data_files   )
             if isinstance(_UpperCAmelCase		,    (str, list, tuple)   ):
                   UpperCAmelCase_      			=							data_files
                   if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):
                         UpperCAmelCase_      			=							[files]
                   UpperCAmelCase_      			=							[dl_manager.iter_files(_UpperCAmelCase   ) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,    gen_kwargs={"files": files}   )]
             UpperCAmelCase_      			=							[]
             for split_name, files in data_files.items():
                   if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):
                         UpperCAmelCase_      			=							[files]
                   UpperCAmelCase_      			=							[dl_manager.iter_files(_UpperCAmelCase   ) for file in files]
                   splits.append(datasets.SplitGenerator(name=_UpperCAmelCase		,    gen_kwargs={"files": files}   )   )
             return splits



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  pa.Table   )			->						pa.Table:


             '''simple docstring'''




             if self.config.features is not None:
                   # adding missing columns
                   for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
                         UpperCAmelCase_      			=							self.config.features.arrow_schema.field(_UpperCAmelCase   ).type
                         UpperCAmelCase_      			=							pa_table.append_column(_UpperCAmelCase		,    pa.array([None] * len(_UpperCAmelCase   )		,    type=_UpperCAmelCase   )   )
                   # more expensive cast to support nested structures with keys in a different order
                   # allows str <-> int/float or str to Audio for example
                   UpperCAmelCase_      			=							table_cast(_UpperCAmelCase		,    self.config.features.arrow_schema   )
             return pa_table







       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  List[Any]   )			->						str:


             '''simple docstring'''




             for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase   )   ):
                   # If the file is one json object and if we need to look at the list of items in one specific field
                   if self.config.field is not None:
                         with open(_UpperCAmelCase		,    encoding=self.config.encoding		,    errors=self.config.encoding_errors   ) as f:
                               UpperCAmelCase_      			=							json.load(_UpperCAmelCase   )

                         # We keep only the field we are interested in
                         UpperCAmelCase_      			=							dataset[self.config.field]

                         # We accept two format: a list of dicts or a dict of lists
                         if isinstance(_UpperCAmelCase		,    (list, tuple)   ):
                               UpperCAmelCase_      			=							set().union(*[row.keys() for row in dataset]   )
                               UpperCAmelCase_      			=							{col: [row.get(_UpperCAmelCase   ) for row in dataset] for col in keys}
                         else:
                               UpperCAmelCase_      			=							dataset
                         UpperCAmelCase_      			=							pa.Table.from_pydict(_UpperCAmelCase   )
                         yield file_idx, self._cast_table(_UpperCAmelCase   )

                   # If the file has one json object per line
                   else:
                         with open(_UpperCAmelCase		,    "rb"   ) as f:
                               UpperCAmelCase_      			=							0
                               # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                               # Set a default minimum value of 16kB if the chunk size is really small
                               UpperCAmelCase_      			=							max(self.config.chunksize // 32		,    16 << 10   )
                               UpperCAmelCase_      			=							(
                                   self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                               )
                               while True:
                                     UpperCAmelCase_      			=							f.read(self.config.chunksize   )
                                     if not batch:
                                           break
                                     # Finish current line
                                     try:
                                           batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                           batch += readline(_UpperCAmelCase   )
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                           UpperCAmelCase_      			=							batch.decode(self.config.encoding		,    errors=_UpperCAmelCase   ).encode("utf-8"   )
                                     try:
                                           while True:
                                                 try:
                                                       UpperCAmelCase_      			=							paj.read_json(
                                                           io.BytesIO(_UpperCAmelCase   )		,    read_options=paj.ReadOptions(block_size=_UpperCAmelCase   )   )
                                                       break
                                                 except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                       if (
                                                           isinstance(_UpperCAmelCase		,    pa.ArrowInvalid   )
                                                           and "straddling" not in str(_UpperCAmelCase   )
                                                           or block_size > len(_UpperCAmelCase   )
                                                       ):
                                                             raise
                                                       else:
                                                             # Increase the block size in case it was too small.
                                                             # The block size will be reset for the next file.
                                                             logger.debug(
                                                                 F"""Batch of {len(_UpperCAmelCase   )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""   )
                                                             block_size *= 2
                                     except pa.ArrowInvalid as e:
                                           try:
                                                 with open(
                                                     _UpperCAmelCase		,    encoding=self.config.encoding		,    errors=self.config.encoding_errors   ) as f:
                                                       UpperCAmelCase_      			=							json.load(_UpperCAmelCase   )
                                           except json.JSONDecodeError:
                                                 logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                 raise e
                                           # If possible, parse the file as a list of json objects and exit the loop
                                           if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):  # list is the only sequence type supported in JSON
                                                 try:
                                                       UpperCAmelCase_      			=							set().union(*[row.keys() for row in dataset]   )
                                                       UpperCAmelCase_      			=							{col: [row.get(_UpperCAmelCase   ) for row in dataset] for col in keys}
                                                       UpperCAmelCase_      			=							pa.Table.from_pydict(_UpperCAmelCase   )
                                                 except (pa.ArrowInvalid, AttributeError) as e:
                                                       logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                       raise ValueError(F"""Not able to read records in the JSON file at {file}."""   ) from None
                                                 yield file_idx, self._cast_table(_UpperCAmelCase   )
                                                 break
                                           else:
                                                 logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                 raise ValueError(
                                                     F"""Not able to read records in the JSON file at {file}. """
                                                     F"""You should probably indicate the field of the JSON file containing your records. """
                                                     F"""This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. """
                                                     F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """   ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase   )
                                     batch_idx += 1




