


'''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      a    :


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		str							,			snake_case					:		Optional[Any]=13							,			snake_case					:		Union[str, Any]=3							,			snake_case					:		List[Any]=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[str]=0.1							,			snake_case					:		List[str]=0.1							,			snake_case					:		List[Any]=224							,			snake_case					:		int=1000							,			snake_case					:		str=[3, 3, 6, 4]							,			snake_case					:		List[str]=[48, 56, 112, 220]							,			)						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      parent
          __UpperCAmelCase     :			Dict  							=      batch_size
          __UpperCAmelCase     :			Any  							=      num_channels
          __UpperCAmelCase     :			Dict  							=      is_training
          __UpperCAmelCase     :			Tuple  							=      use_labels
          __UpperCAmelCase     :			str  							=      hidden_dropout_prob
          __UpperCAmelCase     :			Optional[Any]  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			List[str]  							=      num_labels
          __UpperCAmelCase     :			List[Any]  							=      image_size
          __UpperCAmelCase     :			List[Any]  							=      layer_depths
          __UpperCAmelCase     :			List[str]  							=      embed_dims

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[Any]:
          __UpperCAmelCase     :			List[Any]  							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          __UpperCAmelCase     :			Dict  							=      None
          if self.use_labels:
               __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size]							,			self.num_labels      )

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

          return config, pixel_values, labels

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          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=snake_case							,			layer_scale_init_value=1E-5							,			)

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[int]							,			snake_case					:		List[Any]      )						->							Any:
          __UpperCAmelCase     :			int  							=      SwiftFormerModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case      )
          self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.embed_dims[-1], 7, 7)      )

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Any							,			snake_case					:		Any							,			snake_case					:		List[Any]      )						->							Tuple:
          __UpperCAmelCase     :			List[str]  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      SwiftFormerForImageClassification(snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Optional[int]  							=      model(snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)      )

          __UpperCAmelCase     :			str  							=      SwiftFormerForImageClassification(snake_case      )
          model.to(snake_case      )
          model.eval()

          __UpperCAmelCase     :			int  							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
          __UpperCAmelCase     :			Optional[Any]  							=      model(snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)      )






     def 			lowerCamelCase__			(    self					:		str      )						->							Union[str, Any]:
          ((__UpperCAmelCase)							,       (__UpperCAmelCase)							,       (__UpperCAmelCase))     :			str  							=      self.prepare_config_and_inputs()
          __UpperCAmelCase     :			Optional[int]  							=      {'''pixel_values''': pixel_values}
          return config, inputs_dict



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False
     SCREAMING_SNAKE_CASE	:			Dict							       =       False

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Any:
          __UpperCAmelCase     :			Dict  							=      SwiftFormerModelTester(self      )
          __UpperCAmelCase     :			List[Any]  							=      ConfigTester(
              self							,			config_class=snake_case							,			has_text_modality=snake_case							,			hidden_size=37							,			num_attention_heads=12							,			num_hidden_layers=12							,			)

     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          self.config_tester.run_common_tests()

     @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''      )
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Any:
          pass

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[Any]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			List[str]  							=      model_class(snake_case      )
               __UpperCAmelCase     :			Any  							=      model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(snake_case							,			nn.Linear      )      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Dict:
          __UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCAmelCase     :			Optional[int]  							=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case      )

     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			List[Any]  							=      SwiftFormerModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )

     @unittest.skip(reason='''SwiftFormer does not output attentions'''      )
     def 			lowerCamelCase__			(    self					:		int      )						->							List[str]:
          pass

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Dict:
          def check_hidden_states_output(snake_case					:		Tuple							,			snake_case					:		Dict							,			snake_case					:		Any      ):
               __UpperCAmelCase     :			Optional[Any]  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()

               with torch.no_grad():
                    __UpperCAmelCase     :			Any  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )

               __UpperCAmelCase     :			Tuple  							=      outputs.hidden_states

               __UpperCAmelCase     :			Dict  							=      8
               self.assertEqual(len(snake_case      )							,			snake_case      )  # 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(snake_case      )      ):
                    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     :			Optional[Any]  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			Any  							=      True
               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __UpperCAmelCase     :			List[Any]  							=      True

               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Tuple:
          def _config_zero_init(snake_case					:		Optional[int]      ):
               __UpperCAmelCase     :			Tuple  							=      copy.deepcopy(snake_case      )
               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(snake_case							,			snake_case							,			1E-10      )
                    if isinstance(getattr(snake_case							,			snake_case							,			snake_case      )							,			snake_case      ):
                         __UpperCAmelCase     :			Optional[Any]  							=      _config_zero_init(getattr(snake_case							,			snake_case      )      )
                         setattr(snake_case							,			snake_case							,			snake_case      )
               return configs_no_init

          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCAmelCase     :			str  							=      _config_zero_init(snake_case      )
          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			int  							=      model_class(config=snake_case      )
               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					:		Tuple      )						->							Dict:
          pass




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
     return image



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


     """simple docstring"""






     @cached_property
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Tuple:
          return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''      ) if is_vision_available() else None






     @slow
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(snake_case      )

          __UpperCAmelCase     :			str  							=      self.default_image_processor
          __UpperCAmelCase     :			Dict  							=      prepare_img()
          __UpperCAmelCase     :			Tuple  							=      image_processor(images=snake_case							,			return_tensors='''pt'''      ).to(snake_case      )

          # forward pass
          with torch.no_grad():
               __UpperCAmelCase     :			List[Any]  							=      model(**snake_case      )

          # verify the logits
          __UpperCAmelCase     :			Dict  							=      torch.Size((1, 1000)      )
          self.assertEqual(outputs.logits.shape							,			snake_case      )

          __UpperCAmelCase     :			Any  							=      torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]      ).to(snake_case      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]							,			snake_case							,			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


__UpperCAmelCase      :int			    =       datasets.utils.logging.get_logger(__name__)



@dataclass
class      a    (		datasets.BuilderConfig	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[datasets.Features]							       =       None
     SCREAMING_SNAKE_CASE	:			str							       =       "utf-8"
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       None
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       None
     SCREAMING_SNAKE_CASE	:			bool							       =       True  # deprecated
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       None  # deprecated
     SCREAMING_SNAKE_CASE	:			int							       =       1_0 << 2_0  # 10MB
     SCREAMING_SNAKE_CASE	:			Optional[bool]							       =       None



class      a    (		datasets.ArrowBasedBuilder	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Tuple							       =       JsonConfig

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Dict:
          if self.config.block_size is not None:
               logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
               __UpperCAmelCase     :			Tuple  							=      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 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Tuple      )						->							str:
          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     :			Optional[int]  							=      dl_manager.download_and_extract(self.config.data_files      )
          if isinstance(snake_case							,			(str, list, tuple)      ):
               __UpperCAmelCase     :			Dict  							=      data_files
               if isinstance(snake_case							,			snake_case      ):
                    __UpperCAmelCase     :			List[Any]  							=      [files]
               __UpperCAmelCase     :			Tuple  							=      [dl_manager.iter_files(snake_case      ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,			gen_kwargs={'''files''': files}      )]
          __UpperCAmelCase     :			Tuple  							=      []
          for split_name, files in data_files.items():
               if isinstance(snake_case							,			snake_case      ):
                    __UpperCAmelCase     :			Any  							=      [files]
               __UpperCAmelCase     :			Optional[int]  							=      [dl_manager.iter_files(snake_case      ) for file in files]
               splits.append(datasets.SplitGenerator(name=snake_case							,			gen_kwargs={'''files''': files}      )      )
          return splits

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






     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Any      )						->							Optional[int]:
          for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case      )      ):
               # 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(snake_case							,			encoding=self.config.encoding							,			errors=self.config.encoding_errors      ) as f:
                         __UpperCAmelCase     :			Optional[int]  							=      json.load(snake_case      )

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

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

               # If the file has one json object per line
               else:
                    with open(snake_case							,			'''rb'''      ) as f:
                         __UpperCAmelCase     :			Optional[Any]  							=      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     :			int  							=      max(self.config.chunksize // 32							,			16 << 10      )
                         __UpperCAmelCase     :			Any  							=      (
                             self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                         )
                         while True:
                              __UpperCAmelCase     :			List[Any]  							=      f.read(self.config.chunksize      )
                              if not batch:
                                   break
                              # Finish current line
                              try:
                                   batch += f.readline()
                              except (AttributeError, io.UnsupportedOperation):
                                   batch += readline(snake_case      )
                              # PyArrow only accepts utf-8 encoded bytes
                              if self.config.encoding != "utf-8":
                                   __UpperCAmelCase     :			Union[str, Any]  							=      batch.decode(self.config.encoding							,			errors=snake_case      ).encode('''utf-8'''      )
                              try:
                                   while True:
                                        try:
                                             __UpperCAmelCase     :			List[str]  							=      paj.read_json(
                                                 io.BytesIO(snake_case      )							,			read_options=paj.ReadOptions(block_size=snake_case      )      )
                                             break
                                        except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                             if (
                                                 isinstance(snake_case							,			pa.ArrowInvalid      )
                                                 and "straddling" not in str(snake_case      )
                                                 or block_size > len(snake_case      )
                                             ):
                                                  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(snake_case      )} 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(
                                            snake_case							,			encoding=self.config.encoding							,			errors=self.config.encoding_errors      ) as f:
                                             __UpperCAmelCase     :			Optional[Any]  							=      json.load(snake_case      )
                                   except json.JSONDecodeError:
                                        logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {e}'      )
                                        raise e
                                   # If possible, parse the file as a list of json objects and exit the loop
                                   if isinstance(snake_case							,			snake_case      ):  # list is the only sequence type supported in JSON
                                        try:
                                             __UpperCAmelCase     :			Dict  							=      set().union(*[row.keys() for row in dataset]      )
                                             __UpperCAmelCase     :			Optional[Any]  							=      {col: [row.get(snake_case      ) for row in dataset] for col in keys}
                                             __UpperCAmelCase     :			Union[str, Any]  							=      pa.Table.from_pydict(snake_case      )
                                        except (pa.ArrowInvalid, AttributeError) as e:
                                             logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {e}'      )
                                             raise ValueError(f'Not able to read records in the JSON file at {file}.'      ) from None
                                        yield file_idx, self._cast_table(snake_case      )
                                        break
                                   else:
                                        logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {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(snake_case      )
                              batch_idx += 1