
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor




class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[Any]=13      ,							UpperCamelCase__      :			Tuple=32      ,							UpperCamelCase__      :			List[Any]=3      ,							UpperCamelCase__      :			int=4      ,							UpperCamelCase__      :			List[str]=[10, 20, 30, 40]      ,							UpperCamelCase__      :			Dict=[2, 2, 3, 2]      ,							UpperCamelCase__      :			int=True      ,							UpperCamelCase__      :			Union[str, Any]=True      ,							UpperCamelCase__      :			Tuple=37      ,							UpperCamelCase__      :			Dict="gelu"      ,							UpperCamelCase__      :			Any=10      ,							UpperCamelCase__      :			Any=0.02      ,							UpperCamelCase__      :			Optional[int]=3      ,							UpperCamelCase__      :			Optional[Any]=None      ,							)      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	parent
    __magic_name__    					=	batch_size
    __magic_name__    					=	image_size
    __magic_name__    					=	num_channels
    __magic_name__    					=	num_stages
    __magic_name__    					=	hidden_sizes
    __magic_name__    					=	depths
    __magic_name__    					=	is_training
    __magic_name__    					=	use_labels
    __magic_name__    					=	intermediate_size
    __magic_name__    					=	hidden_act
    __magic_name__    					=	type_sequence_label_size
    __magic_name__    					=	initializer_range
    __magic_name__    					=	scope
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     List[Any]:







    """simple docstring"""






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

    __magic_name__    					=	None
    if self.use_labels:
      __magic_name__    					=	ids_tensor([self.batch_size]      ,							self.type_sequence_label_size   )

    __magic_name__    					=	self.get_config()

    return config, pixel_values, labels
  def 							_lowercase							(			self      :			str   )      ->     int:







    """simple docstring"""






    return ConvNextConfig(
        num_channels=self.num_channels      ,							hidden_sizes=self.hidden_sizes      ,							depths=self.depths      ,							num_stages=self.num_stages      ,							hidden_act=self.hidden_act      ,							is_decoder=UpperCamelCase__      ,							initializer_range=self.initializer_range      ,							)
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			str   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	TFConvNextModel(config=UpperCamelCase__   )
    __magic_name__    					=	model(UpperCamelCase__      ,							training=UpperCamelCase__   )
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape      ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,							)
  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Dict   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	self.type_sequence_label_size
    __magic_name__    					=	TFConvNextForImageClassification(UpperCamelCase__   )
    __magic_name__    					=	model(UpperCamelCase__      ,							labels=UpperCamelCase__      ,							training=UpperCamelCase__   )
    self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)   )





  def 							_lowercase							(			self      :			Tuple   )      ->     List[str]:







    """simple docstring"""






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







@require_tf
class 	UpperCAmelCase_      (			_A  ,	_A  ,	unittest.TestCase					):


  '''simple docstring'''







  a__   	=		(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  a__   	=		(
      {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  a__   	=		False
  a__   	=		False
  a__   	=		False
  a__   	=		False
  a__   	=		False
  def 							_lowercase							(			self      :			Optional[int]   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	TFConvNextModelTester(self   )
    __magic_name__    					=	ConfigTester(
        self      ,							config_class=UpperCamelCase__      ,							has_text_modality=UpperCamelCase__      ,							hidden_size=37      ,							)
  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""   )
  def 							_lowercase							(			self      :			Tuple   )      ->     Tuple:







    """simple docstring"""






    pass
  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0      ,							reason="""TF does not support backprop for grouped convolutions on CPU."""      ,							)
  @slow
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Any:







    """simple docstring"""






    super().test_keras_fit()
  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""   )
  def 							_lowercase							(			self      :			int   )      ->     List[Any]:







    """simple docstring"""






    pass
  def 							_lowercase							(			self      :			str   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__    					=	["""pixel_values"""]
      self.assertListEqual(arg_names[:1]      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__   )
  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0      ,							reason="""TF does not support backprop for grouped convolutions on CPU."""      ,							)
  def 							_lowercase							(			self      :			str   )      ->     int:







    """simple docstring"""






    super().test_dataset_conversion()
  def 							_lowercase							(			self      :			int   )      ->     Dict:







    """simple docstring"""






    def check_hidden_states_output(UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Optional[Any]   ):
      __magic_name__    					=	model_class(UpperCamelCase__   )

      __magic_name__    					=	model(**self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )   )
      __magic_name__    					=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      __magic_name__    					=	self.model_tester.num_stages
      self.assertEqual(len(UpperCamelCase__   )      ,							expected_num_stages + 1   )

      # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
      self.assertListEqual(
          list(hidden_states[0].shape[-2:]   )      ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,							)

    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__    					=	True
      check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )

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

      check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Any   )      ->     Tuple:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    def check_equivalence(UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			List[str]={}   ):
      __magic_name__    					=	model(UpperCamelCase__      ,							return_dict=UpperCamelCase__      ,							**UpperCamelCase__   )
      __magic_name__    					=	model(UpperCamelCase__      ,							return_dict=UpperCamelCase__      ,							**UpperCamelCase__   ).to_tuple()

      def recursive_check(UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Dict   ):
        if isinstance(UpperCamelCase__      ,							(List, Tuple)   ):
          for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__      ,							UpperCamelCase__   ):
            recursive_check(UpperCamelCase__      ,							UpperCamelCase__   )
        elif tuple_object is None:
          return
        else:
          self.assertTrue(
              all(tf.equal(UpperCamelCase__      ,							UpperCamelCase__   )   )      ,							msg=(
                  """Tuple and dict output are not equal. Difference:"""
                  F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'''
              )      ,							)

        recursive_check(UpperCamelCase__      ,							UpperCamelCase__   )

    for model_class in self.all_model_classes:
      __magic_name__    					=	model_class(UpperCamelCase__   )

      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      check_equivalence(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )

      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__      ,							return_labels=UpperCamelCase__   )
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__      ,							return_labels=UpperCamelCase__   )
      check_equivalence(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )

      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )
      check_equivalence(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							{"""output_hidden_states""": True}   )

      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__      ,							return_labels=UpperCamelCase__   )
      __magic_name__    					=	self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__      ,							return_labels=UpperCamelCase__   )
      check_equivalence(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							{"""output_hidden_states""": True}   )
  def 							_lowercase							(			self      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__   )





  @slow
  def 							_lowercase							(			self      :			Dict   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""   )
    self.assertIsNotNone(UpperCamelCase__   )







def a__   (	):






  '''simple docstring'''





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





@require_tf
@require_vision
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  @cached_property
  def 							_lowercase							(			self      :			Any   )      ->     int:







    """simple docstring"""






    return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""   ) if is_vision_available() else None





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







    """simple docstring"""






    __magic_name__    					=	TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""   )

    __magic_name__    					=	self.default_image_processor
    __magic_name__    					=	prepare_img()
    __magic_name__    					=	image_processor(images=UpperCamelCase__      ,							return_tensors="""tf"""   )

    # forward pass
    __magic_name__    					=	model(**UpperCamelCase__   )

    # verify the logits
    __magic_name__    					=	tf.TensorShape((1, 1000)   )
    self.assertEqual(outputs.logits.shape      ,							UpperCamelCase__   )

    __magic_name__    					=	tf.constant([-0.0260, -0.4739, 0.1911]   )

    tf.debugging.assert_near(outputs.logits[0, :3]      ,							UpperCamelCase__      ,							atol=1E-4   )



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      if len(buffer_per_group[group_id]   ) == self.batch_size:
        yield buffer_per_group[group_id]  # TODO
        num_batches += 1
        del buffer_per_group[group_id]
      assert len(buffer_per_group[group_id]   ) < self.batch_size

    # now we have run out of elements that satisfy
    # the group criteria, let's return the remaining
    # elements so that the size of the sampler is
    # deterministic
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





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







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

