'''simple docstring'''






import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
    from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=3   ,    snake_case_=32   ,    snake_case_=3   ,    snake_case_=10   ,    snake_case_=[8, 16, 32, 64]   ,    snake_case_=[1, 1, 2, 1]   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_="relu"   ,    snake_case_=3   ,    snake_case_=None   ,    snake_case_=["stage2", "stage3", "stage4"]   ,    snake_case_=[2, 3, 4]   ,    snake_case_=1   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Any		= num_channels
    __UpperCAmelCase:					Any		= embeddings_size
    __UpperCAmelCase:					Optional[int]		= hidden_sizes
    __UpperCAmelCase:					Dict		= depths
    __UpperCAmelCase:					int		= is_training
    __UpperCAmelCase:					str		= use_labels
    __UpperCAmelCase:					int		= hidden_act
    __UpperCAmelCase:					Any		= num_labels
    __UpperCAmelCase:					List[Any]		= scope
    __UpperCAmelCase:					int		= len(snake_case_					)
    __UpperCAmelCase:					Any		= out_features
    __UpperCAmelCase:					Tuple		= out_indices
    __UpperCAmelCase:					Any		= num_groups







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

    __UpperCAmelCase:					Any		= self.get_config()

    return config, pixel_values, labels







  def    lowercase_  (							self					):

    '''simple docstring'''







    return BitConfig(
        num_channels=self.num_channels   ,    embeddings_size=self.embeddings_size   ,    hidden_sizes=self.hidden_sizes   ,    depths=self.depths   ,    hidden_act=self.hidden_act   ,    num_labels=self.num_labels   ,    out_features=self.out_features   ,    out_indices=self.out_indices   ,    num_groups=self.num_groups   ,    )







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= BitModel(config=snake_case_					)
    model.to(snake_case_					)
    model.eval()
    __UpperCAmelCase:					str		= model(snake_case_					)
    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   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.num_labels
    __UpperCAmelCase:					int		= BitForImageClassification(snake_case_					)
    model.to(snake_case_					)
    model.eval()
    __UpperCAmelCase:					Tuple		= model(snake_case_   ,    labels=snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= BitBackbone(config=snake_case_					)
    model.to(snake_case_					)
    model.eval()
    __UpperCAmelCase:					int		= model(snake_case_					)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps					)   ,    len(config.out_features					)					)
    self.parent.assertListEqual(list(result.feature_maps[0].shape					)   ,    [self.batch_size, self.hidden_sizes[1], 4, 4]					)

    # verify channels
    self.parent.assertEqual(len(model.channels					)   ,    len(config.out_features					)					)
    self.parent.assertListEqual(model.channels   ,    config.hidden_sizes[1:]					)

    # verify backbone works with out_features=None
    __UpperCAmelCase:					Optional[int]		= None
    __UpperCAmelCase:					List[Any]		= BitBackbone(config=snake_case_					)
    model.to(snake_case_					)
    model.eval()
    __UpperCAmelCase:					List[str]		= model(snake_case_					)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps					)   ,    1					)
    self.parent.assertListEqual(list(result.feature_maps[0].shape					)   ,    [self.batch_size, self.hidden_sizes[-1], 1, 1]					)

    # verify channels
    self.parent.assertEqual(len(model.channels					)   ,    1					)
    self.parent.assertListEqual(model.channels   ,    [config.hidden_sizes[-1]]					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= self.prepare_config_and_inputs()
    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[str]		= config_and_inputs
    __UpperCAmelCase:					Optional[Any]		= {"""pixel_values""": pixel_values}
    return config, inputs_dict




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



  """simple docstring"""
  __lowerCAmelCase				      =					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  __lowerCAmelCase				      =					(
      {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= BitModelTester(self					)
    __UpperCAmelCase:					Dict		= ConfigTester(self   ,    config_class=snake_case_   ,    has_text_modality=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()







  def    lowercase_  (							self					):

    '''simple docstring'''







    return







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

    '''simple docstring'''







    pass







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

    '''simple docstring'''







    pass







  @unittest.skip(reason="""Bit does not support input and output embeddings"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Optional[int]		= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __UpperCAmelCase:					Dict		= model_class(snake_case_					)
      __UpperCAmelCase:					List[str]		= inspect.signature(model.forward					)
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __UpperCAmelCase:					int		= [*signature.parameters.keys()]

      __UpperCAmelCase:					int		= ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







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

    for model_class in self.all_model_classes:
      __UpperCAmelCase:					int		= model_class(config=snake_case_					)
      for name, module in model.named_modules():
        if isinstance(snake_case_   ,    (nn.BatchNormad, nn.GroupNorm)					):
          self.assertTrue(
              torch.all(module.weight == 1					)   ,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,    )
          self.assertTrue(
              torch.all(module.bias == 0					)   ,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,    )







  def    lowercase_  (							self					):

    '''simple docstring'''







    def check_hidden_states_output(snake_case_   ,    snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					Union[str, Any]		= model_class(snake_case_					)
      model.to(snake_case_					)
      model.eval()

      with torch.no_grad():
        __UpperCAmelCase:					str		= model(**self._prepare_for_class(snake_case_   ,    snake_case_					)					)

      __UpperCAmelCase:					int		= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      __UpperCAmelCase:					List[Any]		= self.model_tester.num_stages
      self.assertEqual(len(snake_case_					)   ,    expected_num_stages + 1					)

      # Bit'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]   ,    )

    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase:					Any		= ["""preactivation""", """bottleneck"""]
    for model_class in self.all_model_classes:
      for layer_type in layers_type:
        __UpperCAmelCase:					List[str]		= layer_type
        __UpperCAmelCase:					List[str]		= 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:					Tuple		= True

        check_hidden_states_output(snake_case_   ,    snake_case_   ,    snake_case_					)







  @unittest.skip(reason="""Bit does not use feedforward chunking"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __UpperCAmelCase:					str		= BitModel.from_pretrained(snake_case_					)
      self.assertIsNotNone(snake_case_					)






def  UpperCamelCase__					(					)							->	List[Any]:
  __UpperCAmelCase:					Optional[int]		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image




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



  """simple docstring"""







  @cached_property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return (
        BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					) if is_vision_available() else None
    )







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(snake_case_					)

    __UpperCAmelCase:					Optional[int]		= self.default_image_processor
    __UpperCAmelCase:					Dict		= prepare_img()
    __UpperCAmelCase:					List[str]		= image_processor(images=snake_case_   ,    return_tensors="""pt"""					).to(snake_case_					)

    # forward pass
    with torch.no_grad():
      __UpperCAmelCase:					Union[str, Any]		= model(**snake_case_					)

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

    __UpperCAmelCase:					Optional[Any]		= torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]					).to(snake_case_					)

    self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,    snake_case_   ,    atol=1e-4					)					)




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



  """simple docstring"""
  __lowerCAmelCase				      =					(BitBackbone,) if is_torch_available() else ()
  __lowerCAmelCase				      =					BitConfig

  __lowerCAmelCase				      =					False







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= BitModelTester(self					)
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )