

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
  )
  from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DeiTImageProcessor





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=13				,  lowerCAmelCase=30				,  lowerCAmelCase=2				,  lowerCAmelCase=3				,  lowerCAmelCase=True				,  lowerCAmelCase=True				,  lowerCAmelCase=32				,  lowerCAmelCase=2				,  lowerCAmelCase=4				,  lowerCAmelCase=37				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase=10				,  lowerCAmelCase=0.02				,  lowerCAmelCase=3				,  lowerCAmelCase=None				,  lowerCAmelCase=2				,  )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=parent
        _lowercase      			=batch_size
        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=is_training
        _lowercase      			=use_labels
        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=type_sequence_label_size
        _lowercase      			=initializer_range
        _lowercase      			=scope
        _lowercase      			=encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        _lowercase      			=(image_size // patch_size) ** 2
        _lowercase      			=num_patches + 2




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






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

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

        _lowercase      			=self.get_config()

        return config, pixel_values, labels




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return DeiTConfig(
            image_size=self.image_size				,  patch_size=self.patch_size				,  num_channels=self.num_channels				,  hidden_size=self.hidden_size				,  num_hidden_layers=self.num_hidden_layers				,  num_attention_heads=self.num_attention_heads				,  intermediate_size=self.intermediate_size				,  hidden_act=self.hidden_act				,  hidden_dropout_prob=self.hidden_dropout_prob				,  attention_probs_dropout_prob=self.attention_probs_dropout_prob				,  is_decoder=lowerCAmelCase				,  initializer_range=self.initializer_range				,  encoder_stride=self.encoder_stride				,  )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=TFDeiTModel(config=lowerCAmelCase   )
        _lowercase      			=model(lowerCAmelCase   )
        self.parent.assertEqual(result.last_hidden_state.shape				,  (self.batch_size, self.seq_length, self.hidden_size)   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        _lowercase      			=TFDeiTForMaskedImageModeling(config=lowerCAmelCase   )
        _lowercase      			=model(lowerCAmelCase   )
        self.parent.assertEqual(
            result.reconstruction.shape				,  (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

        # test greyscale images
        _lowercase      			=1
        _lowercase      			=TFDeiTForMaskedImageModeling(lowerCAmelCase   )

        _lowercase      			=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        _lowercase      			=model(lowerCAmelCase   )
        self.parent.assertEqual(result.reconstruction.shape				,  (self.batch_size, 1, self.image_size, self.image_size)   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.type_sequence_label_size
        _lowercase      			=TFDeiTForImageClassification(lowerCAmelCase   )
        _lowercase      			=model(lowerCAmelCase				,  labels=lowerCAmelCase   )
        self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        _lowercase      			=1
        _lowercase      			=TFDeiTForImageClassification(lowerCAmelCase   )

        _lowercase      			=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        _lowercase      			=model(lowerCAmelCase				,  labels=lowerCAmelCase   )
        self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.type_sequence_label_size)   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=self.prepare_config_and_inputs()
        _lowercase			,			_lowercase			,			_lowercase      			=config_and_inputs
        _lowercase      			={'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    (
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  _a		     =    (
      {
          """feature-extraction""": TFDeiTModel,
          """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  _a		     =    False
  _a		     =    False
  _a		     =    False
  _a		     =    False




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=TFDeiTModelTester(self   )
        _lowercase      			=ConfigTester(self				,  config_class=lowerCAmelCase				,  has_text_modality=lowerCAmelCase				,  hidden_size=37   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  @unittest.skip(reason='DeiT does not use inputs_embeds'   )
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        pass




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )
              self.assertIsInstance(model.get_input_embeddings()				,  (tf.keras.layers.Layer)   )
              _lowercase      			=model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase				,  tf.keras.layers.Dense   )   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase      			=['pixel_values']
              self.assertListEqual(arg_names[:1]				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=False   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=super()._prepare_for_class(lowerCAmelCase				,  lowerCAmelCase				,  return_labels=lowerCAmelCase   )

        if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call   ).parameters:
                    del inputs_dict["labels"]

        return inputs_dict




  @slow
  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase      			=TFDeiTModel.from_pretrained(lowerCAmelCase   )
              self.assertIsNotNone(lowerCAmelCase   )
def 	a (     )       -> Optional[Any]:





      """simple docstring"""



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







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




  @cached_property
  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        return (
            DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'   )
            if is_vision_available()
            else None
        )




  @slow
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'   )

        _lowercase      			=self.default_image_processor
        _lowercase      			=prepare_img()
        _lowercase      			=image_processor(images=lowerCAmelCase				,  return_tensors='tf'   )

        # forward pass
        _lowercase      			=model(**lowerCAmelCase   )

        # verify the logits
        _lowercase      			=tf.TensorShape((1, 1_000)   )
        self.assertEqual(outputs.logits.shape				,  lowerCAmelCase   )

        _lowercase      			=tf.constant([-1.0266, 0.1912, -1.2861]   )

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





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--repo_path',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      'image_size': 'sample_size',
      'num_res_blocks': 'layers_per_block',
      'block_channels': 'block_out_channels',
      'down_blocks': 'down_block_types',
      'up_blocks': 'up_block_types',
      'downscale_freq_shift': 'freq_shift',
      'resnet_num_groups': 'norm_num_groups',
      'resnet_act_fn': 'act_fn',
      'resnet_eps': 'norm_eps',
      'num_head_channels': 'attention_head_dim',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' if has_file(args.repo_path, 'config.json') else 'unet'

  with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
    lowercase_   							=  reader.read()
    lowercase_   							=  json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, 'config.json'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowercase_   							=  dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowercase_   							=  config[key]
        del config[key]

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

  if do_only_weights:
    lowercase_   							=  torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

    lowercase_   							=  {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
        continue
      lowercase_   							=  False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split('.')[0] == key:
          lowercase_   							=  param_value
          lowercase_   							=  True
      if not has_changed:
        lowercase_   							=  param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))


