





import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_=100							,    snake_case_=13							,    snake_case_=30							,    snake_case_=2							,    snake_case_=3							,    snake_case_=True							,    snake_case_=True							,    snake_case_=32							,    snake_case_=4							,    snake_case_=4							,    snake_case_=37							,    snake_case_="gelu"							,    snake_case_=0.1							,    snake_case_=0.1							,    snake_case_=10							,    snake_case_=0.02							,    snake_case_=3							,    snake_case_=None							,    snake_case_=[0, 1, 2, 3]							,    ):
         _A    =       parent
         _A    =       100
         _A    =       batch_size
         _A    =       image_size
         _A    =       patch_size
         _A    =       num_channels
         _A    =       is_training
         _A    =       use_labels
         _A    =       hidden_size
         _A    =       num_hidden_layers
         _A    =       num_attention_heads
         _A    =       intermediate_size
         _A    =       hidden_act
         _A    =       hidden_dropout_prob
         _A    =       attention_probs_dropout_prob
         _A    =       type_sequence_label_size
         _A    =       initializer_range
         _A    =       scope
         _A    =       out_indices
         _A    =       num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _A    =       (image_size // patch_size) ** 2
         _A    =       num_patches + 1




     def 					lowerCAmelCase__		(						self       ):
         _A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         _A    =       None
         _A    =       None
         if self.use_labels:
             _A    =       ids_tensor([self.batch_size]							,    self.type_sequence_label_size       )
             _A    =       ids_tensor([self.batch_size, self.image_size, self.image_size]							,    self.num_labels       )

         _A    =       self.get_config()

         return config, pixel_values, labels, pixel_labels




     def 					lowerCAmelCase__		(						self       ):
         return DataaVecVisionConfig(
             vocab_size=self.vocab_size							,    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=snake_case_							,    initializer_range=self.initializer_range							,    out_indices=self.out_indices							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       DataaVecVisionModel(config=snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         _A    =       (self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, num_patches + 1, self.hidden_size)       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       self.type_sequence_label_size
         _A    =       DataaVecVisionForImageClassification(snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_							,    labels=snake_case_       )
         self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.type_sequence_label_size)       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       self.num_labels
         _A    =       DataaVecVisionForSemanticSegmentation(snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )
         self.parent.assertEqual(
             result.logits.shape							,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)       )
         _A    =       model(snake_case_							,    labels=snake_case_       )
         self.parent.assertEqual(
             result.logits.shape							,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)       )




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







@require_torch
class     lowerCamelCase(	__snake_case ,   __snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __magic_name__        = (
         {
             'feature-extraction': DataaVecVisionModel,
             'image-classification': DataaVecVisionForImageClassification,
             'image-segmentation': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __magic_name__        = False
     __magic_name__        = False
     __magic_name__        = False




     def 					lowerCAmelCase__		(						self       ):
         _A    =       DataaVecVisionModelTester(self       )
         _A    =       ConfigTester(
             self							,    config_class=snake_case_							,    has_text_modality=snake_case_							,    hidden_size=37       )




     def 					lowerCAmelCase__		(						self       ):
         self.config_tester.run_common_tests()




     def 					lowerCAmelCase__		(						self       ):
         # Data2VecVision does not use inputs_embeds
         pass




     @require_torch_multi_gpu
     @unittest.skip(
         reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'       )
     def 					lowerCAmelCase__		(						self       ):
         pass




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A    =       model_class(snake_case_       )
             self.assertIsInstance(model.get_input_embeddings()							,    (nn.Module)       )
             _A    =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case_							,    nn.Linear       )       )




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

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

             _A    =       ['pixel_values']
             self.assertListEqual(arg_names[:1]							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         if not self.model_tester.is_training:
             return

         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()
         _A    =       True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(snake_case_       )]:
                 continue

             _A    =       model_class(snake_case_       )
             model.to(snake_case_       )
             model.train()
             _A    =       self._prepare_for_class(snake_case_							,    snake_case_							,    return_labels=snake_case_       )
             _A    =       model(**snake_case_       ).loss
             loss.backward()




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         _A    =       False
         _A    =       True

         for model_class in self.all_model_classes:
             if model_class in [*get_values(snake_case_       )] or not model_class.supports_gradient_checkpointing:
                 continue
             # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
             # this can then be incorporated into _prepare_for_class in test_modeling_common.py
             elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                 _A, _A, _A, _A    =       inputs_dict['pixel_values'].shape
                 _A    =       torch.zeros(
                     [self.model_tester.batch_size, height, width]							,    device=snake_case_       ).long()
             _A    =       model_class(snake_case_       )
             model.gradient_checkpointing_enable()
             model.to(snake_case_       )
             model.train()
             _A    =       self._prepare_for_class(snake_case_							,    snake_case_							,    return_labels=snake_case_       )
             _A    =       model(**snake_case_       ).loss
             loss.backward()




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

         _A    =       _config_zero_init(snake_case_       )
         for model_class in self.all_model_classes:
             _A    =       model_class(config=snake_case_       )
             for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                     continue
                 if param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1E9).round() / 1E9).item()							,    [0.0, 1.0]							,    msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_=2E-4							,    snake_case_="outputs"							,    snake_case_=None       ):
         # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
         super().check_pt_tf_outputs(snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    =       DataaVecVisionModel.from_pretrained(snake_case_       )
             self.assertIsNotNone(snake_case_       )





def 							__lowerCAmelCase(					)       ->   Dict:




    """simple docstring"""




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







@require_torch
@require_vision
class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     @cached_property
     def 					lowerCAmelCase__		(						self       ):
         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'       ) if is_vision_available() else None
         )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'       ).to(
             snake_case_       )

         _A    =       self.default_image_processor
         _A    =       prepare_img()
         _A    =       image_processor(images=snake_case_							,    return_tensors='pt'       ).to(snake_case_       )

         # forward pass
         with torch.no_grad():
             _A    =       model(**snake_case_       )
         _A    =       outputs.logits

         # verify the logits
         _A    =       torch.Size((1, 1000)       )
         self.assertEqual(logits.shape							,    snake_case_       )

         _A    =       torch.tensor([0.3277, -0.1395, 0.0911]       ).to(snake_case_       )

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

         _A    =       [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2       ).indices.cpu().tolist()							,    snake_case_       )












import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

    # Embeddings
    # Position IDs
    _A    =       fnet_pretraining_model.state_dict()

    _A    =       state_dict['fnet.embeddings.position_ids']
    _A    =       {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

    # Encoder Layers
    for layer in range(config.num_hidden_layers							):
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']							)

    # Pooler Layers
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

    # Masked LM Layers
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']							)
    _A    =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

    # Seq Relationship Layers
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_kernel']							)
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_bias']							)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE							)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"							)
    fnet_pretraining_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : str						    = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--fnet_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained FNet model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





