





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







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


     def 					lowerCAmelCase__		(						self       ):
         _A    =       tempfile.mkdtemp()

         _A    =       CLIPImageProcessor()
         _A    =       BertTokenizer.from_pretrained(
             'hf-internal-testing/tiny-random-BertModel'							,    model_input_names=['input_ids', 'attention_mask']       )

         _A    =       GitProcessor(snake_case_							,    snake_case_       )

         processor.save_pretrained(self.tmpdirname       )




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return AutoProcessor.from_pretrained(self.tmpdirname							,    **snake_case_       ).tokenizer




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return AutoProcessor.from_pretrained(self.tmpdirname							,    **snake_case_       ).image_processor




     def 					lowerCAmelCase__		(						self       ):
         shutil.rmtree(self.tmpdirname       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       [np.random.randint(255							,    size=(3, 30, 400)							,    dtype=np.uinta       )]

         _A    =       [Image.fromarray(np.moveaxis(snake_case_							,    0							,    -1       )       ) for x in image_inputs]

         return image_inputs




     def 					lowerCAmelCase__		(						self       ):
         _A    =       GitProcessor(tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         _A    =       self.get_tokenizer(bos_token='(BOS)'							,    eos_token='(EOS)'       )
         _A    =       self.get_image_processor(do_normalize=snake_case_							,    padding_value=1.0       )

         _A    =       GitProcessor.from_pretrained(
             self.tmpdirname							,    bos_token='(BOS)'							,    eos_token='(EOS)'							,    do_normalize=snake_case_							,    padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()							,    tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer							,    snake_case_       )

         self.assertEqual(processor.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_image_processor()
         _A    =       self.get_tokenizer()

         _A    =       GitProcessor(tokenizer=snake_case_							,    image_processor=snake_case_       )

         _A    =       self.prepare_image_inputs()

         _A    =       image_processor(snake_case_							,    return_tensors='np'       )
         _A    =       processor(images=snake_case_							,    return_tensors='np'       )

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()							,    input_processor[key].sum()							,    delta=1E-2       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_image_processor()
         _A    =       self.get_tokenizer()

         _A    =       GitProcessor(tokenizer=snake_case_							,    image_processor=snake_case_       )

         _A    =       'lower newer'

         _A    =       processor(text=snake_case_       )

         _A    =       tokenizer(snake_case_							,    return_token_type_ids=snake_case_       )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]							,    encoded_processor[key]       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_image_processor()
         _A    =       self.get_tokenizer()

         _A    =       GitProcessor(tokenizer=snake_case_							,    image_processor=snake_case_       )

         _A    =       'lower newer'
         _A    =       self.prepare_image_inputs()

         _A    =       processor(text=snake_case_							,    images=snake_case_       )

         self.assertListEqual(list(inputs.keys()       )							,    ['input_ids', 'attention_mask', 'pixel_values']       )

         # test if it raises when no input is passed
         with pytest.raises(snake_case_       ):
             processor()




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_image_processor()
         _A    =       self.get_tokenizer()

         _A    =       GitProcessor(tokenizer=snake_case_							,    image_processor=snake_case_       )

         _A    =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _A    =       processor.batch_decode(snake_case_       )
         _A    =       tokenizer.batch_decode(snake_case_       )

         self.assertListEqual(snake_case_							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.get_image_processor()
         _A    =       self.get_tokenizer()

         _A    =       GitProcessor(tokenizer=snake_case_							,    image_processor=snake_case_       )

         _A    =       'lower newer'
         _A    =       self.prepare_image_inputs()

         _A    =       processor(text=snake_case_							,    images=snake_case_       )

         # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
         self.assertListEqual(list(inputs.keys()       )							,    ['input_ids', 'attention_mask', 'pixel_values']       )












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)





