

"""simple docstring"""





import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import FlavaImageProcessor, FlavaProcessor
				from transformers.models.flava.image_processing_flava import (
				    FLAVA_CODEBOOK_MEAN,
				    FLAVA_CODEBOOK_STD,
				    FLAVA_IMAGE_MEAN,
				    FLAVA_IMAGE_STD,
				)

@require_vision
class 			__a      (					unittest.TestCase			):

			"""simple docstring"""

			def       _lowerCAmelCase	(							self   :    Any  ):
								UpperCamelCase__						:			Optional[Any]				     =tempfile.mkdtemp()

								# fmt: off
								UpperCamelCase__						:			int				     =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
								# fmt: on
								UpperCamelCase__						:			Dict				     =os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''vocab_file''']  )

								with open(self.vocab_file  ,     '''w'''  ,     encoding='''utf-8'''  ) as fp:
													fp.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

								UpperCamelCase__						:			int				     ={
								    '''image_mean''': FLAVA_IMAGE_MEAN,
								    '''image_std''': FLAVA_IMAGE_STD,
								    '''do_normalize''': True,
								    '''do_resize''': True,
								    '''size''': 224,
								    '''do_center_crop''': True,
								    '''crop_size''': 224,
								    '''input_size_patches''': 14,
								    '''total_mask_patches''': 75,
								    '''mask_group_max_patches''': None,
								    '''mask_group_min_patches''': 16,
								    '''mask_group_min_aspect_ratio''': 0.3,
								    '''mask_group_max_aspect_ratio''': None,
								    '''codebook_do_resize''': True,
								    '''codebook_size''': 112,
								    '''codebook_do_center_crop''': True,
								    '''codebook_crop_size''': 112,
								    '''codebook_do_map_pixels''': True,
								    '''codebook_do_normalize''': True,
								    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
								    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
								}

								UpperCamelCase__						:			Union[str, Any]				     =os.path.join(self.tmpdirname  ,     SCREAMING_SNAKE_CASE__  )
								with open(self.image_processor_file  ,     '''w'''  ,     encoding='''utf-8'''  ) as fp:
													json.dump(SCREAMING_SNAKE_CASE__  ,     SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    Any  ,     **lowercase_   :    str  ):
								return BertTokenizer.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    str  ,     **lowercase_   :    Dict  ):
								return BertTokenizerFast.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    Union[str, Any]  ,     **lowercase_   :    Any  ):
								return FlavaImageProcessor.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    Dict  ):
								shutil.rmtree(self.tmpdirname  )






			def       _lowerCAmelCase	(							self   :    str  ):
								UpperCamelCase__						:			Optional[int]				     =[np.random.randint(255  ,     size=(3, 30, 400)  ,     dtype=np.uinta  )]

								UpperCamelCase__						:			Dict				     =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__  ,     0  ,     -1  )  ) for x in image_inputs]

								return image_inputs






			def       _lowerCAmelCase	(							self   :    Optional[Any]  ):
								UpperCamelCase__						:			str				     =self.get_tokenizer()
								UpperCamelCase__						:			List[str]				     =self.get_rust_tokenizer()
								UpperCamelCase__						:			str				     =self.get_image_processor()

								UpperCamelCase__						:			Any				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )
								processor_slow.save_pretrained(self.tmpdirname  )
								UpperCamelCase__						:			List[str]				     =FlavaProcessor.from_pretrained(self.tmpdirname  ,     use_fast=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			List[Any]				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )
								processor_fast.save_pretrained(self.tmpdirname  )
								UpperCamelCase__						:			Optional[int]				     =FlavaProcessor.from_pretrained(self.tmpdirname  )

								self.assertEqual(processor_slow.tokenizer.get_vocab()  ,     tokenizer_slow.get_vocab()  )
								self.assertEqual(processor_fast.tokenizer.get_vocab()  ,     tokenizer_fast.get_vocab()  )
								self.assertEqual(tokenizer_slow.get_vocab()  ,     tokenizer_fast.get_vocab()  )
								self.assertIsInstance(processor_slow.tokenizer  ,     SCREAMING_SNAKE_CASE__  )
								self.assertIsInstance(processor_fast.tokenizer  ,     SCREAMING_SNAKE_CASE__  )

								self.assertEqual(processor_slow.image_processor.to_json_string()  ,     image_processor.to_json_string()  )
								self.assertEqual(processor_fast.image_processor.to_json_string()  ,     image_processor.to_json_string()  )
								self.assertIsInstance(processor_slow.image_processor  ,     SCREAMING_SNAKE_CASE__  )
								self.assertIsInstance(processor_fast.image_processor  ,     SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    Dict  ):
								UpperCamelCase__						:			Optional[Any]				     =FlavaProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()  )
								processor.save_pretrained(self.tmpdirname  )

								UpperCamelCase__						:			Union[str, Any]				     =self.get_tokenizer(bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''  )
								UpperCamelCase__						:			Optional[Any]				     =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__  ,     padding_value=1.0  )

								UpperCamelCase__						:			int				     =FlavaProcessor.from_pretrained(
								    self.tmpdirname  ,     bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''  ,     do_normalize=SCREAMING_SNAKE_CASE__  ,     padding_value=1.0  )

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

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






			def       _lowerCAmelCase	(							self   :    str  ):
								UpperCamelCase__						:			Optional[int]				     =self.get_image_processor()
								UpperCamelCase__						:			Union[str, Any]				     =self.get_tokenizer()

								UpperCamelCase__						:			Union[str, Any]				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			Tuple				     =self.prepare_image_inputs()

								UpperCamelCase__						:			List[Any]				     =image_processor(SCREAMING_SNAKE_CASE__  ,     return_tensors='''np'''  )
								UpperCamelCase__						:			Union[str, Any]				     =processor(images=SCREAMING_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  )

								# With rest of the args
								random.seed(1234  )
								UpperCamelCase__						:			Any				     =image_processor(
								    SCREAMING_SNAKE_CASE__  ,     return_image_mask=SCREAMING_SNAKE_CASE__  ,     return_codebook_pixels=SCREAMING_SNAKE_CASE__  ,     return_tensors='''np'''  )
								random.seed(1234  )
								UpperCamelCase__						:			str				     =processor(
								    images=SCREAMING_SNAKE_CASE__  ,     return_image_mask=SCREAMING_SNAKE_CASE__  ,     return_codebook_pixels=SCREAMING_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   :    List[str]  ):
								UpperCamelCase__						:			Optional[Any]				     =self.get_image_processor()
								UpperCamelCase__						:			Any				     =self.get_tokenizer()

								UpperCamelCase__						:			str				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			str				     ='''lower newer'''

								UpperCamelCase__						:			int				     =processor(text=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			str				     =tokenizer(SCREAMING_SNAKE_CASE__  )

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






			def       _lowerCAmelCase	(							self   :    List[Any]  ):
								UpperCamelCase__						:			Optional[Any]				     =self.get_image_processor()
								UpperCamelCase__						:			List[str]				     =self.get_tokenizer()

								UpperCamelCase__						:			int				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			int				     ='''lower newer'''
								UpperCamelCase__						:			Optional[int]				     =self.prepare_image_inputs()

								UpperCamelCase__						:			List[str]				     =processor(text=SCREAMING_SNAKE_CASE__  ,     images=SCREAMING_SNAKE_CASE__  )

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

								# add extra args
								UpperCamelCase__						:			Dict				     =processor(text=SCREAMING_SNAKE_CASE__  ,     images=SCREAMING_SNAKE_CASE__  ,     return_codebook_pixels=SCREAMING_SNAKE_CASE__  ,     return_image_mask=SCREAMING_SNAKE_CASE__  )

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

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






			def       _lowerCAmelCase	(							self   :    str  ):
								UpperCamelCase__						:			Optional[Any]				     =self.get_image_processor()
								UpperCamelCase__						:			Optional[int]				     =self.get_tokenizer()

								UpperCamelCase__						:			Union[str, Any]				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			Union[str, Any]				     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCamelCase__						:			Any				     =processor.batch_decode(SCREAMING_SNAKE_CASE__  )
								UpperCamelCase__						:			str				     =tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  )

								self.assertListEqual(SCREAMING_SNAKE_CASE__  ,     SCREAMING_SNAKE_CASE__  )






			def       _lowerCAmelCase	(							self   :    Tuple  ):
								UpperCamelCase__						:			Optional[int]				     =self.get_image_processor()
								UpperCamelCase__						:			Dict				     =self.get_tokenizer()

								UpperCamelCase__						:			Optional[Any]				     =FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,     image_processor=SCREAMING_SNAKE_CASE__  )

								UpperCamelCase__						:			Optional[Any]				     ='''lower newer'''
								UpperCamelCase__						:			Tuple				     =self.prepare_image_inputs()

								UpperCamelCase__						:			Optional[Any]				     =processor(text=SCREAMING_SNAKE_CASE__  ,     images=SCREAMING_SNAKE_CASE__  )

								self.assertListEqual(list(inputs.keys()  )  ,     processor.model_input_names  )








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 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     lowerCAmelCase__		= checkpoint_dict["target"]

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

     lowerCAmelCase__		= state_dict["fnet.embeddings.position_ids"]
     lowerCAmelCase__		= {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]			)

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

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

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

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

     # Pooler Layers
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]			)

     # Masked LM Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]			)
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]			)

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

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase_			)

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


if __name__ == "__main__":
     UpperCamelCase								=     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.')
     UpperCamelCase								=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
