import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase__  	=      False

if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    PixaStructImageProcessor,
						    PixaStructProcessor,
						    PreTrainedTokenizerFast,
						    TaTokenizer,
						)
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,		)
@require_vision
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

		_lowercase   =PixaStructImageProcessor()
		_lowercase   =TaTokenizer.from_pretrained("t5-small"       )

		_lowercase   =PixaStructProcessor(lowerCAmelCase_       ,  lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).tokenizer


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).image_processor


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


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

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

		return image_inputs


	def __lowerCAmelCase      (   self       ):
		_lowercase   =PixaStructProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

		_lowercase   =self.get_tokenizer(bos_token="(BOS)"       ,  eos_token="(EOS)"       )
		_lowercase   =self.get_image_processor(do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

		_lowercase   =PixaStructProcessor.from_pretrained(
		    self.tmpdirname       ,  bos_token="(BOS)"       ,  eos_token="(EOS)"       ,  do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   =self.prepare_image_inputs()

		_lowercase   =image_processor(lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(images=lowerCAmelCase_       ,  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       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"

		_lowercase   =processor(text=lowerCAmelCase_       )

		_lowercase   =tokenizer(lowerCAmelCase_       ,  return_token_type_ids=lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		self.assertListEqual(
		    list(inputs.keys()       )       ,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		_lowercase   =[512, 1024, 2048, 4096]
		_lowercase   =[770, 770, 770, 770]
		# with text
		for i, max_patch in enumerate(lowerCAmelCase_       ):
			_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       ,  max_patches=lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[0]       ,  lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[1]       ,  expected_hidden_size[i]       )

		# without text input
		for i, max_patch in enumerate(lowerCAmelCase_       ):
			_lowercase   =processor(images=lowerCAmelCase_       ,  max_patches=lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[0]       ,  lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[1]       ,  expected_hidden_size[i]       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

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

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(
		    list(inputs.keys()       )       ,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

		_lowercase   =processor(text=lowerCAmelCase_       )

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










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__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __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(A__      )

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


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)

