

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


__A      		=     ['''gpt2''']
__A      		=     '''gpt2'''

if is_tf_available():



 class   lowercase_   (  tf.Module					):



       def __init__(      self				:    List[str]      , A__				:    List[str]		)   ->					Optional[Any]:
         super().__init__()
         _snake_case						=							tokenizer
         _snake_case						=							AutoConfig.from_pretrained(A__		)
         _snake_case						=							TFGPTaLMHeadModel.from_config(A__		)







       @tf.function(input_signature=(tf.TensorSpec((None,)      , tf.string      , name='''text'''		),)		)
       def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Dict		)   ->					int:
         _snake_case						=							self.tokenizer(A__		)
         _snake_case						=							tokenized['''input_ids'''].to_tensor()

         _snake_case						=							tf.cast(input_ids_dense > 0      , tf.intaa		)
         # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

         _snake_case						=							self.model(input_ids=A__      , attention_mask=A__		)['''logits''']

         return outputs



@require_tf
@require_keras_nlp
class   lowercase_   (  unittest.TestCase					):



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					str:
        super().setUp()

        _snake_case						=							[GPTaTokenizer.from_pretrained(A__		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
        _snake_case						=							[TFGPTaTokenizer.from_pretrained(A__		) for checkpoint in TOKENIZER_CHECKPOINTS]
        assert len(self.tokenizers		) == len(self.tf_tokenizers		)

        _snake_case						=							[
            '''This is a straightforward English test sentence.''',
            '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
            '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
            '''And some much more rare Chinese: 齉 堃 齉堃''',
            '''Je vais aussi écrire en français pour tester les accents''',
            '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
        ]
        _snake_case						=							list(zip(self.test_sentences      , self.test_sentences[::-1]		)		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[int]:
        for tokenizer, tf_tokenizer in zip(self.tokenizers      , self.tf_tokenizers		):
          for test_inputs in self.test_sentences:
            _snake_case						=							tokenizer([test_inputs]      , return_tensors='''tf'''		)
            _snake_case						=							tf_tokenizer([test_inputs]		)

            for key in python_outputs.keys():
              # convert them to numpy to avoid messing with ragged tensors
              _snake_case						=							python_outputs[key].numpy()
              _snake_case						=							tf_outputs[key].numpy()

              self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
              self.assertTrue(tf.reduce_all(tf.cast(A__      , tf.intaa		) == tf_outputs_values		)		)



      @slow
      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        for tf_tokenizer in self.tf_tokenizers:
          _snake_case						=							tf.function(A__		)
          for test_inputs in self.test_sentences:
            _snake_case						=							tf.constant(A__		)
            _snake_case						=							compiled_tokenizer(A__		)
            _snake_case						=							tf_tokenizer(A__		)

            for key in eager_outputs.keys():
              self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)



      @slow
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Dict:
        for tf_tokenizer in self.tf_tokenizers:
          _snake_case						=							ModelToSave(tokenizer=A__		)
          _snake_case						=							tf.convert_to_tensor([self.test_sentences[0]]		)
          _snake_case						=							model.serving(A__		)  # Build model with some sample inputs
          with TemporaryDirectory() as tempdir:
            _snake_case						=							Path(A__		) / '''saved.model'''
            tf.saved_model.save(A__      , A__      , signatures={'''serving_default''': model.serving}		)
            _snake_case						=							tf.saved_model.load(A__		)
          _snake_case						=							loaded_model.signatures['''serving_default'''](A__		)['''output_0''']
          # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
          self.assertTrue(tf.reduce_all(out == loaded_output		)		)



      @slow
      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        for tf_tokenizer in self.tf_tokenizers:
          _snake_case						=							tf.convert_to_tensor([self.test_sentences[0]]		)
          _snake_case						=							tf_tokenizer(A__		)  # Build model with some sample inputs

          _snake_case						=							tf_tokenizer.get_config()
          _snake_case						=							TFGPTaTokenizer.from_config(A__		)
          _snake_case						=							model_from_config(A__		)

          for key in from_config_output.keys():
            self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)







      @slow
      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        for tf_tokenizer in self.tf_tokenizers:
          # for the test to run
          _snake_case						=							123123

          for max_length in [3, 5, 1024]:
            _snake_case						=							tf.convert_to_tensor([self.test_sentences[0]]		)
            _snake_case						=							tf_tokenizer(A__      , max_length=A__		)

            _snake_case						=							out['''input_ids'''].numpy().shape[1]

            assert out_length == max_length




import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							{}

  _snake_case						=							{
      '''token_embedder''': '''embeddings''',
      '''encoder_norm''': '''layernorm''',
      '''kernel''': '''weight''',
      '''.out''': '''.output''',
      '''scale''': '''weight''',
      '''embedders_0.pos_embedding''': '''row_embedder.weight''',
      '''embedders_1.pos_embedding''': '''column_embedder.weight''',
  }

  _snake_case						=							{
      '''query''': '''attention.query''',
      '''key''': '''attention.key''',
      '''value''': '''attention.value''',
      '''output.dense''': '''output''',
      '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
      '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
      '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
      '''mlp.''': '''mlp.DenseReluDense.''',
      '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
      '''self_attention.o''': '''self_attention.attention.o''',
      '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
      '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
      '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
      '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _snake_case						=							'''.'''.join(key[1:]					)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case						=							new_key.replace(_UpperCamelCase			,	_UpperCamelCase					)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case						=							new_key.replace(_UpperCamelCase			,	_UpperCamelCase					)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)
        _snake_case						=							new_key.replace('''encoder'''			,	'''encoder.encoder'''					)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)

      _snake_case						=							flax_dict[key]

  _snake_case						=							{}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _snake_case						=							torch.from_numpy(converted_dict[key].T					)
    else:
      _snake_case						=							torch.from_numpy(converted_dict[key]					)

  return converted_torch_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

  _snake_case						=							AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
  _snake_case						=							PixaStructImageProcessor()
  _snake_case						=							PixaStructProcessor(image_processor=_UpperCamelCase			,	tokenizer=_UpperCamelCase					)

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

  # mkdir if needed
  os.makedirs(_UpperCamelCase			,	exist_ok=_UpperCamelCase					)

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

  print('''Model saved in {}'''.format(_UpperCamelCase					)					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
 parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
 __A      		=     parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )

