




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  A		(  tf.Module     ):



								def __init__(     self,	UpperCamelCase__   ):







														"""simple docstring"""



														super().__init__()
														lowerCAmelCase_      			=  tokenizer
														lowerCAmelCase_      			=  AutoConfig.from_pretrained(UpperCamelCase__   )
														lowerCAmelCase_      			=  TFGPTaLMHeadModel.from_config(UpperCamelCase__   )







								@tf.function(input_signature=(tf.TensorSpec((None,),	tf.string,	name='''text'''   ),)   )
								def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







														"""simple docstring"""



														lowerCAmelCase_      			=  self.tokenizer(UpperCamelCase__   )
														lowerCAmelCase_      			=  tokenized['''input_ids'''].to_tensor()

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

														lowerCAmelCase_      			=  self.model(input_ids=UpperCamelCase__,	attention_mask=UpperCamelCase__   )['''logits''']

														return outputs


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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								super().setUp()

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

								lowerCAmelCase_      			=  [
								    '''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''',
								]
								lowerCAmelCase_      			=  list(zip(self.test_sentences,	self.test_sentences[::-1]   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tokenizer, tf_tokenizer in zip(self.tokenizers,	self.tf_tokenizers   ):
														for test_inputs in self.test_sentences:
																				lowerCAmelCase_      			=  tokenizer([test_inputs],	return_tensors='''tf'''   )
																				lowerCAmelCase_      			=  tf_tokenizer([test_inputs]   )

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

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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  tf.function(UpperCamelCase__   )
														for test_inputs in self.test_sentences:
																				lowerCAmelCase_      			=  tf.constant(UpperCamelCase__   )
																				lowerCAmelCase_      			=  compiled_tokenizer(UpperCamelCase__   )
																				lowerCAmelCase_      			=  tf_tokenizer(UpperCamelCase__   )

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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  ModelToSave(tokenizer=UpperCamelCase__   )
														lowerCAmelCase_      			=  tf.convert_to_tensor([self.test_sentences[0]]   )
														lowerCAmelCase_      			=  model.serving(UpperCamelCase__   )  # Build model with some sample inputs
														with TemporaryDirectory() as tempdir:
																				lowerCAmelCase_      			=  Path(UpperCamelCase__   ) / '''saved.model'''
																				tf.saved_model.save(UpperCamelCase__,	UpperCamelCase__,	signatures={'''serving_default''': model.serving}   )
																				lowerCAmelCase_      			=  tf.saved_model.load(UpperCamelCase__   )
														lowerCAmelCase_      			=  loaded_model.signatures['''serving_default'''](UpperCamelCase__   )['''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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														lowerCAmelCase_      			=  tf.convert_to_tensor([self.test_sentences[0]]   )
														lowerCAmelCase_      			=  tf_tokenizer(UpperCamelCase__   )  # Build model with some sample inputs

														lowerCAmelCase_      			=  tf_tokenizer.get_config()
														lowerCAmelCase_      			=  TFGPTaTokenizer.from_config(UpperCamelCase__   )
														lowerCAmelCase_      			=  model_from_config(UpperCamelCase__   )

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







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														lowerCAmelCase_      			=  12_3123

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

																				lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  checkpoints.load_tax_checkpoint(_A  )
						lowerCAmelCase_      			=  flatten_dict(_A  )
						return flax_params





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  {}

						lowerCAmelCase_      			=  {
						    '''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''',
						}

						lowerCAmelCase_      			=  {
						    '''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
																		lowerCAmelCase_      			=  '''.'''.join(key[1:]  )

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

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

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

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

																		lowerCAmelCase_      			=  flax_dict[key]

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

						return converted_torch_dict





def __UpperCamelCase			(       _A ,  _A ,  _A=False ,  _A=False  ):
						lowerCAmelCase_      			=  get_flax_param(_A  )

						if not use_large:
												lowerCAmelCase_      			=  PixaStructVisionConfig()
												lowerCAmelCase_      			=  PixaStructTextConfig()
						else:
												lowerCAmelCase_      			=  PixaStructVisionConfig(
												    hidden_size=1536 ,  d_ff=3968 ,  num_attention_heads=24 ,  num_hidden_layers=18  )
												lowerCAmelCase_      			=  PixaStructTextConfig(hidden_size=1536 ,  d_ff=3968 ,  num_heads=24 ,  num_layers=18  )
						lowerCAmelCase_      			=  PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,  text_config=decoder_config.to_dict() ,  is_vqa=_A  )

						lowerCAmelCase_      			=  PixaStructForConditionalGeneration(_A  )

						lowerCAmelCase_      			=  rename_and_convert_flax_params(_A  )
						model.load_state_dict(_A  )

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

						if use_large:
												lowerCAmelCase_      			=  4096

						lowerCAmelCase_      			=  True

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

						model.save_pretrained(_A  )
						processor.save_pretrained(_A  )

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


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
						)




