


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 	lowerCAmelCase       (						lowerCAmelCase_       )->       List[str]:
				lowerCAmelCase_       :					List[str]			 = checkpoints.load_tax_checkpoint(_lowercase       )
				lowerCAmelCase_       :					Union[str, Any]			 = flatten_dict(_lowercase       )
				return flax_params



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       str:
				lowerCAmelCase_       :					List[Any]			 = {}

				lowerCAmelCase_       :					Optional[Any]			 = {
				    """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_       :					Tuple			 = {
				    """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_       :					str			 = """.""".join(key[1:]       )

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

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

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

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

												lowerCAmelCase_       :					Optional[int]			 = flax_dict[key]

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

				return converted_torch_dict



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_=False       ,      lowerCAmelCase_=False       )->       Any:
				lowerCAmelCase_       :					Optional[Any]			 = get_flax_param(_lowercase       )

				if not use_large:
								lowerCAmelCase_       :					Dict			 = PixaStructVisionConfig()
								lowerCAmelCase_       :					Any			 = PixaStructTextConfig()
				else:
								lowerCAmelCase_       :					Union[str, Any]			 = PixaStructVisionConfig(
								    hidden_size=1_536       ,      d_ff=3_968       ,      num_attention_heads=24       ,      num_hidden_layers=18       )
								lowerCAmelCase_       :					str			 = PixaStructTextConfig(hidden_size=1_536       ,      d_ff=3_968       ,      num_heads=24       ,      num_layers=18       )
				lowerCAmelCase_       :					int			 = PixaStructConfig(
				    vision_config=encoder_config.to_dict()       ,      text_config=decoder_config.to_dict()       ,      is_vqa=_lowercase       )

				lowerCAmelCase_       :					Union[str, Any]			 = PixaStructForConditionalGeneration(_lowercase       )

				lowerCAmelCase_       :					Union[str, Any]			 = rename_and_convert_flax_params(_lowercase       )
				model.load_state_dict(_lowercase       )

				lowerCAmelCase_       :					Any			 = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
				lowerCAmelCase_       :					str			 = PixaStructImageProcessor()
				lowerCAmelCase_       :					Any			 = PixaStructProcessor(image_processor=_lowercase       ,      tokenizer=_lowercase       )

				if use_large:
								lowerCAmelCase_       :					List[str]			 = 4_096

				lowerCAmelCase_       :					Tuple			 = True

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

				model.save_pretrained(_lowercase       )
				processor.save_pretrained(_lowercase       )

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


if __name__ == "__main__":
						_UpperCAmelCase	:     Tuple   =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.""")
						_UpperCAmelCase	:     Tuple   =parser.parse_args()

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




'''simple docstring'''




import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
							from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
							from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
							from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


a   :			str					  =    DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

a   :			int					  =    """main"""
# Default branch name
a   :			Any					  =    """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
a   :			str					  =    """aaaaaaa"""
# This commit does not exist, so we should 404.

a   :			int					  =    """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
a   :			Any					  =    """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			List[str]:
							print("""Welcome!"""		)
							yield
							print("""Bye!"""		)



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			Optional[int]:
							print("""Bonjour!"""		)
							yield
							print("""Au revoir!"""		)

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			List[Any]:
								# If the spec is missing, importlib would not be able to import the module dynamically.
								assert transformers.__spec__ is not None
								assert importlib.util.find_spec("""transformers"""    ) is not None




class 			UpperCamelCase_ (							unittest.TestCase			):





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Tuple:
								with ContextManagers([]    ):
															print("""Transformers are awesome!"""    )
								# The print statement adds a new line at the end of the output
								self.assertEqual(mock_stdout.getvalue()		,		"""Transformers are awesome!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Dict:
								with ContextManagers([context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Welcome!\nTransformers are awesome!\nBye!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								with ContextManagers([context_fr(), context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English and French welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""    )





	@require_torch
	def  _lowercase(  self    )			->			Optional[int]:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )





	@require_tf
	def  _lowercase(  self    )			->			int:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )






	@require_flax
	def  _lowercase(  self    )			->			Any:
								# Flax models don't have labels
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		[]    )

