






"""simple docstring"""







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a			:    Any				 =		"""src/diffusers"""

# Matches is_xxx_available()
a			:    Optional[Any]				 =		re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a			:    Dict				 =		re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a			:    Dict				 =		"""
{0} = None
"""

a			:    List[Any]				 =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a			:    Any				 =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""





def 						lowercase__(A						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	_re_backend.findall(A						)
						if len(A						) == 0:
												return None

						return "_and_".join(A						)





def 						lowercase__()    ->int:




						"""simple docstring"""



						with open(os.path.join(A ,		"__init__.py"						) ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
												lowercase__    :				Union[str, Any]=	f.readlines()

						# Get to the point we do the actual imports for type checking
						lowercase__    :				List[Any]=	0
						lowercase__    :				List[Any]=	{}
						# Go through the end of the file
						while line_index < len(A						):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												lowercase__    :				Optional[Any]=	find_backend(lines[line_index]						)
												if backend is not None:
																		while not lines[line_index].startswith("else:"						):
																								line_index += 1
																		line_index += 1
																		lowercase__    :				str=	[]
																		# Until we unindent, add backend objects to the list
																		while line_index < len(A						) and len(lines[line_index]						) > 1:
																								lowercase__    :				List[str]=	lines[line_index]
																								lowercase__    :				Any=	_re_single_line_import.search(A						)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(", "						)						)
																								elif line.startswith(" " * 8						):
																														objects.append(line[8:-2]						)
																								line_index += 1

																		if len(A						) > 0:
																								lowercase__    :				str=	objects
												else:
																		line_index += 1

						return backend_specific_objects





def 						lowercase__(A ,		A						)    ->List[Any]:




						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(A						)
						elif name.islower():
												return DUMMY_FUNCTION.format(A ,		A						)
						else:
												return DUMMY_CLASS.format(A ,		A						)





def 						lowercase__(A=None						)    ->Optional[Any]:




						"""simple docstring"""



						if backend_specific_objects is None:
												lowercase__    :				int=	read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						lowercase__    :				Dict=	{}

						for backend, objects in backend_specific_objects.items():
												lowercase__    :				str=	"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"						)						) + "]"
												lowercase__    :				List[str]=	"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(A ,		A						) for o in objects]						)
												lowercase__    :				Optional[Any]=	dummy_file

						return dummy_files





def 						lowercase__(A=False						)    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						lowercase__    :				int=	{"torch": "pt"}

						# Locate actual dummy modules and read their content.
						lowercase__    :				Dict=	os.path.join(A ,		"utils"						)
						lowercase__    :				str=	{
						    backend: os.path.join(A ,		f'''dummy_{short_names.get(A ,		A						)}_objects.py'''						)
						    for backend in dummy_files.keys()
						}

						lowercase__    :				Union[str, Any]=	{}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(A						):
																		with open(A ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
																								lowercase__    :				List[Any]=	f.read()
												else:
																		lowercase__    :				Tuple=	""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f'''Updating diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py as the main '''
																								    "__init__ has new objects."						)
																								with open(dummy_file_paths[backend] ,		"w" ,		encoding="utf-8" ,		newline="\n"						) as f:
																														f.write(dummy_files[backend]						)
																		else:
																								raise ValueError(
																								    "The main __init__ has objects that are not present in "
																								    f'''diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py. Run `make fix-copies` '''
																								    "to fix this."						)


if __name__ == "__main__":
			a			:    Optional[Any]				 =		argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			a			:    Tuple				 =		parser.parse_args()

			check_dummies(args.fix_and_overwrite)














"""simple docstring"""







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 						lowercase__(A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Dict=	checkpoints.load_tax_checkpoint(A						)
						lowercase__    :				List[Any]=	flatten_dict(A						)
						return flax_params





def 						lowercase__(A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	{}

						lowercase__    :				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",
						}

						lowercase__    :				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
																		lowercase__    :				Any=	".".join(key[1:]						)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase__    :				List[Any]=	new_key.replace(A ,		A						)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase__    :				List[str]=	new_key.replace(A ,		A						)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase__    :				List[str]=	re.sub(R"layers_(\d+)" ,		R"layer.\1" ,		A						)
																								lowercase__    :				Any=	new_key.replace("encoder" ,		"encoder.encoder"						)

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

																		lowercase__    :				Dict=	flax_dict[key]

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

						return converted_torch_dict





def 						lowercase__(A ,		A ,		A=False ,		A=False						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	get_flax_param(A						)

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

						lowercase__    :				Optional[Any]=	PixaStructForConditionalGeneration(A						)

						lowercase__    :				Tuple=	rename_and_convert_flax_params(A						)
						model.load_state_dict(A						)

						lowercase__    :				List[str]=	AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
						lowercase__    :				List[str]=	PixaStructImageProcessor()
						lowercase__    :				Dict=	PixaStructProcessor(image_processor=A ,		tokenizer=A						)

						if use_large:
												lowercase__    :				Tuple=	4_096

						lowercase__    :				List[Any]=	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			:    int				 =		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			:    Optional[int]				 =		parser.parse_args()

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






