



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__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
				"""simple docstring"""







				__a       =		checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE    )
				__a       =		flatten_dict(_SCREAMING_SNAKE_CASE    )
				return flax_params

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				__a       =		{}

				__a       =		{
				    """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""",
				}

				__a       =		{
				    """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
												__a       =		""".""".join(key[1:]    )

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

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

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

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

												__a       =		flax_dict[key]

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

				return converted_torch_dict

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]=False				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				__a       =		get_flax_param(_SCREAMING_SNAKE_CASE    )

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

				__a       =		PixaStructForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

				__a       =		rename_and_convert_flax_params(_SCREAMING_SNAKE_CASE    )
				model.load_state_dict(_SCREAMING_SNAKE_CASE    )

				__a       =		AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
				__a       =		PixaStructImageProcessor()
				__a       =		PixaStructProcessor(image_processor=_SCREAMING_SNAKE_CASE				,     tokenizer=_SCREAMING_SNAKE_CASE    )

				if use_large:
								__a       =		4096

				__a       =		True

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

				model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

				print("""Model saved in {}""".format(_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
						lowerCamelCase__			= 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.""")
						lowerCamelCase__			= parser.parse_args()

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





from __future__ import annotations

lowerCamelCase__			= """Muhammad Umer Farooq"""
lowerCamelCase__			= """MIT"""
lowerCamelCase__			= """1.0.0"""
lowerCamelCase__			= """Muhammad Umer Farooq"""
lowerCamelCase__			= """contact@muhammadumerfarooq.me"""
lowerCamelCase__			= """Alpha"""

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		Any      ,				__lowercase		:		str	):

											'''simple docstring'''
											super().__init__()
											__a       =		[]
											__a       =		domain







							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		str      ,				__lowercase		:		list[tuple[str, str | None]]	):

											'''simple docstring'''
											# Only parse the 'anchor' tag.
											if tag == "a":
															# Check the list of defined attributes.
															for name, value in attrs:
																			# If href is defined, and not empty nor # print it.
																			if name == "href" and value != "#" and value != "":
																							# If not already in urls.
																							if value not in self.urls:
																											__a       =		parse.urljoin(self.domain      ,				__lowercase	)
																											self.urls.append(__lowercase	)



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				return ".".join(get_sub_domain_name(_SCREAMING_SNAKE_CASE    ).split("""."""    )[-2:]    )

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				return parse.urlparse(_SCREAMING_SNAKE_CASE    ).netloc

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str = "https://github.com"    ):
				"""simple docstring"""







				__a       =		get_domain_name(_SCREAMING_SNAKE_CASE    )

				# Initialize the parser
				__a       =		Parser(_SCREAMING_SNAKE_CASE    )

				try:
								# Open URL
								__a       =		requests.get(_SCREAMING_SNAKE_CASE    )

								# pass the raw HTML to the parser to get links
								parser.feed(r.text    )

								# Get links and loop through
								__a       =		set()
								for link in parser.urls:
												# open URL.
												# read = requests.get(link)
												try:
																__a       =		requests.get(_SCREAMING_SNAKE_CASE    )
																# Get the valid email.
																__a       =		re.findall("""[a-zA-Z0-9]+@""" + domain				,     read.text    )
																# If not in list then append it.
																for email in emails:
																				valid_emails.add(_SCREAMING_SNAKE_CASE    )
												except ValueError:
																pass
				except ValueError:
								raise SystemExit(1    )

				# Finally return a sorted list of email addresses with no duplicates.
				return sorted(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCamelCase__			= emails_from_url("""https://github.com""")
						print(F"""{len(emails)} emails found:""")
						print("""\n""".join(sorted(emails)))
