





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowerCAmelCase_						   =    {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		Dict:
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		_snake_case   :				Optional[int]														=	list(s_dict.keys()				)
		for key in keys:
				_snake_case   :				Union[str, Any]														=	R'.*/layers_(\d+)'
				_snake_case   :				Union[str, Any]														=	key
				if re.match(lowerCAmelCase    ,							lowerCAmelCase				):
						_snake_case   :				Optional[Any]														=	re.sub(R'layers_(\d+)'    ,							R'block/\1/layer'    ,							lowerCAmelCase				)

				_snake_case   :				Optional[Any]														=	R'(encoder|decoder)\/'

				if re.match(lowerCAmelCase    ,							lowerCAmelCase				):
						_snake_case   :				int														=	re.match(lowerCAmelCase    ,							lowerCAmelCase				).groups()
						if groups[0] == "encoder":
								_snake_case   :				Optional[Any]														=	re.sub(R'/mlp/'    ,							R'/1/mlp/'    ,							lowerCAmelCase				)
								_snake_case   :				Optional[Any]														=	re.sub(R'/pre_mlp_layer_norm/'    ,							R'/1/layer_norm/'    ,							lowerCAmelCase				)

						elif groups[0] == "decoder":
								_snake_case   :				Tuple														=	re.sub(R'/mlp/'    ,							R'/2/mlp/'    ,							lowerCAmelCase				)
								_snake_case   :				Union[str, Any]														=	re.sub(R'/pre_mlp_layer_norm/'    ,							R'/2/layer_norm/'    ,							lowerCAmelCase				)

								# 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								_snake_case   :				Optional[int]														=	new_key.replace(lowerCAmelCase    ,							lowerCAmelCase				)

				print(F"""{key} -> {new_key}"""				)
				_snake_case   :				List[str]														=	s_dict.pop(lowerCAmelCase				)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_snake_case   :				Optional[int]														=	s_dict[
				    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
				].T
		if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				_snake_case   :				Any														=	s_dict[
				    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
				].T

		# 3. Take extra care of the EXPERTS layer
		for key in list(s_dict.keys()				):
				if "expert" in key:
						_snake_case   :				str														=	s_dict[key].shape[0]
						_snake_case   :				Any														=	s_dict[key]
						for idx in range(lowerCAmelCase				):
								_snake_case   :				Tuple														=	expert_weihts[idx]
								print(F"""{key} -> {key.replace('expert/'    ,							'nested fstring'				)}"""				)

						s_dict.pop(lowerCAmelCase				)

		return s_dict


lowerCAmelCase_						   =    {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Tuple				)->		int:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(lowerCAmelCase    ,							'r'				) as f:
				_snake_case   :				Any														=	f.read()

		_snake_case   :				Union[str, Any]														=	re.findall(R'(.*) = ([0-9.]*)'    ,							lowerCAmelCase				)
		_snake_case   :				List[str]														=	{}
		for param, value in regex_match:
				if param in GIN_TO_CONFIG_MAPPING and value != "":
						_snake_case   :				Optional[Any]														=	float(lowerCAmelCase				) if '.' in value else int(lowerCAmelCase				)

		_snake_case   :				Any														=	re.findall(R'(.*activations) = \(\'(.*)\',\)'    ,							lowerCAmelCase				)[0]
		_snake_case   :				Any														=	str(activation[1]				)

		_snake_case   :				int														=	num_experts
		_snake_case   :				int														=	SwitchTransformersConfig(**lowerCAmelCase				)
		return config


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Optional[Any]=None    ,							lowerCAmelCase:	Optional[Any]="./"    ,							lowerCAmelCase:	int=8				)->		List[Any]:
		# Initialise PyTorch model

		print(F"""Loading flax weights from : {flax_checkpoint_path}"""				)
		_snake_case   :				int														=	checkpoints.load_tax_checkpoint(lowerCAmelCase				)

		if gin_file is not None:
				_snake_case   :				Optional[int]														=	convert_gin_to_config(lowerCAmelCase    ,							lowerCAmelCase				)
		else:
				_snake_case   :				Optional[Any]														=	SwitchTransformersConfig.from_pretrained(lowerCAmelCase				)

		_snake_case   :				Dict														=	SwitchTransformersForConditionalGeneration(lowerCAmelCase				)

		_snake_case   :				Optional[Any]														=	flax_params['target']
		_snake_case   :				Any														=	flatten_dict(lowerCAmelCase    ,							sep='/'				)
		_snake_case   :				Union[str, Any]														=	rename_keys(lowerCAmelCase				)
		_snake_case   :				Optional[Any]														=	unflatten_dict(lowerCAmelCase    ,							sep='/'				)

		# Load the flax params in the PT model
		load_flax_weights_in_pytorch_model(lowerCAmelCase    ,							lowerCAmelCase				)

		print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
		pt_model.save_pretrained(lowerCAmelCase				)


if __name__ == "__main__":
						lowerCAmelCase_						   =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--switch_t5x_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
						        """ model architecture. If not provided, a `gin_file` has to be provided."""
						    ),
						)
						parser.add_argument(
						    """--gin_file""",
						    default=None,
						    type=str,
						    required=False,
						    help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
						)
						parser.add_argument(
						    """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
						)
						parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
						lowerCAmelCase_						   =    parser.parse_args()
						convert_flax_checkpoint_to_pytorch(
						    args.switch_tax_checkpoint_path,
						    args.config_name,
						    args.gin_file,
						    args.pytorch_dump_folder_path,
						    args.num_experts,
						)











import csv

import tweepy

# Twitter API credentials
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		None:
  # authorize twitter, initialize tweepy
  _snake_case   :				Optional[Any]														=	tweepy.OAuthHandler(lowerCAmelCase    ,							lowerCAmelCase				)
  auth.set_access_token(lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				List[Any]														=	tweepy.API(lowerCAmelCase				)

  # initialize a list to hold all the tweepy Tweets
  _snake_case   :				Any														=	[]

  # make initial request for most recent tweets (200 is the maximum allowed count)
  _snake_case   :				List[str]														=	api.user_timeline(screen_name=lowerCAmelCase    ,							count=2_00				)

  # save most recent tweets
  alltweets.extend(lowerCAmelCase				)

  # save the id of the oldest tweet less one
  _snake_case   :				List[Any]														=	alltweets[-1].id - 1

  # keep grabbing tweets until there are no tweets left to grab
  while len(lowerCAmelCase				) > 0:
    print(F"""getting tweets before {oldest}"""				)

    # all subsequent requests use the max_id param to prevent duplicates
    _snake_case   :				Tuple														=	api.user_timeline(
        screen_name=lowerCAmelCase    ,							count=2_00    ,							max_id=lowerCAmelCase				)

    # save most recent tweets
    alltweets.extend(lowerCAmelCase				)

    # update the id of the oldest tweet less one
    _snake_case   :				List[str]														=	alltweets[-1].id - 1

    print(F"""...{len(lowerCAmelCase				)} tweets downloaded so far"""				)

  # transform the tweepy tweets into a 2D array that will populate the csv
  _snake_case   :				int														=	[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

  # write the csv
  with open(F"""new_{screen_name}_tweets.csv"""    ,							'w'				) as f:
    _snake_case   :				Any														=	csv.writer(lowerCAmelCase				)
    writer.writerow(['id', 'created_at', 'text']				)
    writer.writerows(lowerCAmelCase				)


if __name__ == "__main__":
      # pass in the username of the account you want to download
      get_all_tweets("""FirePing32""")




