





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser(
						    description=(
						        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
						        """ Distillation"""
						    )
						)
						parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
						parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
						parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
						parser.add_argument("""--vocab_transform""", action="""store_true""")
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()

						if args.model_type == "roberta":
												_SCREAMING_SNAKE_CASE      	=		RobertaForMaskedLM.from_pretrained(args.model_name)
												_SCREAMING_SNAKE_CASE      	=		"""roberta"""
						elif args.model_type == "gpt2":
												_SCREAMING_SNAKE_CASE      	=		GPTaLMHeadModel.from_pretrained(args.model_name)
												_SCREAMING_SNAKE_CASE      	=		"""transformer"""

						_SCREAMING_SNAKE_CASE      	=		model.state_dict()
						_SCREAMING_SNAKE_CASE      	=		{}

						# Embeddings #
						if args.model_type == "gpt2":
												for param_name in ["wte.weight", "wpe.weight"]:
																		_SCREAMING_SNAKE_CASE      	=		state_dict[F'''{prefix}.{param_name}''']
						else:
												for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																		_SCREAMING_SNAKE_CASE      	=		F'''{prefix}.embeddings.{w}.weight'''
																		_SCREAMING_SNAKE_CASE      	=		state_dict[param_name]
												for w in ["weight", "bias"]:
																		_SCREAMING_SNAKE_CASE      	=		F'''{prefix}.embeddings.LayerNorm.{w}'''
																		_SCREAMING_SNAKE_CASE      	=		state_dict[param_name]

    # Transformer Blocks #
						_SCREAMING_SNAKE_CASE      	=		0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												if args.model_type == "gpt2":
																		for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																								for w in ["weight", "bias"]:
																														_SCREAMING_SNAKE_CASE      	=		state_dict[
																														    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																														]
																		_SCREAMING_SNAKE_CASE      	=		state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
												else:
																		for layer in [
																		    "attention.self.query",
																		    "attention.self.key",
																		    "attention.self.value",
																		    "attention.output.dense",
																		    "attention.output.LayerNorm",
																		    "intermediate.dense",
																		    "output.dense",
																		    "output.LayerNorm",
																		]:
																								for w in ["weight", "bias"]:
																														_SCREAMING_SNAKE_CASE      	=		state_dict[
																														    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																														]
												std_idx += 1

						# Language Modeling Head ###s
						if args.model_type == "roberta":
												for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																		_SCREAMING_SNAKE_CASE      	=		state_dict[F'''{layer}''']
												if args.vocab_transform:
																		for w in ["weight", "bias"]:
																								_SCREAMING_SNAKE_CASE      	=		state_dict[F'''lm_head.dense.{w}''']
																								_SCREAMING_SNAKE_CASE      	=		state_dict[F'''lm_head.layer_norm.{w}''']
						elif args.model_type == "gpt2":
												for w in ["weight", "bias"]:
																		_SCREAMING_SNAKE_CASE      	=		state_dict[F'''{prefix}.ln_f.{w}''']
												_SCREAMING_SNAKE_CASE      	=		state_dict["""lm_head.weight"""]

						print(F'''N layers selected for distillation: {std_idx}''')
						print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

						print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
						torch.save(compressed_sd, args.dump_checkpoint)








from __future__ import annotations


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a = None   , __a = None   , __a = False   , ):
	snake_case_		:			int											=	cipher_alphabet or [chr(__a   ) for i in range(97   , 1_23   )]

	# If the argument is None or the user provided an empty dictionary
	if not frequencies_dict:
		# Frequencies of letters in the english language (how much they show up)
		snake_case_		:			Dict											=	{
		    'a': 0.08497,
		    'b': 0.01492,
		    'c': 0.02202,
		    'd': 0.04253,
		    'e': 0.11162,
		    'f': 0.02228,
		    'g': 0.02015,
		    'h': 0.06094,
		    'i': 0.07546,
		    'j': 0.00153,
		    'k': 0.01292,
		    'l': 0.04025,
		    'm': 0.02406,
		    'n': 0.06749,
		    'o': 0.07507,
		    'p': 0.01929,
		    'q': 0.00095,
		    'r': 0.07587,
		    's': 0.06327,
		    't': 0.09356,
		    'u': 0.02758,
		    'v': 0.00978,
		    'w': 0.02560,
		    'x': 0.00150,
		    'y': 0.01994,
		    'z': 0.00077,
		}
	else:
		# Custom frequencies dictionary
		snake_case_		:			int											=	frequencies_dict

	if not case_sensitive:
		snake_case_		:			Optional[Any]											=	ciphertext.lower()

	# Chi squared statistic values
	snake_case_		:			dict[int, tuple[float, str]]											=	{}

	# cycle through all of the shifts
	for shift in range(len(__a   )   ):
		snake_case_		:			int											=	''

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				snake_case_		:			List[Any]											=	(alphabet_letters.index(letter.lower()   ) - shift) % len(
				    __a   )
				decrypted_with_shift += (
				    alphabet_letters[new_key].upper()
				    if case_sensitive and letter.isupper()
				    else alphabet_letters[new_key]
				)
			except ValueError:
				# Append the character if it isn't in the alphabet
				decrypted_with_shift += letter

		snake_case_		:			Union[str, Any]											=	0.0

		# Loop through each letter in the decoded message with the shift
		for letter in decrypted_with_shift:
			if case_sensitive:
				snake_case_		:			List[Any]											=	letter.lower()
				if letter in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Optional[Any]											=	decrypted_with_shift.lower().count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			Tuple											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[int]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value
			else:
				if letter.lower() in frequencies:
					# Get the amount of times the letter occurs in the message
					snake_case_		:			Any											=	decrypted_with_shift.count(__a   )

					# Get the excepcted amount of times the letter should appear based
					# on letter frequencies
					snake_case_		:			List[str]											=	frequencies[letter] * occurrences

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[Any]											=	((occurrences - expected) ** 2) / expected

					# Add the margin of error to the total chi squared statistic
					chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
		snake_case_		:			Any											=	(
		    chi_squared_statistic,
		    decrypted_with_shift,
		)

	# Get the most likely cipher by finding the cipher with the smallest chi squared
	# statistic
	def chi_squared_statistic_values_sorting_key(__a   ) -> tuple[float, str]:
		return chi_squared_statistic_values[key]

	snake_case_		:			int											=	min(
	    __a   , key=__a   , )

	# Get all the data from the most likely cipher (key, decoded message)
	(
	    (
	    snake_case_
	)						,(
	    snake_case_
	)						,
	)		:			str											=	chi_squared_statistic_values[most_likely_cipher]

	# Return the data on the most likely shift
	return (
	    most_likely_cipher,
	    most_likely_cipher_chi_squared_value,
	    decoded_most_likely_cipher,
	)

