




'''simple docstring'''


import string

import numpy
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return b if a == 0 else greatest_common_divisor(b % a				,		UpperCamelCase   )

class     A					:
							lowercase_           =       string.ascii_uppercase + string.digits
							# This cipher takes alphanumerics into account
							# i.e. a total of 36 characters

							# take x and return x % len(key_string)
							lowercase_           =       numpy.vectorize(lambda _a					: x % 36					)

							lowercase_           =       numpy.vectorize(_a					)







							def __init__(						self :						Union[str, Any]							,    lowerCAmelCase_ :						numpy.ndarray      )    ->							None:


														"""simple docstring"""


														_a			  =       self.modulus(lowerCAmelCase_      )  # mod36 calc's on the encrypt key
														self.check_determinant()  # validate the determinant of the encryption key
														_a			  =       encrypt_key.shape[0]







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						str      )    ->							int:


														"""simple docstring"""


														return self.key_string.index(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						int      )    ->							str:


														"""simple docstring"""


														return self.key_string[round(lowerCAmelCase_      )]







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							None:


														"""simple docstring"""


														_a			  =       round(numpy.linalg.det(self.encrypt_key      )      )

														if det < 0:
																					_a			  =       det % len(self.key_string      )

														_a			  =       len(self.key_string      )
														if greatest_common_divisor(lowerCAmelCase_							,    len(self.key_string      )      ) != 1:
																					_a			  =       (
																					    F'determinant modular {req_l} of encryption key({det}) '
																					    F'is not co prime w.r.t {req_l}.\nTry another key.'
																					)
																					raise ValueError(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						str      )    ->							str:


														"""simple docstring"""


														_a			  =       [char for char in text.upper() if char in self.key_string]

														_a			  =       chars[-1]
														while len(lowerCAmelCase_      ) % self.break_key != 0:
																					chars.append(lowerCAmelCase_      )

														return "".join(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						str      )    ->							str:


														"""simple docstring"""


														_a			  =       self.process_text(text.upper()      )
														_a			  =       ''''''

														for i in range(0							,    len(lowerCAmelCase_      ) - self.break_key + 1							,    self.break_key      ):
																					_a			  =       text[i : i + self.break_key]
																					_a			  =       [self.replace_letters(lowerCAmelCase_      ) for char in batch]
																					_a			  =       numpy.array([vec]      ).T
																					_a			  =       self.modulus(self.encrypt_key.dot(lowerCAmelCase_      )      ).T.tolist()[
																					    0
																					]
																					_a			  =       ''''''.join(
																					    self.replace_digits(lowerCAmelCase_      ) for num in batch_encrypted      )
																					encrypted += encrypted_batch

														return encrypted







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							numpy.ndarray:


														"""simple docstring"""


														_a			  =       round(numpy.linalg.det(self.encrypt_key      )      )

														if det < 0:
																					_a			  =       det % len(self.key_string      )
														_a			  =       None
														for i in range(len(self.key_string      )      ):
																					if (det * i) % len(self.key_string      ) == 1:
																												_a			  =       i
																												break

														_a			  =       (
														    det_inv
														    * numpy.linalg.det(self.encrypt_key      )
														    * numpy.linalg.inv(self.encrypt_key      )
														)

														return self.to_int(self.modulus(lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						str      )    ->							str:


														"""simple docstring"""


														_a			  =       self.make_decrypt_key()
														_a			  =       self.process_text(text.upper()      )
														_a			  =       ''''''

														for i in range(0							,    len(lowerCAmelCase_      ) - self.break_key + 1							,    self.break_key      ):
																					_a			  =       text[i : i + self.break_key]
																					_a			  =       [self.replace_letters(lowerCAmelCase_      ) for char in batch]
																					_a			  =       numpy.array([vec]      ).T
																					_a			  =       self.modulus(decrypt_key.dot(lowerCAmelCase_      )      ).T.tolist()[0]
																					_a			  =       ''''''.join(
																					    self.replace_digits(lowerCAmelCase_      ) for num in batch_decrypted      )
																					decrypted += decrypted_batch

														return decrypted



def 	snake_case_					():





							'''simple docstring'''


							_a			  =       int(input('''Enter the order of the encryption key: '''   )   )
							_a			  =       []

							print('''Enter each row of the encryption key with space separated integers'''   )
							for _ in range(UpperCamelCase   ):
														_a			  =       [int(UpperCamelCase   ) for x in input().split()]
														hill_matrix.append(UpperCamelCase   )

							_a			  =       HillCipher(numpy.array(UpperCamelCase   )   )

							print('''Would you like to encrypt or decrypt some text? (1 or 2)'''   )
							_a			  =       input('''\n1. Encrypt\n2. Decrypt\n'''   )
							if option == "1":
														_a			  =       input('''What text would you like to encrypt?: '''   )
														print('''Your encrypted text is:'''   )
														print(hc.encrypt(UpperCamelCase   )   )
							elif option == "2":
														_a			  =       input('''What text would you like to decrypt?: '''   )
														print('''Your decrypted text is:'''   )
														print(hc.decrypt(UpperCamelCase   )   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				main()







'''simple docstring'''


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
				from transformers import LlamaTokenizerFast
except ImportError as e:
				warnings.warn(e)
				warnings.warn(
				    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
				)
				_snake_case   :   int     							=	None
_snake_case   :   Tuple     							=	{
    '7B': 11008,
    '13B': 13824,
    '30B': 17920,
    '65B': 22016,
    '70B': 28672,
}
_snake_case   :   int     							=	{
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[Any]=1				,		UpperCamelCase :						Optional[Any]=256   ):





							'''simple docstring'''


							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''r'''   ) as f:
														return json.load(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''w'''   ) as f:
														json.dump(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						str=True   ):





							'''simple docstring'''


							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )
							_a			  =       os.path.join(UpperCamelCase				,		'''tmp'''   )
							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )

							_a			  =       read_json(os.path.join(UpperCamelCase				,		'''params.json'''   )   )
							_a			  =       NUM_SHARDS[model_size]
							_a			  =       params['''n_layers''']
							_a			  =       params['''n_heads''']
							_a			  =       n_heads // num_shards
							_a			  =       params['''dim''']
							_a			  =       dim // n_heads
							_a			  =       10000.0
							_a			  =       1.0 / (base ** (torch.arange(0				,		UpperCamelCase				,		2   ).float() / dims_per_head))

							if "n_kv_heads" in params:
														_a			  =       params['''n_kv_heads''']  # for GQA / MQA
														_a			  =       n_heads_per_shard // num_key_value_heads
														_a			  =       dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														_a			  =       n_heads
														_a			  =       n_heads_per_shard
														_a			  =       dim

							# permute for sliced rotary
							def permute(UpperCamelCase :						List[str]				,		UpperCamelCase :						str=n_heads				,		UpperCamelCase :						int=dim				,		UpperCamelCase :						Optional[Any]=dim   ):
														return w.view(UpperCamelCase				,		dima // n_heads // 2				,		2				,		UpperCamelCase   ).transpose(1				,		2   ).reshape(UpperCamelCase				,		UpperCamelCase   )

							print(f'Fetching all parameters from the checkpoint at {input_base_path}.'   )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														_a			  =       torch.load(os.path.join(UpperCamelCase				,		'''consolidated.00.pth'''   )				,		map_location='''cpu'''   )
							else:
														# Sharded
														_a			  =       [
														    torch.load(os.path.join(UpperCamelCase				,		f'consolidated.{i:02d}.pth'   )				,		map_location='''cpu'''   )
														    for i in range(UpperCamelCase   )
														]
							_a			  =       0
							_a			  =       {'''weight_map''': {}}
							for layer_i in range(UpperCamelCase   ):
														_a			  =       f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
														if model_size == "7B":
																					# Unsharded
																					_a			  =       {
																					    f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wq.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wk.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
																					    f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
																					    f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
																					    f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
																					    f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					_a			  =       {
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.attention_norm.weight'
																					    ].clone(),
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.ffn_norm.weight'
																					    ].clone(),
																					}
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )   )
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
																					                UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		)
																					_a			  =       torch.cat(
																					    [
																					        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
																					            UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					        for i in range(UpperCamelCase   )
																					    ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )

																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )

														_a			  =       inv_freq
														for k, v in state_dict.items():
																					_a			  =       filename
																					param_count += v.numel()
														torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							_a			  =       f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
							if model_size == "7B":
														# Unsharded
														_a			  =       {
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														_a			  =       {
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(UpperCamelCase   )]				,		dim=1   ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(UpperCamelCase   )]				,		dim=0   ),
														}

							for k, v in state_dict.items():
														_a			  =       filename
														param_count += v.numel()
							torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							# Write configs
							_a			  =       {'''total_size''': param_count * 2}
							write_json(UpperCamelCase				,		os.path.join(UpperCamelCase				,		'''pytorch_model.bin.index.json'''   )   )
							_a			  =       params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							_a			  =       params['''multiple_of'''] if '''multiple_of''' in params else 256
							_a			  =       LlamaConfig(
							    hidden_size=UpperCamelCase				,		intermediate_size=compute_intermediate_size(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )				,		num_attention_heads=params['''n_heads''']				,		num_hidden_layers=params['''n_layers''']				,		rms_norm_eps=params['''norm_eps''']				,		num_key_value_heads=UpperCamelCase				,		)
							config.save_pretrained(UpperCamelCase   )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''   )
							_a			  =       LlamaForCausalLM.from_pretrained(UpperCamelCase				,		torch_dtype=torch.floataa				,		low_cpu_mem_usage=UpperCamelCase   )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''   )
							model.save_pretrained(UpperCamelCase				,		safe_serialization=UpperCamelCase   )
							shutil.rmtree(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							_a			  =       LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'   )
							_a			  =       tokenizer_class(UpperCamelCase   )
							tokenizer.save_pretrained(UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''				,		help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''				,		)
							parser.add_argument(
							    '''--model_size'''				,		choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']				,		)
							parser.add_argument(
							    '''--output_dir'''				,		help='''Location to write HF model and tokenizer'''				,		)
							parser.add_argument('''--safe_serialization'''				,		type=UpperCamelCase				,		help='''Whether or not to save using `safetensors`.'''   )
							_a			  =       parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir				,		input_base_path=os.path.join(args.input_dir				,		args.model_size   )				,		model_size=args.model_size				,		safe_serialization=args.safe_serialization				,		)
							_a			  =       os.path.join(args.input_dir				,		'''tokenizer.model'''   )
							write_tokenizer(args.output_dir				,		UpperCamelCase   )


if __name__ == "__main__":
				main()

