


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase		, UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






   @register_to_config
   def __init__(		self						,				__A						,				__A						,				__A = None						,				__A = 5_0257						,				__A = 1024						,				__A = 768						,				__A = 12						,				__A = 12						,				__A = None						,				__A = "gelu_new"						,				__A = 0.1						,				__A = 0.1						,				__A = 0.1						,				__A = 1e-5						,				__A = 0.02						,				__A = True						,				__A = True						,				__A = False						,				__A = False						,				):






     """simple docstring"""





     super().__init__()

     lowerCamelCase :					Optional[Any]    =				prefix_length

     if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
       raise ValueError(
           F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
           F""" `n_embd`: {n_embd} are not equal.""" )

     lowerCamelCase :					List[str]    =				prefix_inner_dim
     lowerCamelCase :					Dict    =				prefix_hidden_dim

     lowerCamelCase :					List[str]    =				(
         nn.Linear(self.prefix_inner_dim						,				self.prefix_hidden_dim )
         if self.prefix_hidden_dim is not None
         else nn.Identity()
     )
     lowerCamelCase :					int    =				(
         nn.Linear(self.prefix_hidden_dim						,				__A ) if self.prefix_hidden_dim is not None else nn.Identity()
     )

     lowerCamelCase :					Union[str, Any]    =				GPTaConfig(
         vocab_size=__A						,				n_positions=__A						,				n_embd=__A						,				n_layer=__A						,				n_head=__A						,				n_inner=__A						,				activation_function=__A						,				resid_pdrop=__A						,				embd_pdrop=__A						,				attn_pdrop=__A						,				layer_norm_epsilon=__A						,				initializer_range=__A						,				scale_attn_weights=__A						,				use_cache=__A						,				scale_attn_by_inverse_layer_idx=__A						,				reorder_and_upcast_attn=__A						,				)
     lowerCamelCase :					int    =				GPTaLMHeadModel(__A )






   def _snake_case    (		self						,				__A						,				__A						,				__A = None						,				__A = None						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.transformer.transformer.wte(__A )
     lowerCamelCase :					List[Any]    =				self.encode_prefix(__A )
     lowerCamelCase :					str    =				self.decode_prefix(__A )
     lowerCamelCase :					List[str]    =				torch.cat((prefix_embeds, embedding_text)						,				dim=1 )

     if labels is not None:
       lowerCamelCase :					Union[str, Any]    =				self.get_dummy_token(input_ids.shape[0]						,				input_ids.device )
       lowerCamelCase :					Dict    =				torch.cat((dummy_token, input_ids)						,				dim=1 )
     lowerCamelCase :					Any    =				self.transformer(inputs_embeds=__A						,				labels=__A						,				attention_mask=__A )
     if self.prefix_hidden_dim is not None:
       return out, hidden
     else:
       return out






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     return torch.zeros(__A						,				self.prefix_length						,				dtype=torch.intaa						,				device=__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.encode_prefix(__A )






   @torch.no_grad()
   def _snake_case    (		self						,				__A						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				torch.split(__A						,				1						,				dim=0 )
     lowerCamelCase :					List[str]    =				[]
     lowerCamelCase :					List[str]    =				[]
     for feature in features:
       lowerCamelCase :					Any    =				self.decode_prefix(feature.to(__A ) )  # back to the clip feature
       # Only support beam search for now
       lowerCamelCase    ,	lowerCamelCase :					Optional[int]    =				self.generate_beam(
           input_embeds=__A						,				device=__A						,				eos_token_id=__A )
       generated_tokens.append(output_tokens[0] )
       generated_seq_lengths.append(seq_lengths[0] )
     lowerCamelCase :					Optional[Any]    =				torch.stack(__A )
     lowerCamelCase :					int    =				torch.stack(__A )
     return generated_tokens, generated_seq_lengths






   @torch.no_grad()
   def _snake_case    (		self						,				__A=None						,				__A=None						,				__A=None						,				__A = 5						,				__A = 67						,				__A = 1.0						,				__A = None						,				):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				eos_token_id
     lowerCamelCase :					Optional[int]    =				None
     lowerCamelCase :					Optional[int]    =				None
     lowerCamelCase :					List[Any]    =				torch.ones(__A						,				device=__A						,				dtype=torch.int )
     lowerCamelCase :					int    =				torch.zeros(__A						,				device=__A						,				dtype=torch.bool )

     if input_embeds is not None:
       lowerCamelCase :					Tuple    =				input_embeds
     else:
       lowerCamelCase :					str    =				self.transformer.transformer.wte(__A )

     for i in range(__A ):
       lowerCamelCase :					Union[str, Any]    =				self.transformer(inputs_embeds=__A )
       lowerCamelCase :					int    =				outputs.logits
       lowerCamelCase :					Optional[int]    =				logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
       lowerCamelCase :					List[str]    =				logits.softmax(-1 ).log()

       if scores is None:
         lowerCamelCase    ,	lowerCamelCase :					Any    =				logits.topk(__A						,				-1 )
         lowerCamelCase :					Tuple    =				generated.expand(__A						,				*generated.shape[1:] )
         lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				next_tokens.permute(1						,				0 ), scores.squeeze(0 )
         if tokens is None:
           lowerCamelCase :					Union[str, Any]    =				next_tokens
         else:
           lowerCamelCase :					Tuple    =				tokens.expand(__A						,				*tokens.shape[1:] )
           lowerCamelCase :					Dict    =				torch.cat((tokens, next_tokens)						,				dim=1 )
       else:
         lowerCamelCase :					Dict    =				-float(np.inf )
         lowerCamelCase :					Optional[Any]    =				0
         lowerCamelCase :					Dict    =				scores[:, None] + logits
         seq_lengths[~is_stopped] += 1
         lowerCamelCase :					Optional[Any]    =				scores_sum / seq_lengths[:, None]
         lowerCamelCase    ,	lowerCamelCase :					Any    =				scores_sum_average.view(-1 ).topk(__A						,				-1 )
         lowerCamelCase :					Optional[int]    =				next_tokens // scores_sum.shape[1]
         lowerCamelCase :					List[str]    =				seq_lengths[next_tokens_source]
         lowerCamelCase :					Optional[Any]    =				next_tokens % scores_sum.shape[1]
         lowerCamelCase :					Union[str, Any]    =				next_tokens.unsqueeze(1 )
         lowerCamelCase :					Union[str, Any]    =				tokens[next_tokens_source]
         lowerCamelCase :					Dict    =				torch.cat((tokens, next_tokens)						,				dim=1 )
         lowerCamelCase :					Union[str, Any]    =				generated[next_tokens_source]
         lowerCamelCase :					Any    =				scores_sum_average * seq_lengths
         lowerCamelCase :					Tuple    =				is_stopped[next_tokens_source]

       lowerCamelCase :					Tuple    =				self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0]						,				1						,				-1 )
       lowerCamelCase :					Any    =				torch.cat((generated, next_token_embed)						,				dim=1 )
       lowerCamelCase :					Any    =				is_stopped + next_tokens.eq(__A ).squeeze()
       if is_stopped.all():
         break

     lowerCamelCase :					str    =				scores / seq_lengths
     lowerCamelCase :					Optional[Any]    =				scores.argsort(descending=__A )
     # tokens tensors are already padded to max_seq_length
     lowerCamelCase :					List[Any]    =				[tokens[i] for i in order]
     lowerCamelCase :					Union[str, Any]    =				torch.stack(__A						,				dim=0 )
     lowerCamelCase :					Tuple    =				torch.tensor([seq_lengths[i] for i in order]						,				dtype=seq_lengths.dtype )
     return output_texts, seq_lengths







from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



