






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 							SCREAMING_SNAKE_CASE__		(       __A				,     __A				,     __A		):





     """simple docstring"""






     a_			=	[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]



     @register_to_config
     def __init__(      self  :						Tuple							,	__A  :						Tuple							,	__A  :						str							,	__A  :						Union[str, Any] = None							,	__A  :						Union[str, Any] = 5_0_2_5_7							,	__A  :						int = 1_0_2_4							,	__A  :						Optional[int] = 7_6_8							,	__A  :						str = 1_2							,	__A  :						List[Any] = 1_2							,	__A  :						List[Any] = None							,	__A  :						Union[str, Any] = "gelu_new"							,	__A  :						str = 0.1							,	__A  :						List[str] = 0.1							,	__A  :						Any = 0.1							,	__A  :						Any = 1e-5							,	__A  :						int = 0.0_2							,	__A  :						str = True							,	__A  :						int = True							,	__A  :						Optional[int] = False							,	__A  :						Any = False							,	):
      super().__init__()

      snake_case__				:   Tuple     =						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.''' )

      snake_case__				:   Optional[Any]     =						prefix_inner_dim
      snake_case__				:   Any     =						prefix_hidden_dim

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

      snake_case__				:   str     =						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							,	)
      snake_case__				:   List[Any]     =						GPTaLMHeadModel(__A )



     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Union[str, Any]							,	__A  :						int							,	__A  :						int = None							,	__A  :						str = None							,	):
      snake_case__				:   Tuple     =						self.transformer.transformer.wte(__A )
      snake_case__				:   Optional[int]     =						self.encode_prefix(__A )
      snake_case__				:   str     =						self.decode_prefix(__A )
      snake_case__				:   Tuple     =						torch.cat((prefix_embeds, embedding_text)							,	dim=1 )

      if labels is not None:
       snake_case__				:   Tuple     =						self.get_dummy_token(input_ids.shape[0]							,	input_ids.device )
       snake_case__				:   Optional[Any]     =						torch.cat((dummy_token, input_ids)							,	dim=1 )
      snake_case__				:   List[str]     =						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  _lowercase  (      self  :						List[Any]							,	__A  :						Optional[Any]							,	__A  :						str ):
      return torch.zeros(__A							,	self.prefix_length							,	dtype=torch.intaa							,	device=__A )



     def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[str] ):
      return self.encode_prefix(__A )



     @torch.no_grad()
     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[str]							,	__A  :						Dict							,	__A  :						List[Any] ):
      snake_case__				:   Any     =						torch.split(__A							,	1							,	dim=0 )
      snake_case__				:   Union[str, Any]     =						[]
      snake_case__				:   Optional[Any]     =						[]
      for feature in features:
       snake_case__				:   Optional[int]     =						self.decode_prefix(feature.to(__A ) )  # back to the clip feature
       # Only support beam search for now
       snake_case__				:   Dict     =						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] )
      snake_case__				:   Any     =						torch.stack(__A )
      snake_case__				:   int     =						torch.stack(__A )
      return generated_tokens, generated_seq_lengths






     @torch.no_grad()
     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[str]=None							,	__A  :						Dict=None							,	__A  :						Optional[Any]=None							,	__A  :						Union[str, Any] = 5							,	__A  :						List[str] = 6_7							,	__A  :						str = 1.0							,	__A  :						Optional[int] = None							,	):
      snake_case__				:   Optional[Any]     =						eos_token_id
      snake_case__				:   Optional[Any]     =						None
      snake_case__				:   Dict     =						None
      snake_case__				:   Union[str, Any]     =						torch.ones(__A							,	device=__A							,	dtype=torch.int )
      snake_case__				:   int     =						torch.zeros(__A							,	device=__A							,	dtype=torch.bool )

      if input_embeds is not None:
       snake_case__				:   Optional[Any]     =						input_embeds
      else:
       snake_case__				:   Dict     =						self.transformer.transformer.wte(__A )

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

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

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

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











# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase      :    Dict   		=   abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	from diffusers.utils.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any     ):
	from diffusers.utils.testing_utils import pytest_terminal_summary_main

	snake_case__				:   Optional[int]     =						terminalreporter.config.getoption("--make-reports"     )
	if make_reports:
		pytest_terminal_summary_main(snake_case_  ,						id=snake_case_     )



