




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'''






    UpperCAmelCase__													=   [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']


    @register_to_config
    def __init__(    self   :			List[str]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			int = 50_257      ,				UpperCAmelCase__   :			int = 1_024      ,				UpperCAmelCase__   :			int = 768      ,				UpperCAmelCase__   :			int = 12      ,				UpperCAmelCase__   :			int = 12      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			str = "gelu_new"      ,				UpperCAmelCase__   :			float = 0.1      ,				UpperCAmelCase__   :			float = 0.1      ,				UpperCAmelCase__   :			float = 0.1      ,				UpperCAmelCase__   :			float = 1e-5      ,				UpperCAmelCase__   :			float = 0.02      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			bool = False      ,				UpperCAmelCase__   :			bool = False      ,				)		->Optional[Any]:





           '''simple docstring'''

           super().__init__()

           A__   				=					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.""")

           A__   				=					prefix_inner_dim
           A__   				=					prefix_hidden_dim

           A__   				=					(
               nn.Linear(self.prefix_inner_dim      ,				self.prefix_hidden_dim)
               if self.prefix_hidden_dim is not None
               else nn.Identity()
           )
           A__   				=					(
               nn.Linear(self.prefix_hidden_dim      ,				UpperCAmelCase__) if self.prefix_hidden_dim is not None else nn.Identity()
           )

           A__   				=					GPTaConfig(
               vocab_size=UpperCAmelCase__      ,				n_positions=UpperCAmelCase__      ,				n_embd=UpperCAmelCase__      ,				n_layer=UpperCAmelCase__      ,				n_head=UpperCAmelCase__      ,				n_inner=UpperCAmelCase__      ,				activation_function=UpperCAmelCase__      ,				resid_pdrop=UpperCAmelCase__      ,				embd_pdrop=UpperCAmelCase__      ,				attn_pdrop=UpperCAmelCase__      ,				layer_norm_epsilon=UpperCAmelCase__      ,				initializer_range=UpperCAmelCase__      ,				scale_attn_weights=UpperCAmelCase__      ,				use_cache=UpperCAmelCase__      ,				scale_attn_by_inverse_layer_idx=UpperCAmelCase__      ,				reorder_and_upcast_attn=UpperCAmelCase__      ,				)
           A__   				=					GPTaLMHeadModel(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			torch.Tensor      ,				UpperCAmelCase__   :			torch.Tensor      ,				UpperCAmelCase__   :			Optional[torch.Tensor] = None      ,				UpperCAmelCase__   :			Optional[torch.Tensor] = None      ,				)		->Dict:





           '''simple docstring'''

           A__   				=					self.transformer.transformer.wte(UpperCAmelCase__)
           A__   				=					self.encode_prefix(UpperCAmelCase__)
           A__   				=					self.decode_prefix(UpperCAmelCase__)
           A__   				=					torch.cat((prefix_embeds, embedding_text)      ,				dim=1)

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


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			torch.device)		->torch.Tensor:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			int)		->int:





           '''simple docstring'''

           return self.encode_prefix(UpperCAmelCase__)


    @torch.no_grad()
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Dict)		->List[str]:





           '''simple docstring'''

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





    @torch.no_grad()
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[Any]=None      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			int = 5      ,				UpperCAmelCase__   :			int = 67      ,				UpperCAmelCase__   :			float = 1.0      ,				UpperCAmelCase__   :			Optional[int] = None      ,				)		->Any:





           '''simple docstring'''

           A__   				=					eos_token_id
           A__   				=					None
           A__   				=					None
           A__   				=					torch.ones(UpperCAmelCase__      ,				device=UpperCAmelCase__      ,				dtype=torch.int)
           A__   				=					torch.zeros(UpperCAmelCase__      ,				device=UpperCAmelCase__      ,				dtype=torch.bool)

           if input_embeds is not None:
                  A__   				=					input_embeds
           else:
                  A__   				=					self.transformer.transformer.wte(UpperCAmelCase__)

           for i in range(UpperCAmelCase__):
                  A__   				=					self.transformer(inputs_embeds=UpperCAmelCase__)
                  A__   				=					outputs.logits
                  A__   				=					logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
                  A__   				=					logits.softmax(-1).log()

                  if scores is None:
                         A__				,							A__   				=					logits.topk(UpperCAmelCase__      ,				-1)
                         A__   				=					generated.expand(UpperCAmelCase__      ,				*generated.shape[1:])
                         A__				,							A__   				=					next_tokens.permute(1      ,				0), scores.squeeze(0)
                         if tokens is None:
                                A__   				=					next_tokens
                         else:
                                A__   				=					tokens.expand(UpperCAmelCase__      ,				*tokens.shape[1:])
                                A__   				=					torch.cat((tokens, next_tokens)      ,				dim=1)
                  else:
                         A__   				=					-float(np.inf)
                         A__   				=					0
                         A__   				=					scores[:, None] + logits
                         seq_lengths[~is_stopped] += 1
                         A__   				=					scores_sum / seq_lengths[:, None]
                         A__				,							A__   				=					scores_sum_average.view(-1).topk(UpperCAmelCase__      ,				-1)
                         A__   				=					next_tokens // scores_sum.shape[1]
                         A__   				=					seq_lengths[next_tokens_source]
                         A__   				=					next_tokens % scores_sum.shape[1]
                         A__   				=					next_tokens.unsqueeze(1)
                         A__   				=					tokens[next_tokens_source]
                         A__   				=					torch.cat((tokens, next_tokens)      ,				dim=1)
                         A__   				=					generated[next_tokens_source]
                         A__   				=					scores_sum_average * seq_lengths
                         A__   				=					is_stopped[next_tokens_source]

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

           A__   				=					scores / seq_lengths
           A__   				=					scores.argsort(descending=UpperCAmelCase__)
           # tokens tensors are already padded to max_seq_length
           A__   				=					[tokens[i] for i in order]
           A__   				=					torch.stack(UpperCAmelCase__      ,				dim=0)
           A__   				=					torch.tensor([seq_lengths[i] for i in order]      ,				dtype=seq_lengths.dtype)
           return output_texts, seq_lengths








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


