



'''simple docstring'''



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



					@register_to_config
					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = 5_0257			,				UpperCamelCase = 1024			,				UpperCamelCase = 768			,				UpperCamelCase = 12			,				UpperCamelCase = 12			,				UpperCamelCase = None			,				UpperCamelCase = "gelu_new"			,				UpperCamelCase = 0.1			,				UpperCamelCase = 0.1			,				UpperCamelCase = 0.1			,				UpperCamelCase = 1E-5			,				UpperCamelCase = 0.02			,				UpperCamelCase = True			,				UpperCamelCase = True			,				UpperCamelCase = False			,				UpperCamelCase = False			,				)     ->			Tuple:
						super().__init__()

						__lowerCAmelCase												=  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												=  prefix_inner_dim
						__lowerCAmelCase												=  prefix_hidden_dim

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

						__lowerCAmelCase												=  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			,				)
						__lowerCAmelCase												=  GPTaLMHeadModel(UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = None			,				)     ->			str:
						__lowerCAmelCase												=  self.transformer.transformer.wte(UpperCamelCase     )
						__lowerCAmelCase												=  self.encode_prefix(UpperCamelCase     )
						__lowerCAmelCase												=  self.decode_prefix(UpperCamelCase     )
						__lowerCAmelCase												=  torch.cat((prefix_embeds, embedding_text)			,				dim=1     )

						if labels is not None:
							__lowerCAmelCase												=  self.get_dummy_token(input_ids.shape[0]			,				input_ids.device     )
							__lowerCAmelCase												=  torch.cat((dummy_token, input_ids)			,				dim=1     )
						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			torch.Tensor:
						return torch.zeros(UpperCamelCase			,				self.prefix_length			,				dtype=torch.intaa			,				device=UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[Any]:
						return self.encode_prefix(UpperCamelCase     )



					@torch.no_grad()
					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  torch.split(UpperCamelCase			,				1			,				dim=0     )
						__lowerCAmelCase												=  []
						__lowerCAmelCase												=  []
						for feature in features:
							__lowerCAmelCase												=  self.decode_prefix(feature.to(UpperCamelCase     )     )  # back to the clip feature
							# Only support beam search for now
							__lowerCAmelCase							,       __lowerCAmelCase												=  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]     )
						__lowerCAmelCase												=  torch.stack(UpperCamelCase     )
						__lowerCAmelCase												=  torch.stack(UpperCamelCase     )
						return generated_tokens, generated_seq_lengths



					@torch.no_grad()
					def        UpperCAmelCase_ (    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase = 5			,				UpperCamelCase = 67			,				UpperCamelCase = 1.0			,				UpperCamelCase = None			,				)     ->			str:
						__lowerCAmelCase												=  eos_token_id
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  torch.ones(UpperCamelCase			,				device=UpperCamelCase			,				dtype=torch.int     )
						__lowerCAmelCase												=  torch.zeros(UpperCamelCase			,				device=UpperCamelCase			,				dtype=torch.bool     )

						if input_embeds is not None:
							__lowerCAmelCase												=  input_embeds
						else:
							__lowerCAmelCase												=  self.transformer.transformer.wte(UpperCamelCase     )

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

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

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

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




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCAmelCase	:		Optional[Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[Any]				=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase	:		str				=   {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


lowerCAmelCase	:		Tuple				=   {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						super().__init__(
						    UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				do_lower_case=UpperCamelCase			,				unk_token=UpperCamelCase			,				sep_token=UpperCamelCase			,				pad_token=UpperCamelCase			,				cls_token=UpperCamelCase			,				mask_token=UpperCamelCase			,				tokenize_chinese_chars=UpperCamelCase			,				strip_accents=UpperCamelCase			,				**UpperCamelCase			,				)

						__lowerCAmelCase												=  json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
						if (
						    normalizer_state.get("lowercase"			,				UpperCamelCase     ) != do_lower_case
						    or normalizer_state.get("strip_accents"			,				UpperCamelCase     ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"			,				UpperCamelCase     ) != tokenize_chinese_chars
						):
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				normalizer_state.pop("type"     )     )
							__lowerCAmelCase												=  do_lower_case
							__lowerCAmelCase												=  strip_accents
							__lowerCAmelCase												=  tokenize_chinese_chars
							__lowerCAmelCase												=  normalizer_class(**UpperCamelCase     )

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__lowerCAmelCase												=  [self.sep_token_id]
						__lowerCAmelCase												=  [self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep     ) * [0]
						return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )