




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__      :			int      =  [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']






						@register_to_config
						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = 50_257				,							_A = 1_024				,							_A = 768				,							_A = 12				,							_A = 12				,							_A = None				,							_A = "gelu_new"				,							_A = 0.1				,							_A = 0.1				,							_A = 0.1				,							_A = 1e-5				,							_A = 0.0_2				,							_A = True				,							_A = True				,							_A = False				,							_A = False				,							):







													'''simple docstring'''

													super().__init__()

													__SCREAMING_SNAKE_CASE									= 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."""		)

													__SCREAMING_SNAKE_CASE									= prefix_inner_dim
													__SCREAMING_SNAKE_CASE									= prefix_hidden_dim

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

													__SCREAMING_SNAKE_CASE									= 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				,							)
													__SCREAMING_SNAKE_CASE									= GPTaLMHeadModel(_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A = None				,							_A = None				,							):







													'''simple docstring'''

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

													if labels is not None:
																				__SCREAMING_SNAKE_CASE									= self.get_dummy_token(input_ids.shape[0]				,							input_ids.device		)
																				__SCREAMING_SNAKE_CASE									= torch.cat((dummy_token, input_ids)				,							dim=1		)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.encode_prefix(_A		)






						@torch.no_grad()
						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.split(_A				,							1				,							dim=0		)
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []
													for feature in features:
																				__SCREAMING_SNAKE_CASE									= self.decode_prefix(feature.to(_A		)		)  # back to the clip feature
																				# Only support beam search for now
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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]		)
													__SCREAMING_SNAKE_CASE									= torch.stack(_A		)
													__SCREAMING_SNAKE_CASE									= torch.stack(_A		)
													return generated_tokens, generated_seq_lengths






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= eos_token_id
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= torch.ones(_A				,							device=_A				,							dtype=torch.int		)
													__SCREAMING_SNAKE_CASE									= torch.zeros(_A				,							device=_A				,							dtype=torch.bool		)

													if input_embeds is not None:
																				__SCREAMING_SNAKE_CASE									= input_embeds
													else:
																				__SCREAMING_SNAKE_CASE									= self.transformer.transformer.wte(_A		)

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

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

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

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











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






