'''simple docstring'''





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:	Any    =get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 			__A (					UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		int			  =    GPTSwaTokenizer
    a__			:		Optional[Any]			  =    False
    a__			:		str			  =    True
    a__			:		Optional[int]			  =    False



    def    _lowercase		(self       :       int					):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_			       =  GPTSwaTokenizer(__a  ,     eos_token="<unk>"  ,     bos_token="<unk>"  ,     pad_token="<unk>"					)

         tokenizer.save_pretrained(self.tmpdirname					)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  "This is a test"
         UpperCAmelCase_			       =  "This is a test"
         return input_text, output_text



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  "<s>"
         UpperCAmelCase_			       =  1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a					)  ,     __a					)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a					)  ,     __a					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  list(self.get_tokenizer().get_vocab().keys()					)

         self.assertEqual(vocab_keys[0]  ,     "<unk>"					)
         self.assertEqual(vocab_keys[1]  ,     "<s>"					)
         self.assertEqual(vocab_keys[-1]  ,     "j"					)
         self.assertEqual(len(__a					)  ,     2000					)



    def    _lowercase		(self       :       Optional[Any]					):
         self.assertEqual(self.get_tokenizer().vocab_size  ,     2000					)



    def    _lowercase		(self       :       List[Any]					):
         UpperCAmelCase_			       =  GPTSwaTokenizer(__a					)

         UpperCAmelCase_			       =  tokenizer.tokenize("This is a test"					)
         self.assertListEqual(__a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]					)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(__a					)  ,     [465, 287, 265, 631, 842]					)

         UpperCAmelCase_			       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
         # fmt: off
         self.assertListEqual(
             __a  ,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]  ,     )
         # fmt: on

         UpperCAmelCase_			       =  tokenizer.convert_tokens_to_ids(__a					)
         self.assertListEqual(
             __a  ,     [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]  ,     )

         UpperCAmelCase_			       =  tokenizer.convert_ids_to_tokens(__a					)
         # fmt: off
         self.assertListEqual(
             __a  ,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]					)
         # fmt: on



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  GPTSwaTokenizer(__a					)
         UpperCAmelCase_			       =  ["This is a test", "I was born in 92000, and this is falsé."]
         UpperCAmelCase_			       =  [
             [465, 287, 265, 631, 842],
             [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
         ]

         # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
         for text, expected_ids in zip(__a  ,     __a					):
              self.assertListEqual(tokenizer.encode_fast(__a					)  ,     __a					)

         # Test that decode_fast returns the input text
         for text, token_ids in zip(__a  ,     __a					):
              self.assertEqual(tokenizer.decode_fast(__a					)  ,     __a					)






    @slow
    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  [
             "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
             "Hey there, how are you doing this fine day?",
             "This is a text with a trailing spaces followed by a dot     .",
             "Häj sväjs lillebrör! =)",
             "Det är inget fel på Mr. Cool",
         ]

         # fmt: off
         UpperCAmelCase_			       =  {"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
         # fmt: on
         self.tokenizer_integration_test_util(
             expected_encoding=__a  ,     model_name="AI-Sweden/gpt-sw3-126m"  ,     sequences=__a  ,     )









"""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     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





	@register_to_config
	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 50_257     ,       _SCREAMING_SNAKE_CASE				:       int = 1_024     ,       _SCREAMING_SNAKE_CASE				:       int = 768     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       str = "gelu_new"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 1E-5     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     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						:							int							=     prefix_inner_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     prefix_hidden_dim

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

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





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.encode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.decode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.cat((prefix_embeds, embedding_text)     ,       dim=1    )

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





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       torch.device    )						->	torch.Tensor:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.encode_prefix(_SCREAMING_SNAKE_CASE    )





	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Dict:





					"""simple docstring"""


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







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Tuple							=     None
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.ones(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.int    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.bool    )

					if input_embeds is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_embeds
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     outputs.logits
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     logits.softmax(-1    ).log()

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

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

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





