


'''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


lowerCAmelCase     :  Optional[Any]											=    get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     GPTSwaTokenizer
     __magic_name__           =     False
     __magic_name__           =     True
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            super().setUp()

            # We have a SentencePiece fixture for testing
            _lowerCAmelCase     :				List[Any]       =							GPTSwaTokenizer(snake_case__   ,						eos_token='<unk>'   ,						bos_token='<unk>'   ,						pad_token='<unk>' )

            tokenizer.save_pretrained(self.tmpdirname )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							'This is a test'
            _lowerCAmelCase     :				Optional[Any]       =							'This is a test'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							'<s>'
            _lowerCAmelCase     :				Optional[Any]       =							1

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							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(snake_case__ )   ,						2000 )







     def a						(			self ):

            '''simple docstring'''

            self.assertEqual(self.get_tokenizer().vocab_size   ,						2000 )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							GPTSwaTokenizer(snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize('This is a test' )
            self.assertListEqual(snake_case__   ,						['▁This', '▁is', '▁a', '▁t', 'est'] )

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

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

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

            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.convert_ids_to_tokens(snake_case__ )
            # fmt: off
            self.assertListEqual(
                snake_case__   ,						['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] )
            # fmt: on







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							GPTSwaTokenizer(snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							['This is a test', 'I was born in 92000, and this is falsé.']
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [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(snake_case__   ,						snake_case__ ):
                   self.assertListEqual(tokenizer.encode_fast(snake_case__ )   ,						snake_case__ )

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







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							[
                '<|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
            _lowerCAmelCase     :				List[Any]       =							{'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 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=snake_case__   ,						model_name='AI-Sweden/gpt-sw3-126m'   ,						sequences=snake_case__   ,						)





'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     42
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @register_to_config
     def __init__(			self   ,						snake_case__ = 6_5536   ,						snake_case__ = None   ,						snake_case__ = 2   ,						snake_case__ = 2   ,						snake_case__ = 0   ,						snake_case__ = "fourier"   ,						snake_case__ = True   ,						snake_case__ = False   ,						snake_case__ = 0.0   ,						snake_case__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,						snake_case__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,						snake_case__ = "UNetMidBlock1D"   ,						snake_case__ = None   ,						snake_case__ = (32, 32, 64)   ,						snake_case__ = None   ,						snake_case__ = 8   ,						snake_case__ = 1   ,						snake_case__ = False   ,						):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase     :				Optional[int]       =							sample_size

            # time
            if time_embedding_type == "fourier":
                   _lowerCAmelCase     :				Tuple       =							GaussianFourierProjection(
                       embedding_size=8   ,						set_W_to_weight=snake_case__   ,						log=snake_case__   ,						flip_sin_to_cos=snake_case__ )
                   _lowerCAmelCase     :				List[Any]       =							2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                   _lowerCAmelCase     :				Optional[Any]       =							Timesteps(
                       block_out_channels[0]   ,						flip_sin_to_cos=snake_case__   ,						downscale_freq_shift=snake_case__ )
                   _lowerCAmelCase     :				Optional[int]       =							block_out_channels[0]

            if use_timestep_embedding:
                   _lowerCAmelCase     :				Any       =							block_out_channels[0] * 4
                   _lowerCAmelCase     :				List[str]       =							TimestepEmbedding(
                       in_channels=snake_case__   ,						time_embed_dim=snake_case__   ,						act_fn=snake_case__   ,						out_dim=block_out_channels[0]   ,						)

            _lowerCAmelCase     :				Tuple       =							nn.ModuleList([] )
            _lowerCAmelCase     :				Tuple       =							None
            _lowerCAmelCase     :				Optional[Any]       =							nn.ModuleList([] )
            _lowerCAmelCase     :				str       =							None

            # down
            _lowerCAmelCase     :				List[str]       =							in_channels
            for i, down_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				str       =							output_channel
                   _lowerCAmelCase     :				Any       =							block_out_channels[i]

                   if i == 0:
                          input_channel += extra_in_channels

                   _lowerCAmelCase     :				Tuple       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				List[str]       =							get_down_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_downsample=not is_final_block or downsample_each_block   ,						)
                   self.down_blocks.append(snake_case__ )

            # mid
            _lowerCAmelCase     :				List[str]       =							get_mid_block(
                snake_case__   ,						in_channels=block_out_channels[-1]   ,						mid_channels=block_out_channels[-1]   ,						out_channels=block_out_channels[-1]   ,						embed_dim=block_out_channels[0]   ,						num_layers=snake_case__   ,						add_downsample=snake_case__   ,						)

            # up
            _lowerCAmelCase     :				Union[str, Any]       =							list(reversed(snake_case__ ) )
            _lowerCAmelCase     :				List[Any]       =							reversed_block_out_channels[0]
            if out_block_type is None:
                   _lowerCAmelCase     :				Tuple       =							out_channels
            else:
                   _lowerCAmelCase     :				Tuple       =							block_out_channels[0]

            for i, up_block_type in enumerate(snake_case__ ):
                   _lowerCAmelCase     :				int       =							output_channel
                   _lowerCAmelCase     :				Dict       =							(
                       reversed_block_out_channels[i + 1] if i < len(snake_case__ ) - 1 else final_upsample_channels
                   )

                   _lowerCAmelCase     :				Union[str, Any]       =							i == len(snake_case__ ) - 1

                   _lowerCAmelCase     :				Tuple       =							get_up_block(
                       snake_case__   ,						num_layers=snake_case__   ,						in_channels=snake_case__   ,						out_channels=snake_case__   ,						temb_channels=block_out_channels[0]   ,						add_upsample=not is_final_block   ,						)
                   self.up_blocks.append(snake_case__ )
                   _lowerCAmelCase     :				List[str]       =							output_channel

            # out
            _lowerCAmelCase     :				Tuple       =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,						32 )
            _lowerCAmelCase     :				Any       =							get_out_block(
                out_block_type=snake_case__   ,						num_groups_out=snake_case__   ,						embed_dim=block_out_channels[0]   ,						out_channels=snake_case__   ,						act_fn=snake_case__   ,						fc_dim=block_out_channels[-1] // 4   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							timestep
            if not torch.is_tensor(snake_case__ ):
                   _lowerCAmelCase     :				Optional[int]       =							torch.tensor([timesteps]   ,						dtype=torch.long   ,						device=sample.device )
            elif torch.is_tensor(snake_case__ ) and len(timesteps.shape ) == 0:
                   _lowerCAmelCase     :				List[Any]       =							timesteps[None].to(sample.device )

            _lowerCAmelCase     :				int       =							self.time_proj(snake_case__ )
            if self.config.use_timestep_embedding:
                   _lowerCAmelCase     :				Optional[int]       =							self.time_mlp(snake_case__ )
            else:
                   _lowerCAmelCase     :				List[str]       =							timestep_embed[..., None]
                   _lowerCAmelCase     :				Dict       =							timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
                   _lowerCAmelCase     :				Tuple       =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

            # 2. down
            _lowerCAmelCase     :				str       =							()
            for downsample_block in self.down_blocks:
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							downsample_block(hidden_states=snake_case__   ,						temb=snake_case__ )
                   down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                   _lowerCAmelCase     :				Any       =							self.mid_block(snake_case__   ,						snake_case__ )

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks ):
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[-1:]
                   _lowerCAmelCase     :				Union[str, Any]       =							down_block_res_samples[:-1]
                   _lowerCAmelCase     :				Union[str, Any]       =							upsample_block(snake_case__   ,						res_hidden_states_tuple=snake_case__   ,						temb=snake_case__ )

            # 5. post-process
            if self.out_block:
                   _lowerCAmelCase     :				Optional[Any]       =							self.out_block(snake_case__   ,						snake_case__ )

            if not return_dict:
                   return (sample,)

            return UNetaDOutput(sample=snake_case__ )

