import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__				= get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class  UpperCamelCase				( lowercase__   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  XLMProphetNetTokenizer
     A_										=  False
     A_										=  True



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           super().setUp()

           # We have a SentencePiece fixture for testing
           _lowerCamelCase			    =				XLMProphetNetTokenizer(__lowerCamelCase				,				keep_accents=__lowerCamelCase						)
           tokenizer.save_pretrained(self.tmpdirname						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				"[PAD]"
           _lowerCamelCase			    =				0

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



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				list(self.get_tokenizer().get_vocab().keys()						)

           self.assertEqual(vocab_keys[0]				,				'''[PAD]'''						)
           self.assertEqual(vocab_keys[1]				,				'''[CLS]'''						)
           self.assertEqual(vocab_keys[-1]				,				'''j'''						)
           self.assertEqual(len(__lowerCamelCase						)				,				10_12						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           self.assertEqual(self.get_tokenizer().vocab_size				,				10_12						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				XLMProphetNetTokenizer(__lowerCamelCase				,				keep_accents=__lowerCamelCase						)

           _lowerCamelCase			    =				tokenizer.tokenize('''This is a test'''						)
           self.assertListEqual(__lowerCamelCase				,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__lowerCamelCase						)				,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,				)

           _lowerCamelCase			    =				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
           self.assertListEqual(
               __lowerCamelCase				,				[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ]				,				)
           _lowerCamelCase			    =				tokenizer.convert_tokens_to_ids(__lowerCamelCase						)
           self.assertListEqual(
               __lowerCamelCase				,				[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
               ]				,				)

           _lowerCamelCase			    =				tokenizer.convert_ids_to_tokens(__lowerCamelCase						)
           self.assertListEqual(
               __lowerCamelCase				,				[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''[UNK]''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''[UNK]''',
                   '''.''',
               ]				,				)



     @cached_property
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				"Hello World!"
           _lowerCamelCase			    =				[3_53_89, 66_72, 49, 2]
           self.assertListEqual(__lowerCamelCase				,				self.big_tokenizer.encode(__lowerCamelCase						)						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				{"input_ids": [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 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], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__lowerCamelCase				,				model_name='''microsoft/xprophetnet-large-wiki100-cased'''				,				revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''				,				)
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				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

           # time
           if time_embedding_type == "fourier":
                 _lowerCamelCase			    =				GaussianFourierProjection(
                     embedding_size=8				,				set_W_to_weight=A_				,				log=A_				,				flip_sin_to_cos=A_						)
                 _lowerCamelCase			    =				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _lowerCamelCase			    =				Timesteps(
                     block_out_channels[0]				,				flip_sin_to_cos=A_				,				downscale_freq_shift=A_						)
                 _lowerCamelCase			    =				block_out_channels[0]

           if use_timestep_embedding:
                 _lowerCamelCase			    =				block_out_channels[0] * 4
                 _lowerCamelCase			    =				TimestepEmbedding(
                     in_channels=A_				,				time_embed_dim=A_				,				act_fn=A_				,				out_dim=block_out_channels[0]				,				)

           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None

           # down
           _lowerCamelCase			    =				in_channels
           for i, down_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_down_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_downsample=not is_final_block or downsample_each_block				,				)
                 self.down_blocks.append(A_						)

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				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=A_				,				add_downsample=A_				,				)

           # up
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           if out_block_type is None:
                 _lowerCamelCase			    =				out_channels
           else:
                 _lowerCamelCase			    =				block_out_channels[0]

           for i, up_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				(
                     reversed_block_out_channels[i + 1] if i < len(A_						) - 1 else final_upsample_channels
                 )

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_up_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_upsample=not is_final_block				,				)
                 self.up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel

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





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           if self.config.use_timestep_embedding:
                 _lowerCamelCase			    =				self.time_mlp(A_						)
           else:
                 _lowerCamelCase			    =				timestep_embed[..., None]
                 _lowerCamelCase			    =				timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
                 _lowerCamelCase			    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

           # 2. down
           _lowerCamelCase			    =				()
           for downsample_block in self.down_blocks:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				downsample_block(hidden_states=A_				,				temb=A_						)
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks						):
                 _lowerCamelCase			    =				down_block_res_samples[-1:]
                 _lowerCamelCase			    =				down_block_res_samples[:-1]
                 _lowerCamelCase			    =				upsample_block(A_				,				res_hidden_states_tuple=A_				,				temb=A_						)

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)