





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     str	 =    MvpTokenizer
						__magic_name__:     str	 =    MvpTokenizerFast
						__magic_name__:     str	 =    True
						__magic_name__:     Any	 =    filter_roberta_detectors






						def UpperCAmelCase_			( self : Optional[Any]		)							->      List[str]:







							"""simple docstring"""


							super().setUp()
							snake_case_						:     str     =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							snake_case_						:     Optional[int]     =		dict(zip(_A						,				range(len(_A		)		)		)		)
							snake_case_						:     Optional[int]     =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							snake_case_						:     Optional[Any]     =		{'unk_token': '<unk>'}

							snake_case_						:     Union[str, Any]     =		os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['vocab_file']		)
							snake_case_						:     Tuple     =		os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['merges_file']		)
							with open(self.vocab_file						,				'w'						,				encoding='utf-8'		) as fp:
								fp.write(json.dumps(_A		) + '\n'		)
							with open(self.merges_file						,				'w'						,				encoding='utf-8'		) as fp:
								fp.write('\n'.join(_A		)		)






						def UpperCAmelCase_			( self : List[Any]						,				**_A : Any		)							->      Union[str, Any]:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return self.tokenizer_class.from_pretrained(self.tmpdirname						,				**_A		)






						def UpperCAmelCase_			( self : List[Any]						,				**_A : List[str]		)							->      Any:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,				**_A		)






						def UpperCAmelCase_			( self : List[str]						,				_A : Optional[int]		)							->      Any:







							"""simple docstring"""


							return "lower newer", "lower newer"






						@cached_property
						def UpperCAmelCase_			( self : List[str]		)							->      List[str]:







							"""simple docstring"""


							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'		)






						@cached_property
						def UpperCAmelCase_			( self : List[Any]		)							->      str:







							"""simple docstring"""


							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'		)






						@require_torch
						def UpperCAmelCase_			( self : List[str]		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     Any     =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							snake_case_						:     List[Any]     =		[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[int]     =		tokenizer(_A						,				max_length=len(_A		)						,				padding=_A						,				return_tensors='pt'		)
								self.assertIsInstance(_A						,				_A		)

								self.assertEqual((2, 9)						,				batch.input_ids.shape		)
								self.assertEqual((2, 9)						,				batch.attention_mask.shape		)
								snake_case_						:     List[str]     =		batch.input_ids.tolist()[0]
								self.assertListEqual(_A						,				_A		)
								# Test that special tokens are reset






						@require_torch
						def UpperCAmelCase_			( self : str		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[Any]     =		tokenizer(_A						,				padding=_A						,				return_tensors='pt'		)
								# check if input_ids are returned and no labels
								self.assertIn('input_ids'						,				_A		)
								self.assertIn('attention_mask'						,				_A		)
								self.assertNotIn('labels'						,				_A		)
								self.assertNotIn('decoder_attention_mask'						,				_A		)






						@require_torch
						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     str     =		tokenizer(text_target=_A						,				max_length=32						,				padding='max_length'						,				return_tensors='pt'		)
								self.assertEqual(32						,				targets['input_ids'].shape[1]		)






						@require_torch
						def UpperCAmelCase_			( self : List[str]		)							->      Union[str, Any]:







							"""simple docstring"""


							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[int]     =		tokenizer(
								    ['I am a small frog' * 1024, 'I am a small frog']						,				padding=_A						,				truncation=_A						,				return_tensors='pt'		)
								self.assertIsInstance(_A						,				_A		)
								self.assertEqual(batch.input_ids.shape						,				(2, 1024)		)






						@require_torch
						def UpperCAmelCase_			( self : List[Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		['A long paragraph for summarization.']
							snake_case_						:     Tuple     =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Any     =		tokenizer(_A						,				text_target=_A						,				return_tensors='pt'		)
								snake_case_						:     Union[str, Any]     =		inputs['input_ids']
								snake_case_						:     Dict     =		inputs['labels']
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)






						def UpperCAmelCase_			( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Any		)							->      List[str]:







							"""simple docstring"""


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
									snake_case_						:     Tuple     =		self.rust_tokenizer_class.from_pretrained(_A						,				**_A		)
									snake_case_						:     Tuple     =		self.tokenizer_class.from_pretrained(_A						,				**_A		)
									snake_case_						:     Optional[int]     =		'A, <mask> AllenNLP sentence.'
									snake_case_						:     Optional[Any]     =		tokenizer_r.encode_plus(_A						,				add_special_tokens=_A						,				return_token_type_ids=_A		)
									snake_case_						:     Dict     =		tokenizer_p.encode_plus(_A						,				add_special_tokens=_A						,				return_token_type_ids=_A		)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r['token_type_ids']		)						,				sum(tokens_p['token_type_ids']		)		)

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)						,				sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)						,				)

									snake_case_						:     Any     =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
									snake_case_						:     List[str]     =		tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p['input_ids']						,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
									self.assertSequenceEqual(tokens_r['input_ids']						,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

									self.assertSequenceEqual(
									    _A						,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
									self.assertSequenceEqual(
									    _A						,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@register_to_config
						def __init__( self : Optional[Any]						,				*,
						    _A : int = 4						,				_A : int = 768						,				_A : int						,				_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     int     =		nn.Parameter(torch.zeros(_A		)		)

							# parameters for additional clip time embeddings
							snake_case_						:     Tuple     =		nn.Linear(_A						,				_A		)
							snake_case_						:     List[Any]     =		nn.Linear(_A						,				_A		)

							# parameters for encoder hidden states
							snake_case_						:     Union[str, Any]     =		clip_extra_context_tokens
							snake_case_						:     str     =		nn.Linear(
							    _A						,				self.clip_extra_context_tokens * cross_attention_dim		)
							snake_case_						:     Any     =		nn.Linear(_A						,				_A		)
							snake_case_						:     Tuple     =		nn.LayerNorm(_A		)






						def UpperCAmelCase_			( self : List[str]						,				*, _A : Tuple						,				_A : List[Any]						,				_A : str						,				_A : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								snake_case_						:     Optional[int]     =		image_embeddings.shape[0]
								snake_case_						:     Optional[Any]     =		self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
								snake_case_						:     Optional[Any]     =		classifier_free_guidance_embeddings.expand(
								    _A						,				-1		)
								snake_case_						:     Any     =		torch.cat([classifier_free_guidance_embeddings, image_embeddings]						,				dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							snake_case_						:     str     =		prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							snake_case_						:     str     =		self.embedding_proj(_A		)
							snake_case_						:     Dict     =		self.clip_image_embeddings_project_to_time_embeddings(_A		)
							snake_case_						:     Tuple     =		time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							snake_case_						:     List[str]     =		self.clip_extra_context_tokens_proj(_A		)
							snake_case_						:     Optional[Any]     =		clip_extra_context_tokens.reshape(_A						,				-1						,				self.clip_extra_context_tokens		)
							snake_case_						:     int     =		clip_extra_context_tokens.permute(0						,				2						,				1		)

							snake_case_						:     Optional[int]     =		self.encoder_hidden_states_proj(_A		)
							snake_case_						:     Any     =		self.text_encoder_hidden_states_norm(_A		)
							snake_case_						:     Dict     =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,				dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings

