



import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__					:     Any    		=   get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     List[str]      							=      BartphoTokenizer
						_snake_case			:     Tuple      							=      False
						_snake_case			:     int      							=      True



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							super().setUp()

							__lowercase		       =			["▁This", "▁is", "▁a", "▁t", "est"]
							__lowercase		       =			dict(zip(lowerCamelCase							,      range(len(lowerCamelCase	)	)	)	)
							__lowercase		       =			{"unk_token": "<unk>"}

							__lowercase		       =			os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES["monolingual_vocab_file"]	)
							with open(self.monolingual_vocab_file							,      "w"							,      encoding="utf-8"	) as fp:
								for token in vocab_tokens:
									fp.write(f"""{token} {vocab_tokens[token]}\n"""	)

							__lowercase		       =			BartphoTokenizer(lowerCamelCase							,      self.monolingual_vocab_file							,      **self.special_tokens_map	)
							tokenizer.save_pretrained(self.tmpdirname	)



						def     _snake_case	(	self	:							Tuple							,      **lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							kwargs.update(self.special_tokens_map	)
							return BartphoTokenizer.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			"This is a là test"
							__lowercase		       =			"This is a<unk><unk> test"
							return input_text, output_text



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			BartphoTokenizer(lowerCamelCase							,      self.monolingual_vocab_file							,      **self.special_tokens_map	)
							__lowercase		       =			"This is a là test"
							__lowercase		       =			"▁This ▁is ▁a ▁l à ▁t est".split()
							__lowercase		       =			tokenizer.tokenize(lowerCamelCase	)
							self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			tokens + [tokenizer.unk_token]
							__lowercase		       =			[4, 5, 6, 3, 3, 7, 8, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)							,      lowerCamelCase	)








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			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]

							__lowercase		       =			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, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			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"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



