





"""simple docstring"""




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					Dict      					=DebertaTokenizer
							lowerCamelCase   :					Optional[Any]      					=True
							lowerCamelCase   :					List[Any]      					=DebertaTokenizerFast






							def     __a   (   self      )	-> Optional[int]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a      :		Optional[int]	=				[
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "\u0120",
								    "\u0120l",
								    "\u0120n",
								    "\u0120lo",
								    "\u0120low",
								    "er",
								    "\u0120lowest",
								    "\u0120newer",
								    "\u0120wider",
								    "[UNK]",
								]
								a      :		str	=				dict(zip(lowerCAmelCase__      ,					range(len(lowerCAmelCase__      )      )      )      )
								a      :		str	=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
								a      :		Dict	=				{"unk_token": "[UNK]"}

								a      :		Union[str, Any]	=				os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES["vocab_file"]      )
								a      :		str	=				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(lowerCAmelCase__      ) + "\n"      )
								with open(self.merges_file      ,					"w"      ,					encoding="utf-8"      ) as fp:
									fp.write("\n".join(lowerCAmelCase__      )      )






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> str:
								kwargs.update(self.special_tokens_map      )
								return self.tokenizer_class.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[Any]:
								a      :		Dict	=				"lower newer"
								a      :		Dict	=				"lower newer"
								return input_text, output_text






							def     __a   (   self      )	-> List[Any]:
								a      :		str	=				self.get_tokenizer()
								a      :		str	=				"lower newer"
								a      :		Union[str, Any]	=				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
								a      :		Optional[int]	=				tokenizer.tokenize(lowerCAmelCase__      )
								self.assertListEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )

								a      :		Tuple	=				tokens + [tokenizer.unk_token]
								a      :		Union[str, Any]	=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__      )      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> List[Any]:
								a      :		List[Any]	=				self.get_tokenizer()
								a      :		Optional[Any]	=				tokenizer("Hello"      ,					"World"      )
								a      :		List[Any]	=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd["token_type_ids"]      ,					lowerCAmelCase__      )






							@slow
							def     __a   (   self      )	-> Tuple:
								a      :		Tuple	=				self.tokenizer_class.from_pretrained("microsoft/deberta-base"      )

								a      :		Dict	=				tokenizer.encode("sequence builders"      ,					add_special_tokens=lowerCAmelCase__      )
								a      :		str	=				tokenizer.encode("multi-sequence build"      ,					add_special_tokens=lowerCAmelCase__      )

								a      :		Dict	=				tokenizer.encode(
								    "sequence builders"      ,					add_special_tokens=lowerCAmelCase__      ,					add_prefix_space=lowerCAmelCase__      )
								a      :		Optional[int]	=				tokenizer.encode(
								    "sequence builders"      ,					"multi-sequence build"      ,					add_special_tokens=lowerCAmelCase__      ,					add_prefix_space=lowerCAmelCase__      )

								a      :		str	=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      )
								a      :		List[str]	=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      ,					lowerCAmelCase__      )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode






							@slow
							def     __a   (   self      )	-> str:
								a      :		str	=				[self.tokenizer_class]
								if self.test_rust_tokenizer:
									tokenizer_classes.append(self.rust_tokenizer_class      )

								for tokenizer_class in tokenizer_classes:
									a      :		int	=				tokenizer_class.from_pretrained("microsoft/deberta-base"      )

									a      :		Optional[int]	=				[
									    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
									    "ALBERT incorporates two parameter reduction techniques",
									    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
									    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
									    " vocabulary embedding.",
									]

									a      :		Dict	=				tokenizer(lowerCAmelCase__      ,					padding=lowerCAmelCase__      )
									a      :		Optional[Any]	=				[tokenizer.decode(lowerCAmelCase__      ,					skip_special_tokens=lowerCAmelCase__      ) for seq in encoding["input_ids"]]

									# fmt: off
									a      :		Optional[int]	=				{
									    "input_ids": [
									        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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],
									        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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],
									        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
									    ],
									    "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, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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]
									    ]
									}
									# fmt: on

									a      :		Union[str, Any]	=				[
									    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
									    "ALBERT incorporates two parameter reduction techniques",
									    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
									    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
									    " vocabulary embedding.",
									]

									self.assertDictEqual(encoding.data      ,					lowerCAmelCase__      )

									for expected, decoded in zip(lowerCAmelCase__      ,					lowerCAmelCase__      ):
										self.assertEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )









"""simple docstring"""




import torch
from torch import nn

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


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		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]

								a      :		List[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, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				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"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


