





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






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







							"""simple docstring"""


							snake_case_						:     Dict     =		tempfile.mkdtemp()

							snake_case_						:     Dict     =		[
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							snake_case_						:     Tuple     =		os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['vocab_file']		)
							with open(self.vocab_file						,				'w'						,				encoding='utf-8'		) as vocab_writer:
								vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

							snake_case_						:     int     =		{
							    'do_resize': True,
							    'size': 20,
							    'do_center_crop': True,
							    'crop_size': 18,
							    'do_normalize': True,
							    'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							snake_case_						:     Union[str, Any]     =		os.path.join(self.tmpdirname						,				_A		)
							with open(self.image_processor_file						,				'w'						,				encoding='utf-8'		) as fp:
								json.dump(_A						,				_A		)






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







							"""simple docstring"""


							return BertTokenizer.from_pretrained(self.tmpdirname						,				**_A		)






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







							"""simple docstring"""


							return BertTokenizerFast.from_pretrained(self.tmpdirname						,				**_A		)






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







							"""simple docstring"""


							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,				**_A		)






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







							"""simple docstring"""


							shutil.rmtree(self.tmpdirname		)






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







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		[np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta		)]
							snake_case_						:     Optional[Any]     =		[Image.fromarray(np.moveaxis(_A						,				0						,				-1		)		) for x in image_inputs]
							return image_inputs






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







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.get_tokenizer()
							snake_case_						:     Union[str, Any]     =		self.get_rust_tokenizer()
							snake_case_						:     Any     =		self.get_image_processor()

							snake_case_						:     Dict     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)
							processor_slow.save_pretrained(self.tmpdirname		)
							snake_case_						:     str     =		AlignProcessor.from_pretrained(self.tmpdirname						,				use_fast=_A		)

							snake_case_						:     Optional[int]     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)
							processor_fast.save_pretrained(self.tmpdirname		)
							snake_case_						:     Union[str, Any]     =		AlignProcessor.from_pretrained(self.tmpdirname		)

							self.assertEqual(processor_slow.tokenizer.get_vocab()						,				tokenizer_slow.get_vocab()		)
							self.assertEqual(processor_fast.tokenizer.get_vocab()						,				tokenizer_fast.get_vocab()		)
							self.assertEqual(tokenizer_slow.get_vocab()						,				tokenizer_fast.get_vocab()		)
							self.assertIsInstance(processor_slow.tokenizer						,				_A		)
							self.assertIsInstance(processor_fast.tokenizer						,				_A		)

							self.assertEqual(processor_slow.image_processor.to_json_string()						,				image_processor.to_json_string()		)
							self.assertEqual(processor_fast.image_processor.to_json_string()						,				image_processor.to_json_string()		)
							self.assertIsInstance(processor_slow.image_processor						,				_A		)
							self.assertIsInstance(processor_fast.image_processor						,				_A		)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		AlignProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor()		)
							processor.save_pretrained(self.tmpdirname		)

							snake_case_						:     str     =		self.get_tokenizer(bos_token='(BOS)'						,				eos_token='(EOS)'		)
							snake_case_						:     Optional[Any]     =		self.get_image_processor(do_normalize=_A						,				padding_value=1.0		)

							snake_case_						:     Union[str, Any]     =		AlignProcessor.from_pretrained(
							    self.tmpdirname						,				bos_token='(BOS)'						,				eos_token='(EOS)'						,				do_normalize=_A						,				padding_value=1.0		)

							self.assertEqual(processor.tokenizer.get_vocab()						,				tokenizer_add_kwargs.get_vocab()		)
							self.assertIsInstance(processor.tokenizer						,				_A		)

							self.assertEqual(processor.image_processor.to_json_string()						,				image_processor_add_kwargs.to_json_string()		)
							self.assertIsInstance(processor.image_processor						,				_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     int     =		self.get_image_processor()
							snake_case_						:     Optional[Any]     =		self.get_tokenizer()

							snake_case_						:     Optional[int]     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)

							snake_case_						:     int     =		self.prepare_image_inputs()

							snake_case_						:     str     =		image_processor(_A						,				return_tensors='np'		)
							snake_case_						:     Dict     =		processor(images=_A						,				return_tensors='np'		)

							for key in input_image_proc.keys():
								self.assertAlmostEqual(input_image_proc[key].sum()						,				input_processor[key].sum()						,				delta=1E-2		)






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







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.get_image_processor()
							snake_case_						:     List[Any]     =		self.get_tokenizer()

							snake_case_						:     List[str]     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)

							snake_case_						:     int     =		'lower newer'

							snake_case_						:     List[Any]     =		processor(text=_A		)

							snake_case_						:     List[Any]     =		tokenizer(_A						,				padding='max_length'						,				max_length=64		)

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]						,				encoded_processor[key]		)






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







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.get_image_processor()
							snake_case_						:     List[Any]     =		self.get_tokenizer()

							snake_case_						:     Optional[Any]     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)

							snake_case_						:     Dict     =		'lower newer'
							snake_case_						:     List[Any]     =		self.prepare_image_inputs()

							snake_case_						:     int     =		processor(text=_A						,				images=_A		)

							self.assertListEqual(list(inputs.keys()		)						,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)

							# test if it raises when no input is passed
							with pytest.raises(_A		):
								processor()






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







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.get_image_processor()
							snake_case_						:     int     =		self.get_tokenizer()

							snake_case_						:     int     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)

							snake_case_						:     Tuple     =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							snake_case_						:     Union[str, Any]     =		processor.batch_decode(_A		)
							snake_case_						:     str     =		tokenizer.batch_decode(_A		)

							self.assertListEqual(_A						,				_A		)






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







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.get_image_processor()
							snake_case_						:     int     =		self.get_tokenizer()

							snake_case_						:     Optional[int]     =		AlignProcessor(tokenizer=_A						,				image_processor=_A		)

							snake_case_						:     str     =		'lower newer'
							snake_case_						:     Optional[Any]     =		self.prepare_image_inputs()

							snake_case_						:     str     =		processor(text=_A						,				images=_A		)

							self.assertListEqual(list(inputs.keys()		)						,				processor.model_input_names		)








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

