



"""simple docstring"""







import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)  # pylint: disable=invalid-name






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=768		)      ->	str:
				super().__init__(SCREAMING_SNAKE_CASE__		)
				A__											=					proj_size

				A__											=					CLIPVisionModel(SCREAMING_SNAKE_CASE__		)
				A__											=					PaintByExampleMapper(SCREAMING_SNAKE_CASE__		)
				A__											=					nn.LayerNorm(config.hidden_size		)
				A__											=					nn.Linear(config.hidden_size						,	self.proj_size		)

				# uncondition for scaling
				A__											=					nn.Parameter(torch.randn((1, 1, self.proj_size)		)		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False		)      ->	Dict:
				A__											=					self.model(pixel_values=SCREAMING_SNAKE_CASE__		)
				A__											=					clip_output.pooler_output
				A__											=					self.mapper(latent_states[:, None]		)
				A__											=					self.final_layer_norm(SCREAMING_SNAKE_CASE__		)
				A__											=					self.proj_out(SCREAMING_SNAKE_CASE__		)
				if return_uncond_vector:
					return latent_states, self.uncond_vector

				return latent_states






class       UpperCamelCase__			(   nn.Module      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				super().__init__()
				A__											=					(config.num_hidden_layers + 1) // 5
				A__											=					config.hidden_size
				A__											=					1
				A__											=					nn.ModuleList(
				    [
				        BasicTransformerBlock(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	activation_fn="gelu"						,	attention_bias=SCREAMING_SNAKE_CASE__		)
				        for _ in range(SCREAMING_SNAKE_CASE__		)
				    ]		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				for block in self.blocks:
					A__											=					block(SCREAMING_SNAKE_CASE__		)

				return hidden_states









"""simple docstring"""







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       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					[
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				A__											=					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]		)		)

				A__											=					{
				    "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],
				}
				A__											=					os.path.join(self.tmpdirname						,	SCREAMING_SNAKE_CASE__		)
				with open(self.image_processor_file						,	"w"						,	encoding="utf-8"		) as fp:
					json.dump(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				return BertTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]
				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]
				return image_inputs


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_tokenizer()
				A__											=					self.get_rust_tokenizer()
				A__											=					self.get_image_processor()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_slow.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=SCREAMING_SNAKE_CASE__		)

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_fast.save_pretrained(self.tmpdirname		)
				A__											=					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						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				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						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					AlignProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					AlignProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=64		)

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				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(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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




