

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			tempfile.mkdtemp()

		UpperCAmelCase_       				=			BlipImageProcessor()
		UpperCAmelCase_       				=			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)

		UpperCAmelCase_       				=			BlipaProcessor(lowerCAmelCase		,	lowerCAmelCase							)

		processor.save_pretrained(self.tmpdirname							)




	def       __A					(		self	:						int		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		return AutoProcessor.from_pretrained(self.tmpdirname		,	**lowerCAmelCase							).tokenizer




	def       __A					(		self	:						List[Any]		,	**lowerCAmelCase	:						Any							):


		'''simple docstring'''







		return AutoProcessor.from_pretrained(self.tmpdirname		,	**lowerCAmelCase							).image_processor




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		shutil.rmtree(self.tmpdirname							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[np.random.randint(255		,	size=(3, 30, 400)		,	dtype=np.uinta							)]

		UpperCAmelCase_       				=			[Image.fromarray(np.moveaxis(lowerCAmelCase		,	0		,	-1							)							) for x in image_inputs]

		return image_inputs




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=self.get_tokenizer()		,	image_processor=self.get_image_processor()							)
		processor.save_pretrained(self.tmpdirname							)

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

		UpperCAmelCase_       				=			BlipaProcessor.from_pretrained(
		    self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=lowerCAmelCase		,	padding_value=1.0							)

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

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




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_image_processor()
		UpperCAmelCase_       				=			self.get_tokenizer()

		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=lowerCAmelCase		,	image_processor=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.prepare_image_inputs()

		UpperCAmelCase_       				=			image_processor(lowerCAmelCase		,	return_tensors="np"							)
		UpperCAmelCase_       				=			processor(images=lowerCAmelCase		,	return_tensors="np"							)

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




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_image_processor()
		UpperCAmelCase_       				=			self.get_tokenizer()

		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=lowerCAmelCase		,	image_processor=lowerCAmelCase							)

		UpperCAmelCase_       				=			"lower newer"

		UpperCAmelCase_       				=			processor(text=lowerCAmelCase							)

		UpperCAmelCase_       				=			tokenizer(lowerCAmelCase		,	return_token_type_ids=lowerCAmelCase							)

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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_image_processor()
		UpperCAmelCase_       				=			self.get_tokenizer()

		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=lowerCAmelCase		,	image_processor=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			processor(text=lowerCAmelCase		,	images=lowerCAmelCase							)

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

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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_image_processor()
		UpperCAmelCase_       				=			self.get_tokenizer()

		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=lowerCAmelCase		,	image_processor=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			processor.batch_decode(lowerCAmelCase							)
		UpperCAmelCase_       				=			tokenizer.batch_decode(lowerCAmelCase							)

		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_image_processor()
		UpperCAmelCase_       				=			self.get_tokenizer()

		UpperCAmelCase_       				=			BlipaProcessor(tokenizer=lowerCAmelCase		,	image_processor=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			processor(text=lowerCAmelCase		,	images=lowerCAmelCase							)

		# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
		self.assertListEqual(list(inputs.keys()							)		,	["pixel_values", "input_ids", "attention_mask"]							)


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3