

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__()
				self.register_modules(
				    vae=__UpperCamelCase			,					text_encoder=__UpperCamelCase			,					tokenizer=__UpperCamelCase			,					unet=__UpperCamelCase			,					scheduler=__UpperCamelCase			,					safety_checker=__UpperCamelCase			,					feature_extractor=__UpperCamelCase			,					)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase = "auto"				):

				"""simple docstring"""


				if slice_size == "auto":
					# half the attention head size is usually a good trade-off between
					# speed and memory
					snake_case_   		=			self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.enable_attention_slicing(__UpperCamelCase				)







			@torch.no_grad()
			def __call__(     self			,					__UpperCamelCase			,					__UpperCamelCase = 5_12			,					__UpperCamelCase = 5_12			,					__UpperCamelCase = 50			,					__UpperCamelCase = 7.5			,					__UpperCamelCase = None			,					__UpperCamelCase = 1			,					__UpperCamelCase = 0.0			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = "pil"			,					__UpperCamelCase = True			,					__UpperCamelCase = None			,					__UpperCamelCase = 1			,					__UpperCamelCase = None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				if isinstance(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			1
				elif isinstance(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			len(__UpperCamelCase				)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase				)}"""				)

				if height % 8 != 0 or width % 8 != 0:
					raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""				)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(__UpperCamelCase			,					__UpperCamelCase				) or callback_steps <= 0)
				):
					raise ValueError(
					    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
					    f""" {type(__UpperCamelCase				)}."""				)

				# get prompt text embeddings
				snake_case_   		=			self.tokenizer(
				    __UpperCamelCase			,					padding='max_length'			,					max_length=self.tokenizer.model_max_length			,					return_tensors='pt'			,					)
				snake_case_   		=			text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_   		=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
					logger.warning(
					    'The following part of your input was truncated because CLIP can only handle sequences up to'
					    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""				)
					snake_case_   		=			text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
					snake_case_   		=			self.text_encoder(text_input_ids.to(self.device				)				)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				snake_case_		,							snake_case_		,							snake_case_   		=			text_embeddings.shape
				snake_case_   		=			text_embeddings.repeat(1			,					__UpperCamelCase			,					1				)
				snake_case_   		=			text_embeddings.view(bs_embed * num_images_per_prompt			,					__UpperCamelCase			,					-1				)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				snake_case_   		=			guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
					snake_case_   		=			42
					if negative_prompt is None:
						snake_case_   		=			['']
					elif type(__UpperCamelCase				) is not type(__UpperCamelCase				):
						raise TypeError(
						    f"""`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCamelCase				)} !="""
						    f""" {type(__UpperCamelCase				)}."""				)
					elif isinstance(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			[negative_prompt]
					elif batch_size != len(__UpperCamelCase				):
						raise ValueError(
						    f"""`negative_prompt`: {negative_prompt} has batch size {len(__UpperCamelCase				)}, but `prompt`:"""
						    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
						    ' the batch size of `prompt`.'				)
					else:
						snake_case_   		=			negative_prompt

					snake_case_   		=			text_input_ids.shape[-1]
					snake_case_   		=			self.tokenizer(
					    __UpperCamelCase			,					padding='max_length'			,					max_length=__UpperCamelCase			,					truncation=__UpperCamelCase			,					return_tensors='pt'			,					)
					snake_case_   		=			self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					snake_case_   		=			uncond_embeddings.shape[1]
					snake_case_   		=			uncond_embeddings.repeat(__UpperCamelCase			,					__UpperCamelCase			,					1				)
					snake_case_   		=			uncond_embeddings.view(batch_size * num_images_per_prompt			,					__UpperCamelCase			,					-1				)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					snake_case_   		=			torch.cat([uncond_embeddings, text_embeddings]				)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				snake_case_   		=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				snake_case_   		=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				snake_case_   		=			text_embeddings.dtype
				if latents is None:
					if self.device.type == "mps":
						# randn does not exist on mps
						snake_case_   		=			torch.randn(
						    __UpperCamelCase			,					generator=__UpperCamelCase			,					device='cpu'			,					dtype=__UpperCamelCase				).to(self.device				)
						snake_case_   		=			torch.randn(__UpperCamelCase			,					generator=__UpperCamelCase			,					device='cpu'			,					dtype=__UpperCamelCase				).to(
						    self.device				)
					else:
						snake_case_   		=			torch.randn(
						    __UpperCamelCase			,					generator=__UpperCamelCase			,					device=self.device			,					dtype=__UpperCamelCase				)
						snake_case_   		=			torch.randn(__UpperCamelCase			,					generator=__UpperCamelCase			,					device=self.device			,					dtype=__UpperCamelCase				)
				else:
					if latents_reference.shape != latents_shape:
						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
					snake_case_   		=			latents_reference.to(self.device				)
					snake_case_   		=			latents.to(self.device				)

				# This is the key part of the pipeline where we
				# try to ensure that the generated images w/ the same seed
				# but different sizes actually result in similar images
				snake_case_   		=			(latents_shape[3] - latents_shape_reference[3]) // 2
				snake_case_   		=			(latents_shape[2] - latents_shape_reference[2]) // 2
				snake_case_   		=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				snake_case_   		=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				snake_case_   		=			0 if dx < 0 else dx
				snake_case_   		=			0 if dy < 0 else dy
				snake_case_   		=			max(-dx			,					0				)
				snake_case_   		=			max(-dy			,					0				)
				# import pdb
				# pdb.set_trace()
				snake_case_   		=			latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(__UpperCamelCase				)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				snake_case_   		=			self.scheduler.timesteps.to(self.device				)

				# scale the initial noise by the standard deviation required by the scheduler
				snake_case_   		=			latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				snake_case_   		=			'eta' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
				snake_case_   		=			{}
				if accepts_eta:
					snake_case_   		=			eta

				for i, t in enumerate(self.progress_bar(__UpperCamelCase				)				):
					# expand the latents if we are doing classifier free guidance
					snake_case_   		=			torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
					snake_case_   		=			self.scheduler.scale_model_input(__UpperCamelCase			,					__UpperCamelCase				)

					# predict the noise residual
					snake_case_   		=			self.unet(__UpperCamelCase			,					__UpperCamelCase			,					encoder_hidden_states=__UpperCamelCase				).sample

					# perform guidance
					if do_classifier_free_guidance:
						snake_case_		,							snake_case_   		=			noise_pred.chunk(2				)
						snake_case_   		=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_   		=			self.scheduler.step(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase				).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			1 / 0.1_8215 * latents
				snake_case_   		=			self.vae.decode(__UpperCamelCase				).sample

				snake_case_   		=			(image / 2 + 0.5).clamp(0			,					1				)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				).float().numpy()

				if self.safety_checker is not None:
					snake_case_   		=			self.feature_extractor(self.numpy_to_pil(__UpperCamelCase				)			,					return_tensors='pt'				).to(
					    self.device				)
					snake_case_		,							snake_case_   		=			self.safety_checker(
					    images=__UpperCamelCase			,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype				)				)
				else:
					snake_case_   		=			None

				if output_type == "pil":
					snake_case_   		=			self.numpy_to_pil(__UpperCamelCase				)

				if not return_dict:
					return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=__UpperCamelCase			,					nsfw_content_detected=__UpperCamelCase				)



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	None
			__A    	=	BloomTokenizerFast
			__A    	=	BloomTokenizerFast
			__A    	=	True
			__A    	=	False
			__A    	=	"""tokenizer_file"""
			__A    	=	{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().setUp()
				snake_case_   		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'				)
				tokenizer.save_pretrained(self.tmpdirname				)


			def       __lowerCAmelCase			(     self			,					**__UpperCamelCase				):

				"""simple docstring"""


				kwargs.update(self.special_tokens_map				)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()

				snake_case_   		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
				snake_case_   		=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

				snake_case_   		=			tokenizer.batch_encode_plus(__UpperCamelCase				)['input_ids']
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			tokenizer.batch_decode(__UpperCamelCase				)
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=6				):

				"""simple docstring"""


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
						snake_case_   		=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,					**__UpperCamelCase				)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						snake_case_   		=			'This is a simple input'
						snake_case_   		=			['This is a simple input 1', 'This is a simple input 2']
						snake_case_   		=			('This is a simple input', 'This is a pair')
						snake_case_   		=			[
						    ('This is a simple input 1', 'This is a simple input 2'),
						    ('This is a simple pair 1', 'This is a simple pair 2'),
						]

						# Simple input tests
						try:
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)

							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
						except ValueError:
							self.fail('Bloom Tokenizer should be able to deal with padding'				)

						snake_case_   		=			None  # Hotfixing padding = None
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()
				snake_case_   		=			load_dataset('xnli'			,					'all_languages'			,					split='test'			,					streaming=__UpperCamelCase				)

				snake_case_   		=			next(iter(__UpperCamelCase				)				)['premise']  # pick up one data
				snake_case_   		=			list(sample_data.values()				)

				snake_case_   		=			list(map(tokenizer.encode			,					__UpperCamelCase				)				)
				snake_case_   		=			[tokenizer.decode(__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase				) for x in output_tokens]
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)			,					1				)
				self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)			,					1				)
