


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 			_lowercase (							__lowerCamelCase							,  unittest.TestCase):


       """simple docstring"""




       A__											=			MvpTokenizer
       A__											=			MvpTokenizerFast
       A__											=			True
       A__											=			filter_roberta_detectors





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             super().setUp()
             lowerCamelCase__		:				List[str]    =  [
                 "l",
                 "o",
                 "w",
                 "e",
                 "r",
                 "s",
                 "t",
                 "i",
                 "d",
                 "n",
                 "\u0120",
                 "\u0120l",
                 "\u0120n",
                 "\u0120lo",
                 "\u0120low",
                 "er",
                 "\u0120lowest",
                 "\u0120newer",
                 "\u0120wider",
                 "<unk>",
             ]
             lowerCamelCase__		:				Optional[int]    =  dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase       )       )       )       )
             lowerCamelCase__		:				Union[str, Any]    =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
             lowerCamelCase__		:				Any    =  {"unk_token": "<unk>"}

             lowerCamelCase__		:				Tuple    =  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["vocab_file"]       )
             lowerCamelCase__		:				Dict    =  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 						lowerCAmelCase     (   self							:			Union[str, Any]					,      **__lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map       )
             return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map       )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





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


             '''simple docstring'''



             return "lower newer", "lower newer"





       @cached_property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return MvpTokenizer.from_pretrained("RUCAIBox/mvp"       )





       @cached_property
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__		:				Union[str, Any]    =  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				int    =  tokenizer(__lowerCamelCase					,      max_length=len(__lowerCamelCase       )					,      padding=__lowerCamelCase					,      return_tensors="pt"       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   self.assertEqual((2, 9)					,      batch.input_ids.shape       )
                   self.assertEqual((2, 9)					,      batch.attention_mask.shape       )
                   lowerCamelCase__		:				List[Any]    =  batch.input_ids.tolist()[0]
                   self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase       )
                   # Test that special tokens are reset





       @require_torch
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				int    =  tokenizer(__lowerCamelCase					,      padding=__lowerCamelCase					,      return_tensors="pt"       )
                   # check if input_ids are returned and no labels
                   self.assertIn("input_ids"					,      __lowerCamelCase       )
                   self.assertIn("attention_mask"					,      __lowerCamelCase       )
                   self.assertNotIn("labels"					,      __lowerCamelCase       )
                   self.assertNotIn("decoder_attention_mask"					,      __lowerCamelCase       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  [
                 "Summary of the text.",
                 "Another summary.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				List[Any]    =  tokenizer(text_target=__lowerCamelCase					,      max_length=32					,      padding="max_length"					,      return_tensors="pt"       )
                   self.assertEqual(32					,      targets["input_ids"].shape[1]       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				Optional[Any]    =  tokenizer(
                       ["I am a small frog" * 1024, "I am a small frog"]					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      return_tensors="pt"       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )
                   self.assertEqual(batch.input_ids.shape					,      (2, 1024)       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  ["A long paragraph for summarization."]
             lowerCamelCase__		:				Optional[Any]    =  [
                 "Summary of the text.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				Optional[int]    =  tokenizer(__lowerCamelCase					,      text_target=__lowerCamelCase					,      return_tensors="pt"       )
                   lowerCamelCase__		:				Optional[Any]    =  inputs["input_ids"]
                   lowerCamelCase__		:				Optional[Any]    =  inputs["labels"]
                   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
                   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
                   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
                   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                         lowerCamelCase__		:				List[str]    =  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase       )
                         lowerCamelCase__		:				List[Any]    =  self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase       )
                         lowerCamelCase__		:				Union[str, Any]    =  "A, <mask> AllenNLP sentence."
                         lowerCamelCase__		:				Union[str, Any]    =  tokenizer_r.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase       )
                         lowerCamelCase__		:				Optional[Any]    =  tokenizer_p.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase       )

                         # token_type_ids should put 0 everywhere
                         self.assertEqual(sum(tokens_r["token_type_ids"]       )					,      sum(tokens_p["token_type_ids"]       )       )

                         # attention_mask should put 1 everywhere, so sum over length should be 1
                         self.assertEqual(
                             sum(tokens_r["attention_mask"]       ) / len(tokens_r["attention_mask"]       )					,      sum(tokens_p["attention_mask"]       ) / len(tokens_p["attention_mask"]       )					,      )

                         lowerCamelCase__		:				List[Any]    =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
                         lowerCamelCase__		:				int    =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]       )

                         # Rust correctly handles the space before the mask while python doesnt
                         self.assertSequenceEqual(tokens_p["input_ids"]					,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )
                         self.assertSequenceEqual(tokens_r["input_ids"]					,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

                         self.assertSequenceEqual(
                             __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )
                         self.assertSequenceEqual(
                             __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )




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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    ):
		super().__init__()

		if safety_checker is None:
			logger.warning(
			    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
			    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
			    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
			    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
			    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
			    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'       )

		self.register_modules(
		    speech_model=snake_case		,    speech_processor=snake_case		,    vae=snake_case		,    text_encoder=snake_case		,    tokenizer=snake_case		,    unet=snake_case		,    scheduler=snake_case		,    feature_extractor=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case = "auto"       ):
		if slice_size == "auto":
			lowercase       =    self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.enable_attention_slicing(snake_case       )





	@torch.no_grad()
	def __call__(       self		,    snake_case		,    snake_case=1_6000		,    snake_case = 512		,    snake_case = 512		,    snake_case = 50		,    snake_case = 7.5		,    snake_case = None		,    snake_case = 1		,    snake_case = 0.0		,    snake_case = None		,    snake_case = None		,    snake_case = "pil"		,    snake_case = True		,    snake_case = None		,    snake_case = 1		,    **snake_case		,    ):
		lowercase       =    self.speech_processor.feature_extractor(
		    snake_case		,    return_tensors='pt'		,    sampling_rate=snake_case       ).input_features.to(self.device       )
		lowercase       =    self.speech_model.generate(snake_case		,    max_length=48_0000       )

		lowercase       =    self.speech_processor.tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case		,    normalize=snake_case       )[
		    0
		]

		if isinstance(snake_case		,    snake_case       ):
			lowercase       =    1
		elif isinstance(snake_case		,    snake_case       ):
			lowercase       =    len(snake_case       )
		else:
			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case       )}'''       )

		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(snake_case		,    snake_case       ) or callback_steps <= 0)
		):
			raise ValueError(
			    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
			    F''' {type(snake_case       )}.'''       )

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

		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
			lowercase       =    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}'''       )
			lowercase       =    text_input_ids[:, : self.tokenizer.model_max_length]
		lowercase       =    self.text_encoder(text_input_ids.to(self.device       )       )[0]

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		lowercase  ,					lowercase  ,					lowercase       =    text_embeddings.shape
		lowercase       =    text_embeddings.repeat(1		,    snake_case		,    1       )
		lowercase       =    text_embeddings.view(bs_embed * num_images_per_prompt		,    snake_case		,    -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.
		lowercase       =    guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			lowercase       =    42
			if negative_prompt is None:
				lowercase       =    [''] * batch_size
			elif type(snake_case       ) is not type(snake_case       ):
				raise TypeError(
				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case       )} !='''
				    F''' {type(snake_case       )}.'''       )
			elif isinstance(snake_case		,    snake_case       ):
				lowercase       =    [negative_prompt]
			elif batch_size != len(snake_case       ):
				raise ValueError(
				    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case       )}, but `prompt`:'''
				    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
				    ' the batch size of `prompt`.'       )
			else:
				lowercase       =    negative_prompt

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

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			lowercase       =    uncond_embeddings.shape[1]
			lowercase       =    uncond_embeddings.repeat(1		,    snake_case		,    1       )
			lowercase       =    uncond_embeddings.view(batch_size * num_images_per_prompt		,    snake_case		,    -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
			lowercase       =    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`.
		lowercase       =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
		lowercase       =    text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not exist on mps
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device='cpu'		,    dtype=snake_case       ).to(
				    self.device       )
			else:
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device=self.device		,    dtype=snake_case       )
		else:
			if latents.shape != latents_shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
			lowercase       =    latents.to(self.device       )

		# set timesteps
		self.scheduler.set_timesteps(snake_case       )

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

		# scale the initial noise by the standard deviation required by the scheduler
		lowercase       =    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]
		lowercase       =    'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
		lowercase       =    {}
		if accepts_eta:
			lowercase       =    eta

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

			# predict the noise residual
			lowercase       =    self.unet(snake_case		,    snake_case		,    encoder_hidden_states=snake_case       ).sample

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

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

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

		lowercase       =    1 / 0.18_215 * latents
		lowercase       =    self.vae.decode(snake_case       ).sample

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

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

		if output_type == "pil":
			lowercase       =    self.numpy_to_pil(snake_case       )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case		,    nsfw_content_detected=snake_case       )

