



"""simple docstring"""
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

SCREAMING_SNAKE_CASE_				= """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=8	)			->      List[str]:
		a_     :      Optional[Any] 						=     height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		a_     :      List[str] 						=     width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class 			snake_case_						(						a_				):






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

									self.register_modules(
									    unet=a_			,     scheduler=a_			,     movq=a_			,     )
									a_     :      Optional[int] 						=     2 ** (len(self.movq.config.block_out_channels      ) - 1)






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									if latents is None:
											a_     :      Optional[int] 						=     randn_tensor(a_			,     generator=a_			,     device=a_			,     dtype=a_      )
									else:
											if latents.shape != shape:
													raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
											a_     :      Optional[int] 						=     latents.to(a_      )

									a_     :      List[Any] 						=     latents * scheduler.init_noise_sigma
									return latents






							def snake_case_  (   self			,     a_=0      ):
									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError("Please install accelerate via `pip install accelerate`"      )

									a_     :      str 						=     torch.device(F"""cuda:{gpu_id}"""      )

									a_     :      Dict 						=     [
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
													cpu_offload(a_			,     a_      )






							def snake_case_  (   self			,     a_=0      ):
									if is_accelerate_available() and is_accelerate_version(">="			,     "0.17.0.dev0"      ):
											from accelerate import cpu_offload_with_hook
									else:
											raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."      )

									a_     :      List[str] 						=     torch.device(F"""cuda:{gpu_id}"""      )

									if self.device.type != "cpu":
											self.to("cpu"			,     silence_dtype_warnings=a_      )
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									a_     :      Tuple 						=     None
									for cpu_offloaded_model in [self.unet, self.movq]:
											a_						,				a_     :      Optional[Any] 						=     cpu_offload_with_hook(a_			,     a_			,     prev_module_hook=a_      )

									# We'll offload the last model manually.
									a_     :      List[str] 						=     hook






							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def snake_case_  (   self      ):
									if not hasattr(self.unet			,     "_hf_hook"      ):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(a_			,     "_hf_hook"      )
											    and hasattr(module._hf_hook			,     "execution_device"      )
											    and module._hf_hook.execution_device is not None
											):
													return torch.device(module._hf_hook.execution_device      )
									return self.device






							@torch.no_grad()
							@replace_example_docstring(a_      )
							def __call__(   self			,     a_			,     a_			,     a_			,     a_ = 5_1_2			,     a_ = 5_1_2			,     a_ = 1_0_0			,     a_ = 4.0			,     a_ = 1			,     a_ = None			,     a_ = None			,     a_ = "pil"			,     a_ = True			,     ):
									a_     :      Dict 						=     self._execution_device

									a_     :      List[str] 						=     guidance_scale > 1.0

									if isinstance(a_			,     a_      ):
											a_     :      Optional[int] 						=     torch.cat(a_			,     dim=0      )
									if isinstance(a_			,     a_      ):
											a_     :      Optional[Any] 						=     torch.cat(a_			,     dim=0      )
									if isinstance(a_			,     a_      ):
											a_     :      str 						=     torch.cat(a_			,     dim=0      )

									a_     :      str 						=     image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
											a_     :      Dict 						=     image_embeds.repeat_interleave(a_			,     dim=0      )
											a_     :      Dict 						=     negative_image_embeds.repeat_interleave(a_			,     dim=0      )
											a_     :      Dict 						=     hint.repeat_interleave(a_			,     dim=0      )

									a_     :      List[str] 						=     torch.cat([negative_image_embeds, image_embeds]			,     dim=0      ).to(dtype=self.unet.dtype			,     device=a_      )
									a_     :      Dict 						=     torch.cat([hint, hint]			,     dim=0      ).to(dtype=self.unet.dtype			,     device=a_      )
									self.scheduler.set_timesteps(a_			,     device=a_      )
									a_     :      List[str] 						=     self.scheduler.timesteps

									a_     :      Any 						=     self.movq.config.latent_channels

									a_						,				a_     :      Tuple 						=     downscale_height_and_width(a_			,     a_			,     self.movq_scale_factor      )

									# create initial latent
									a_     :      str 						=     self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)			,     image_embeds.dtype			,     a_			,     a_			,     a_			,     self.scheduler			,     )

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

											a_     :      Any 						=     {"image_embeds": image_embeds, "hint": hint}
											a_     :      Dict 						=     self.unet(
											    sample=a_			,     timestep=a_			,     encoder_hidden_states=a_			,     added_cond_kwargs=a_			,     return_dict=a_			,     )[0]

											if do_classifier_free_guidance:
													a_						,				a_     :      Union[str, Any] 						=     noise_pred.split(latents.shape[1]			,     dim=1      )
													a_						,				a_     :      Any 						=     noise_pred.chunk(2      )
													a_						,				a_     :      Tuple 						=     variance_pred.chunk(2      )
													a_     :      str 						=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													a_     :      int 						=     torch.cat([noise_pred, variance_pred_text]			,     dim=1      )

											if not (
											    hasattr(self.scheduler.config			,     "variance_type"      )
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
													a_						,				a_     :      List[str] 						=     noise_pred.split(latents.shape[1]			,     dim=1      )

											# compute the previous noisy sample x_t -> x_t-1
											a_     :      Optional[int] 						=     self.scheduler.step(
											    a_			,     a_			,     a_			,     generator=a_			,     )[0]
									# post-processing
									a_     :      List[Any] 						=     self.movq.decode(a_			,     force_not_quantize=a_      )["sample"]

									if output_type not in ["pt", "np", "pil"]:
											raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""      )

									if output_type in ["np", "pil"]:
											a_     :      List[str] 						=     image * 0.5 + 0.5
											a_     :      List[str] 						=     image.clamp(0			,     1      )
											a_     :      List[str] 						=     image.cpu().permute(0			,     2			,     3			,     1      ).float().numpy()

									if output_type == "pil":
											a_     :      int 						=     self.numpy_to_pil(a_      )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=a_      )




"""simple docstring"""
import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					SpeechTaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Any 						=     SpeechTaTokenizer(a_      )

									a_     :      Optional[int] 						=     AddedToken("<mask>"			,     lstrip=a_			,     rstrip=a_      )
									a_     :      Any 						=     mask_token
									tokenizer.add_special_tokens({"mask_token": mask_token}      )
									tokenizer.add_tokens(["<ctc_blank>"]      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "this is a test"
									a_     :      Any 						=     "this is a test"
									return input_text, output_text






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=2_0			,     a_=5      ):
									a_						,				a_     :      Optional[Any] 						=     self.get_input_output_texts(a_      )
									a_     :      Optional[Any] 						=     tokenizer.encode(a_			,     add_special_tokens=a_      )
									a_     :      Dict 						=     tokenizer.decode(a_			,     clean_up_tokenization_spaces=a_      )
									return text, ids






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     "<pad>"
									a_     :      Optional[int] 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<s>"      )
									self.assertEqual(vocab_keys[1]			,     "<pad>"      )
									self.assertEqual(vocab_keys[-4]			,     "œ"      )
									self.assertEqual(vocab_keys[-2]			,     "<mask>"      )
									self.assertEqual(vocab_keys[-1]			,     "<ctc_blank>"      )
									self.assertEqual(len(a_      )			,     8_1      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     7_9      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizers(do_lower_case=a_      )
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
													a_     :      Dict 						=     tokenizer.vocab_size
													a_     :      List[str] 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													a_     :      Optional[int] 						=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
													a_     :      int 						=     tokenizer.add_tokens(a_      )
													a_     :      List[Any] 						=     tokenizer.vocab_size
													a_     :      Tuple 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size + len(a_      )      )

													a_     :      str 						=     tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     4      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )

													a_     :      Tuple 						=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
													a_     :      Dict 						=     tokenizer.add_special_tokens(a_      )
													a_     :      Optional[Any] 						=     tokenizer.vocab_size
													a_     :      Any 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size_a + len(a_      )      )

													a_     :      Any 						=     tokenizer.encode(
													    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     6      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[0]			,     tokens[1]      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokens[-4]      )
													self.assertEqual(tokens[0]			,     tokenizer.eos_token_id      )
													self.assertEqual(tokens[-3]			,     tokenizer.pad_token_id      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     tokenizer.tokenize("This is a test"      )
									# fmt: off
									self.assertListEqual(a_			,     [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]      )
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(a_      )			,     [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]			,     )

									a_     :      Optional[int] 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )

									a_     :      Tuple 						=     tokenizer.convert_tokens_to_ids(a_      )
									# fmt: off
									self.assertListEqual(a_			,     [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]      )
									# fmt: on

									a_     :      Tuple 						=     tokenizer.convert_ids_to_tokens(a_      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )






							@slow
							def snake_case_  (   self      ):
									# Use custom sequence because this tokenizer does not handle numbers.
									a_     :      List[Any] 						=     [
									    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
									    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
									    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
									    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
									    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
									    "conditioning on both left and right context in all layers.",
									    "The quick brown fox jumps over the lazy dog.",
									]

									# fmt: off
									a_     :      Tuple 						=     {
									    "input_ids": [
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									        [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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									    ],
									    "attention_mask": [
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									    ]
									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="microsoft/speecht5_asr"			,     revision="c5ef64c71905caeccde0e4462ef3f9077224c524"			,     sequences=a_			,     )