


import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		int        =	GPTSanJapaneseTokenizer
	_lowerCAmelCase    :		Tuple        =	False
	_lowerCAmelCase    :		Tuple        =	{'''do_clean_text''': False, '''add_prefix_space''': False}


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

							# fmt: off
							__UpperCAmelCase :       Union[str, Any]  						=  ['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
							# fmt: on
							__UpperCAmelCase :       str  						=  {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
							__UpperCAmelCase :       Union[str, Any]  						=  {'''unk_token''': '''<unk>'''}

							__UpperCAmelCase :       str  						=  os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['''vocab_file'''])
							__UpperCAmelCase :       Any  						=  os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES['''emoji_file'''])
							with open(self.vocab_file							,    '''w'''							,    encoding='''utf-8''') as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))
							with open(self.emoji_file							,    '''w''') as emoji_writer:
													emoji_writer.write(json.dumps(lowercase__))


	def    A(						self							,    **lowercase__):
							kwargs.update(self.special_tokens_map)
							return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname							,    **lowercase__)


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       Optional[Any]  						=  '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
							__UpperCAmelCase :       Tuple  						=  '''こんにちは、世界。 \nこんばんは、世界。😀'''
							return input_text, output_text


	def    A(						self							,    lowercase__):
							__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  self.get_input_output_texts(lowercase__)
							__UpperCAmelCase :       Tuple  						=  tokenizer.encode(lowercase__							,    add_special_tokens=lowercase__)
							__UpperCAmelCase :       Dict  						=  tokenizer.decode(lowercase__							,    clean_up_tokenization_spaces=lowercase__)
							return text, ids


	def    A(						self):
							pass  # TODO add if relevant


	def    A(						self):
							pass  # TODO add if relevant


	def    A(						self):
							pass  # TODO add if relevant


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.get_tokenizer()

							# Testing tokenization
							__UpperCAmelCase :       Any  						=  '''こんにちは、世界。　こんばんは、㔺界。'''
							__UpperCAmelCase :       List[Any]  						=  ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
							__UpperCAmelCase :       List[Any]  						=  tokenizer.tokenize(lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)

							# Testing conversion to ids without special tokens
							__UpperCAmelCase :       List[Any]  						=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							__UpperCAmelCase :       Optional[Any]  						=  tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)

							# Testing conversion to ids with special tokens
							__UpperCAmelCase :       Optional[Any]  						=  tokens + [tokenizer.unk_token]
							__UpperCAmelCase :       Optional[int]  						=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
							__UpperCAmelCase :       Dict  						=  tokenizer.convert_tokens_to_ids(lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       str  						=  self.get_tokenizer()

							# Testing tokenization
							__UpperCAmelCase :       List[str]  						=  '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
							__UpperCAmelCase :       Optional[Any]  						=  '''こんにちは、、、、世界。こんばんは、、、、世界。'''
							__UpperCAmelCase :       Any  						=  tokenizer.encode(lowercase__)
							__UpperCAmelCase :       List[Any]  						=  tokenizer.decode(lowercase__)
							self.assertEqual(lowercase__							,    lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

							# Testing tokenization
							__UpperCAmelCase :       Dict  						=  '''こんにちは、世界。'''
							__UpperCAmelCase :       List[str]  						=  '''こんばんは、㔺界。😀'''
							__UpperCAmelCase :       Union[str, Any]  						=  '''こんにちは、世界。こんばんは、世界。😀'''
							__UpperCAmelCase :       Optional[Any]  						=  tokenizer.encode(prefix_text + input_text)
							__UpperCAmelCase :       int  						=  tokenizer.encode(''''''							,    prefix_text=prefix_text + input_text)
							__UpperCAmelCase :       List[Any]  						=  tokenizer.encode(lowercase__							,    prefix_text=lowercase__)
							__UpperCAmelCase :       Any  						=  tokenizer.decode(lowercase__)
							__UpperCAmelCase :       Tuple  						=  tokenizer.decode(lowercase__)
							__UpperCAmelCase :       Optional[Any]  						=  tokenizer.decode(lowercase__)
							self.assertEqual(lowercase__							,    lowercase__)
							self.assertEqual(lowercase__							,    lowercase__)
							self.assertEqual(lowercase__							,    lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

							# Testing tokenization
							__UpperCAmelCase :       Tuple  						=  '''こんにちは、世界。'''
							__UpperCAmelCase :       List[Any]  						=  '''こんばんは、㔺界。😀'''

							__UpperCAmelCase :       Union[str, Any]  						=  len(tokenizer.encode(lowercase__)) - 2
							__UpperCAmelCase :       Tuple  						=  len(tokenizer.encode(lowercase__)) - 2

							__UpperCAmelCase :       int  						=  [1] + [0] * (len_prefix + len_text + 1)
							__UpperCAmelCase :       Optional[Any]  						=  [1] * (len_prefix + len_text + 1) + [0]
							__UpperCAmelCase :       Dict  						=  [1] + [1] * (len_prefix) + [0] * (len_text + 1)

							__UpperCAmelCase :       List[Any]  						=  tokenizer(prefix_text + input_text).token_type_ids
							__UpperCAmelCase :       int  						=  tokenizer(''''''							,    prefix_text=prefix_text + input_text).token_type_ids
							__UpperCAmelCase :       int  						=  tokenizer(lowercase__							,    prefix_text=lowercase__).token_type_ids
							self.assertListEqual(lowercase__							,    lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)


	@slow
	def    A(						self):
							__UpperCAmelCase :       str  						=  self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

							__UpperCAmelCase :       Union[str, Any]  						=  tokenizer.encode('''あンいワ''')
							__UpperCAmelCase :       str  						=  tokenizer.encode(''''''							,    prefix_text='''あンいワ''')
							__UpperCAmelCase :       Optional[Any]  						=  tokenizer.encode('''いワ'''							,    prefix_text='''あン''')

							self.assertEqual(tokenizer.decode(lowercase__)							,    tokenizer.decode(lowercase__))
							self.assertEqual(tokenizer.decode(lowercase__)							,    tokenizer.decode(lowercase__))
							self.assertNotEqual(lowercase__							,    lowercase__)
							self.assertNotEqual(lowercase__							,    lowercase__)
							self.assertEqual(x_token_a[1]							,    x_token_a[-1])  # SEG token
							self.assertEqual(x_token_a[1]							,    x_token_a[3])  # SEG token


	@slow
	def    A(						self):
							__UpperCAmelCase :       Optional[int]  						=  self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

							__UpperCAmelCase :       List[str]  						=  [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
							__UpperCAmelCase :       List[Any]  						=  tokenizer(lowercase__							,    padding=lowercase__)
							__UpperCAmelCase :       Tuple  						=  tokenizer.batch_encode_plus(lowercase__							,    padding=lowercase__)

							# fmt: off
							__UpperCAmelCase :       str  						=  [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
							__UpperCAmelCase :       List[str]  						=  [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
							__UpperCAmelCase :       int  						=  [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
							# fmt: on
							self.assertListEqual(x_token.input_ids							,    lowercase__)
							self.assertListEqual(x_token.token_type_ids							,    lowercase__)
							self.assertListEqual(x_token.attention_mask							,    lowercase__)
							self.assertListEqual(x_token_a.input_ids							,    lowercase__)
							self.assertListEqual(x_token_a.token_type_ids							,    lowercase__)
							self.assertListEqual(x_token_a.attention_mask							,    lowercase__)


	def    A(						self):
							# Intentionally convert some words to accommodate character fluctuations unique to Japanese
							pass


	def    A(						self):
							# tokenizer has no padding token
							pass





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,
)


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

lowerCAmelCase							=						"""
    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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=8			)		->    List[Any]:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__UpperCAmelCase :       Union[str, Any]  						=  width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





class        lowerCamelCase      (     _UpperCamelCase   ):


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

							self.register_modules(
							    unet=lowercase__							,    scheduler=lowercase__							,    movq=lowercase__							,    )
							__UpperCAmelCase :       Any  						=  2 ** (len(self.movq.config.block_out_channels) - 1)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							if latents is None:
													__UpperCAmelCase :       Any  						=  randn_tensor(lowercase__							,    generator=lowercase__							,    device=lowercase__							,    dtype=lowercase__)
							else:
													if latents.shape != shape:
																			raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
													__UpperCAmelCase :       Union[str, Any]  						=  latents.to(lowercase__)

							__UpperCAmelCase :       Union[str, Any]  						=  latents * scheduler.init_noise_sigma
							return latents


	def    A(						self							,    lowercase__=0):
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('''Please install accelerate via `pip install accelerate`''')

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

							__UpperCAmelCase :       List[Any]  						=  [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(lowercase__							,    lowercase__)


	def    A(						self							,    lowercase__=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.''')

							__UpperCAmelCase :       Optional[Any]  						=  torch.device(F"cuda:{gpu_id}")

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

							__UpperCAmelCase :       List[Any]  						=  None
							for cpu_offloaded_model in [self.unet, self.movq]:
													__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  cpu_offload_with_hook(lowercase__							,    lowercase__							,    prev_module_hook=lowercase__)

							# We'll offload the last model manually.
							__UpperCAmelCase :       Any  						=  hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    A(						self):
							if not hasattr(self.unet							,    '''_hf_hook'''):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(lowercase__							,    '''_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(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = 5_1_2							,    lowercase__ = 5_1_2							,    lowercase__ = 1_0_0							,    lowercase__ = 4.0							,    lowercase__ = 1							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       str  						=  self._execution_device

							__UpperCAmelCase :       List[str]  						=  guidance_scale > 1.0

							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Tuple  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Any  						=  torch.cat(lowercase__							,    dim=0)

							__UpperCAmelCase :       Union[str, Any]  						=  image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
													__UpperCAmelCase :       Optional[int]  						=  image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       Dict  						=  negative_image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       List[Any]  						=  hint.repeat_interleave(lowercase__							,    dim=0)

							__UpperCAmelCase :       Tuple  						=  torch.cat([negative_image_embeds, image_embeds]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  torch.cat([hint, hint]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							self.scheduler.set_timesteps(lowercase__							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  self.scheduler.timesteps

							__UpperCAmelCase :       Any  						=  self.movq.config.latent_channels

							__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  downscale_height_and_width(lowercase__							,    lowercase__							,    self.movq_scale_factor)

							# create initial latent
							__UpperCAmelCase :       Union[str, Any]  						=  self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)							,    image_embeds.dtype							,    lowercase__							,    lowercase__							,    lowercase__							,    self.scheduler							,    )

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

													__UpperCAmelCase :       Union[str, Any]  						=  {'''image_embeds''': image_embeds, '''hint''': hint}
													__UpperCAmelCase :       Any  						=  self.unet(
													    sample=lowercase__							,    timestep=lowercase__							,    encoder_hidden_states=lowercase__							,    added_cond_kwargs=lowercase__							,    return_dict=lowercase__							,    )[0]

													if do_classifier_free_guidance:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred.split(latents.shape[1]							,    dim=1)
																			__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  noise_pred.chunk(2)
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  variance_pred.chunk(2)
																			__UpperCAmelCase :       Union[str, Any]  						=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__UpperCAmelCase :       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"]
													):
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred.split(latents.shape[1]							,    dim=1)

													# compute the previous noisy sample x_t -> x_t-1
													__UpperCAmelCase :       Tuple  						=  self.scheduler.step(
													    lowercase__							,    lowercase__							,    lowercase__							,    generator=lowercase__							,    )[0]
							# post-processing
							__UpperCAmelCase :       str  						=  self.movq.decode(lowercase__							,    force_not_quantize=lowercase__)['''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"]:
													__UpperCAmelCase :       Dict  						=  image * 0.5 + 0.5
													__UpperCAmelCase :       Union[str, Any]  						=  image.clamp(0							,    1)
													__UpperCAmelCase :       List[str]  						=  image.cpu().permute(0							,    2							,    3							,    1).float().numpy()

							if output_type == "pil":
													__UpperCAmelCase :       List[str]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowercase__)

