
"""simple docstring"""


import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase							   =				get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    XLMProphetNetTokenizer
						_lowerCamelCase:    Union[str, Any]      =    False
						_lowerCamelCase:    List[str]      =    True


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							A						   =   XLMProphetNetTokenizer(A_  ,keep_accents=A_      )
							tokenizer.save_pretrained(self.tmpdirname      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Union[str, Any]:
							A						   =   '[PAD]'
							A						   =   0

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[Any]:
							A						   =   list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]  ,'[PAD]'      )
							self.assertEqual(vocab_keys[1]  ,'[CLS]'      )
							self.assertEqual(vocab_keys[-1]  ,'j'      )
							self.assertEqual(len(A_      )  ,1012      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   List[str]:
							self.assertEqual(self.get_tokenizer().vocab_size  ,1012      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   XLMProphetNetTokenizer(A_  ,keep_accents=A_      )

							A						   =   tokenizer.tokenize('This is a test'      )
							self.assertListEqual(A_  ,['▁This', '▁is', '▁a', '▁t', 'est']      )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(A_      )  ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,)

							A						   =   tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
							self.assertListEqual(
							    A_  ,[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ]  ,)
							A						   =   tokenizer.convert_tokens_to_ids(A_      )
							self.assertListEqual(
							    A_  ,[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
							    ]  ,)

							A						   =   tokenizer.convert_ids_to_tokens(A_      )
							self.assertListEqual(
							    A_  ,[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '[UNK]',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '[UNK]',
							        '.',
							    ]  ,)


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
							return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   int:
							A						   =   'Hello World!'
							A						   =   [3_5389, 6672, 49, 2]
							self.assertListEqual(A_  ,self.big_tokenizer.encode(A_      )      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							# fmt: off
							A						   =   {'input_ids': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=A_  ,model_name='microsoft/xprophetnet-large-wiki100-cased'  ,revision='1acad1643ddd54a44df6a1b797ada8373685d90e'  ,)

"""simple docstring"""


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_lowercase							   =				8






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Optional[int]=BITS     ):
	A						   =   x.device

	A						   =   (x * 255).int().clamp(0	,      255     )

	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__     )
	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b c h w -> b c 1 h w'     )

	A						   =   ((x & mask) != 0).float()
	A						   =   rearrange(snake_case__	,      'b c d h w -> b (c d) h w'     )
	A						   =   bits * 2 - 1
	return bits






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Any=BITS     ):
	A						   =   x.device

	A						   =   (x > 0).int()
	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__	,      dtype=torch.intaa     )

	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b (c d) h w -> b c d h w'	,      d=8     )
	A						   =   reduce(x * mask	,      'b c d h w -> b c h w'	,      'sum'     )
	return (dec / 255).clamp(0.0	,      1.0     )






def       _snake_case				(		self   :     Optional[int]	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     float = 0.0	,      snake_case__   :     bool = True	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	if self.num_inference_steps is None:
		raise ValueError(
		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	A						   =   timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	A						   =   self.alphas_cumprod[timestep]
	A						   =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	A						   =   1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	A						   =   self._get_variance(snake_case__	,      snake_case__     )
	A						   =   eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		A						   =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		A						   =   model_output.device if torch.is_tensor(snake_case__     ) else 'cpu'
		A						   =   torch.randn(model_output.shape	,      dtype=model_output.dtype	,      generator=snake_case__     ).to(snake_case__     )
		A						   =   self._get_variance(snake_case__	,      snake_case__     ) ** 0.5 * eta * noise

		A						   =   prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )






def       _snake_case				(		self   :     Dict	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     Tuple="epsilon"	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	A						   =   timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		A   ,      A						   =   torch.split(snake_case__	,      sample.shape[1]	,      dim=1     )
	else:
		A						   =   None

	# 1. compute alphas, betas
	A						   =   self.alphas_cumprod[t]
	A						   =   self.alphas_cumprod[t - 1] if t > 0 else self.one
	A						   =   1 - alpha_prod_t
	A						   =   1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		A						   =   model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'     )

	# 3. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	A						   =   self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	A						   =   0
	if t > 0:
		A						   =   torch.randn(
		    model_output.size()	,      dtype=model_output.dtype	,      layout=model_output.layout	,      generator=snake_case__     ).to(model_output.device     )
		A						   =   (self._get_variance(snake_case__	,      predicted_variance=snake_case__     ) ** 0.5) * noise

	A						   =   pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      UNetaDConditionModel  ,A_     :      Union[DDIMScheduler, DDPMScheduler]  ,A_     :      Optional[float] = 1.0  ,)     ->   Optional[int]:
							super().__init__()
							A						   =   bit_scale
							A						   =   (
							    ddim_bit_scheduler_step if isinstance(A_  ,A_      ) else ddpm_bit_scheduler_step
							)

							self.register_modules(unet=A_  ,scheduler=A_      )




						@torch.no_grad()
						def __call__( self     :      Tuple  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 50  ,A_     :      Optional[torch.Generator] = None  ,A_     :      Optional[int] = 1  ,A_     :      Optional[str] = "pil"  ,A_     :      bool = True  ,**A_     :      Optional[Any]  ,)     ->   Union[Tuple, ImagePipelineOutput]:
							A						   =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)  ,generator=A_  ,)
							A						   =   decimal_to_bits(A_      ) * self.bit_scale
							A						   =   latents.to(self.device      )

							self.scheduler.set_timesteps(A_      )

							for t in self.progress_bar(self.scheduler.timesteps      ):
								# predict the noise residual
								A						   =   self.unet(A_  ,A_      ).sample

								# compute the previous noisy sample x_t -> x_t-1
								A						   =   self.scheduler.step(A_  ,A_  ,A_      ).prev_sample

							A						   =   bits_to_decimal(A_      )

							if output_type == "pil":
								A						   =   self.numpy_to_pil(A_      )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_      )