'''simple docstring'''




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 				snake_case__							(			_A:					Tuple      ,   _A:					Dict=0.999      ,   _A:					str="cosine"      ,   )    ->	int:

		'''simple docstring'''


		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_A:					Union[str, Any] ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_A:					List[str] ):
						return math.exp(t * -12.0 )

		else:
				raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )

		lowerCAmelCase   					=  []
		for i in range(_A ):
				lowerCAmelCase   					=  i / num_diffusion_timesteps
				lowerCAmelCase   					=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_A ) / alpha_bar_fn(_A )      ,   _A ) )
		return torch.tensor(_A      ,   dtype=torch.floataa )






class 				a__(					lowerCAmelCase__ ,						lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       [e.name for e in KarrasDiffusionSchedulers]
			UpperCAmelCase_			:     Optional[Any]      =       2



			@register_to_config
			def __init__(   self       ,    __lowerCAmelCase = 1000       ,    __lowerCAmelCase = 0.00085       ,    __lowerCAmelCase = 0.012       ,    __lowerCAmelCase = "linear"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "epsilon"       ,    __lowerCAmelCase = "linspace"       ,    __lowerCAmelCase = 0       ,    ):




					"""simple docstring"""


					if trained_betas is not None:
							lowerCAmelCase   					=  torch.tensor(__lowerCAmelCase       ,    dtype=torch.floataa)
					elif beta_schedule == "linear":
							lowerCAmelCase   					=  torch.linspace(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    dtype=torch.floataa)
					elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase   					=  (
							    torch.linspace(beta_start**0.5       ,    beta_end**0.5       ,    __lowerCAmelCase       ,    dtype=torch.floataa) ** 2
							)
					elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase   					=  betas_for_alpha_bar(__lowerCAmelCase)
					else:
							raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

					lowerCAmelCase   					=  1.0 - self.betas
					lowerCAmelCase   					=  torch.cumprod(self.alphas       ,    dim=0)

					#  set all values
					self.set_timesteps(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=None):




					"""simple docstring"""


					if schedule_timesteps is None:
							lowerCAmelCase   					=  self.timesteps

					lowerCAmelCase   					=  (schedule_timesteps == timestep).nonzero()

					# The sigma index that is taken for the **very** first `step`
					# is always the second index (or the last index if there is only 1)
					# This way we can ensure we don't accidentally skip a sigma in
					# case we start in the middle of the denoising schedule (e.g. for image-to-image)
					if len(self._index_counter) == 0:
							lowerCAmelCase   					=  1 if len(__lowerCAmelCase) > 1 else 0
					else:
							lowerCAmelCase   					=  timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase) else timestep
							lowerCAmelCase   					=  self._index_counter[timestep_int]

					return indices[pos].item()



			@property
			def 							a_       (   self):




					"""simple docstring"""


					if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

					return (self.sigmas.max() ** 2 + 1) ** 0.5



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  self.index_for_timestep(__lowerCAmelCase)

					if self.state_in_first_order:
							lowerCAmelCase   					=  self.sigmas[step_index]
					else:
							lowerCAmelCase   					=  self.sigmas_interpol[step_index]

					lowerCAmelCase   					=  sample / ((sigma**2 + 1) ** 0.5)
					return sample



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  num_inference_steps

					lowerCAmelCase   					=  num_train_timesteps or self.config.num_train_timesteps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
							lowerCAmelCase   					=  np.linspace(0       ,    num_train_timesteps - 1       ,    __lowerCAmelCase       ,    dtype=__lowerCAmelCase)[::-1].copy()
					elif self.config.timestep_spacing == "leading":
							lowerCAmelCase   					=  num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase   					=  (np.arange(0       ,    __lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase)
							timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
							lowerCAmelCase   					=  num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase   					=  (np.arange(__lowerCAmelCase       ,    0       ,    -step_ratio)).round().copy().astype(__lowerCAmelCase)
							timesteps -= 1
					else:
							raise ValueError(
							    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")

					lowerCAmelCase   					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
					lowerCAmelCase   					=  torch.from_numpy(np.log(__lowerCAmelCase)).to(__lowerCAmelCase)

					lowerCAmelCase   					=  np.interp(__lowerCAmelCase       ,    np.arange(0       ,    len(__lowerCAmelCase))       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
					lowerCAmelCase   					=  torch.from_numpy(__lowerCAmelCase).to(device=__lowerCAmelCase)

					# interpolate sigmas
					lowerCAmelCase   					=  sigmas.log().lerp(sigmas.roll(1).log()       ,    0.5).exp()

					lowerCAmelCase   					=  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
					lowerCAmelCase   					=  torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

					if str(__lowerCAmelCase).startswith("""mps"""):
							# mps does not support float64
							lowerCAmelCase   					=  torch.from_numpy(__lowerCAmelCase).to(__lowerCAmelCase       ,    dtype=torch.floataa)
					else:
							lowerCAmelCase   					=  torch.from_numpy(__lowerCAmelCase).to(__lowerCAmelCase)

					# interpolate timesteps
					lowerCAmelCase   					=  self.sigma_to_t(__lowerCAmelCase).to(__lowerCAmelCase       ,    dtype=timesteps.dtype)
					lowerCAmelCase   					=  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])       ,    dim=-1).flatten()

					lowerCAmelCase   					=  torch.cat([timesteps[:1], interleaved_timesteps])

					lowerCAmelCase   					=  None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					lowerCAmelCase   					=  defaultdict(__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  sigma.log()

					# get distribution
					lowerCAmelCase   					=  log_sigma - self.log_sigmas[:, None]

					# get sigmas range
					lowerCAmelCase   					=  dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
					lowerCAmelCase   					=  low_idx + 1

					lowerCAmelCase   					=  self.log_sigmas[low_idx]
					lowerCAmelCase   					=  self.log_sigmas[high_idx]

					# interpolate sigmas
					lowerCAmelCase   					=  (low - log_sigma) / (low - high)
					lowerCAmelCase   					=  w.clamp(0       ,    1)

					# transform interpolation to time range
					lowerCAmelCase   					=  (1 - w) * low_idx + w * high_idx
					lowerCAmelCase   					=  t.view(sigma.shape)
					return t



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.sample is None



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  self.index_for_timestep(__lowerCAmelCase)

					# advance index counter by 1
					lowerCAmelCase   					=  timestep.cpu().item() if torch.is_tensor(__lowerCAmelCase) else timestep
					self._index_counter[timestep_int] += 1

					if self.state_in_first_order:
							lowerCAmelCase   					=  self.sigmas[step_index]
							lowerCAmelCase   					=  self.sigmas_interpol[step_index + 1]
							lowerCAmelCase   					=  self.sigmas[step_index + 1]
					else:
							# 2nd order / KDPM2's method
							lowerCAmelCase   					=  self.sigmas[step_index - 1]
							lowerCAmelCase   					=  self.sigmas_interpol[step_index]
							lowerCAmelCase   					=  self.sigmas[step_index]

					# currently only gamma=0 is supported. This usually works best anyways.
					# We can support gamma in the future but then need to scale the timestep before
					# passing it to the model which requires a change in API
					lowerCAmelCase   					=  0
					lowerCAmelCase   					=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
							lowerCAmelCase   					=  sigma_hat if self.state_in_first_order else sigma_interpol
							lowerCAmelCase   					=  sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
							lowerCAmelCase   					=  sigma_hat if self.state_in_first_order else sigma_interpol
							lowerCAmelCase   					=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
							    sample / (sigma_input**2 + 1)
							)
					elif self.config.prediction_type == "sample":
							raise NotImplementedError("""prediction_type not implemented yet: sample""")
					else:
							raise ValueError(
							    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`")

					if self.state_in_first_order:
							# 2. Convert to an ODE derivative for 1st order
							lowerCAmelCase   					=  (sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							lowerCAmelCase   					=  sigma_interpol - sigma_hat

							# store for 2nd order step
							lowerCAmelCase   					=  sample
					else:
							# DPM-Solver-2
							# 2. Convert to an ODE derivative for 2nd order
							lowerCAmelCase   					=  (sample - pred_original_sample) / sigma_interpol

							# 3. delta timestep
							lowerCAmelCase   					=  sigma_next - sigma_hat

							lowerCAmelCase   					=  self.sample
							lowerCAmelCase   					=  None

					lowerCAmelCase   					=  sample + derivative * dt

					if not return_dict:
							return (prev_sample,)

					return SchedulerOutput(prev_sample=__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  self.sigmas.to(device=original_samples.device       ,    dtype=original_samples.dtype)
					if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase):
							# mps does not support float64
							lowerCAmelCase   					=  self.timesteps.to(original_samples.device       ,    dtype=torch.floataa)
							lowerCAmelCase   					=  timesteps.to(original_samples.device       ,    dtype=torch.floataa)
					else:
							lowerCAmelCase   					=  self.timesteps.to(original_samples.device)
							lowerCAmelCase   					=  timesteps.to(original_samples.device)

					lowerCAmelCase   					=  [self.index_for_timestep(__lowerCAmelCase       ,    __lowerCAmelCase) for t in timesteps]

					lowerCAmelCase   					=  sigmas[step_indices].flatten()
					while len(sigma.shape) < len(original_samples.shape):
							lowerCAmelCase   					=  sigma.unsqueeze(-1)

					lowerCAmelCase   					=  original_samples + noise * sigma
					return noisy_samples







			def __len__(   self):




					"""simple docstring"""


					return self.config.num_train_timesteps





'''simple docstring'''




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 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


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

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  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 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  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   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


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



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  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   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  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>"""])




