



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 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							Optional[Any]=0.999			,							UpperCamelCase_					:							Any="cosine"			,							)->	Tuple:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase_					:							int						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase_					:							Any						):
            return math.exp(t * -12.0						)

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

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



class      _UpperCAmelCase      (							A__      ,   A__  ):
     UpperCamelCase__		     =					[e.name for e in KarrasDiffusionSchedulers]
     UpperCamelCase__		     =					2




     @register_to_config
     def __init__(					self			,    a__ = 1_0_0_0			,    a__ = 0.0_0_0_8_5			,    a__ = 0.0_1_2			,    a__ = "linear"			,    a__ = None			,    a__ = "epsilon"			,    a__ = "linspace"			,    a__ = 0			,    ):
         if trained_betas is not None:
             A__					=       torch.tensor(a__			,    dtype=torch.floataa)
         elif beta_schedule == "linear":
             A__					=       torch.linspace(a__			,    a__			,    a__			,    dtype=torch.floataa)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             A__					=       (
                 torch.linspace(beta_start**0.5			,    beta_end**0.5			,    a__			,    dtype=torch.floataa) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             A__					=       betas_for_alpha_bar(a__)
         else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")

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

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




     def 		snake_case_		(					self			,    a__			,    a__=None):
         if schedule_timesteps is None:
             A__					=       self.timesteps

         A__					=       (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:
             A__					=       1 if len(a__) > 1 else 0
         else:
             A__					=       timestep.cpu().item() if torch.is_tensor(a__) else timestep
             A__					=       self._index_counter[timestep_int]

         return indices[pos].item()




     @property
     def 		snake_case_		(					self):
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

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




     def 		snake_case_		(					self			,    a__			,    a__			,    ):
         A__					=       self.index_for_timestep(a__)

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

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




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None			,    ):
         A__					=       num_inference_steps

         A__					=       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":
             A__					=       np.linspace(0			,    num_train_timesteps - 1			,    a__			,    dtype=a__)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             A__					=       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
             A__					=       (np.arange(0			,    a__) * step_ratio).round()[::-1].copy().astype(a__)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             A__					=       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
             A__					=       (np.arange(a__			,    0			,    -step_ratio)).round().copy().astype(a__)
             timesteps -= 1
         else:
             raise ValueError(
                 F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")

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

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

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

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

         if str(a__).startswith('''mps'''):
             # mps does not support float64
             A__					=       torch.from_numpy(a__).to(a__			,    dtype=torch.floataa)
         else:
             A__					=       torch.from_numpy(a__).to(a__)

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

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

         A__					=       None

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




     def 		snake_case_		(					self			,    a__):
         # get log sigma
         A__					=       sigma.log()

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

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

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

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

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




     @property
     def 		snake_case_		(					self):
         return self.sample is None




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__ = True			,    ):
         A__					=       self.index_for_timestep(a__)

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

         if self.state_in_first_order:
             A__					=       self.sigmas[step_index]
             A__					=       self.sigmas_interpol[step_index + 1]
             A__					=       self.sigmas[step_index + 1]
         else:
             # 2nd order / KDPM2's method
             A__					=       self.sigmas[step_index - 1]
             A__					=       self.sigmas_interpol[step_index]
             A__					=       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
         A__					=       0
         A__					=       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":
             A__					=       sigma_hat if self.state_in_first_order else sigma_interpol
             A__					=       sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             A__					=       sigma_hat if self.state_in_first_order else sigma_interpol
             A__					=       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
             A__					=       (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             A__					=       sigma_interpol - sigma_hat

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

             # 3. delta timestep
             A__					=       sigma_next - sigma_hat

             A__					=       self.sample
             A__					=       None

         A__					=       sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         A__					=       self.sigmas.to(device=original_samples.device			,    dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(a__):
             # mps does not support float64
             A__					=       self.timesteps.to(original_samples.device			,    dtype=torch.floataa)
             A__					=       timesteps.to(original_samples.device			,    dtype=torch.floataa)
         else:
             A__					=       self.timesteps.to(original_samples.device)
             A__					=       timesteps.to(original_samples.device)

         A__					=       [self.index_for_timestep(a__			,    a__) for t in timesteps]

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

         A__					=       original_samples + noise * sigma
         return noisy_samples




     def __len__(					self):
         return self.config.num_train_timesteps





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDebertaVaForMaskedLM,
       TFDebertaVaForQuestionAnswering,
       TFDebertaVaForSequenceClassification,
       TFDebertaVaForTokenClassification,
       TFDebertaVaModel,
   )



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=2			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=False			,    a__=True			,    a__="None"			,    a__=3			,    a__=4			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       seq_length
         A__					=       is_training
         A__					=       use_input_mask
         A__					=       use_token_type_ids
         A__					=       use_labels
         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       type_sequence_label_size
         A__					=       initializer_range
         A__					=       num_labels
         A__					=       num_choices
         A__					=       relative_attention
         A__					=       position_biased_input
         A__					=       pos_att_type
         A__					=       scope




     def 		snake_case_		(					self):
         A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

         A__					=       None
         if self.use_input_mask:
             A__					=       random_attention_mask([self.batch_size, self.seq_length])

         A__					=       None
         if self.use_token_type_ids:
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

         A__					=       None
         A__					=       None
         A__					=       None
         if self.use_labels:
             A__					=       ids_tensor([self.batch_size]			,    self.type_sequence_label_size)
             A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels)

         A__					=       DebertaVaConfig(
             vocab_size=self.vocab_size			,    hidden_size=self.hidden_size			,    num_hidden_layers=self.num_hidden_layers			,    num_attention_heads=self.num_attention_heads			,    intermediate_size=self.intermediate_size			,    hidden_act=self.hidden_act			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    type_vocab_size=self.type_vocab_size			,    relative_attention=self.relative_attention			,    position_biased_input=self.position_biased_input			,    initializer_range=self.initializer_range			,    return_dict=a__			,    )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaModel(config=a__)
         A__					=       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A__					=       [input_ids, input_mask]
         A__					=       model(a__)

         A__					=       model(a__)

         self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForMaskedLM(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForSequenceClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFDebertaVaForTokenClassification(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A__					=       model(a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFDebertaVaForQuestionAnswering(config=a__)
         A__					=       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A__					=       model(a__)
         self.parent.assertEqual(result.start_logits.shape			,    (self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape			,    (self.batch_size, self.seq_length))




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       config_and_inputs
         A__					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict







@require_tf
class      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (
             TFDebertaVaModel,
             TFDebertaVaForMaskedLM,
             TFDebertaVaForQuestionAnswering,
             TFDebertaVaForSequenceClassification,
             TFDebertaVaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		     =					(
         {
             '''feature-extraction''': TFDebertaVaModel,
             '''fill-mask''': TFDebertaVaForMaskedLM,
             '''question-answering''': TFDebertaVaForQuestionAnswering,
             '''text-classification''': TFDebertaVaForSequenceClassification,
             '''token-classification''': TFDebertaVaForTokenClassification,
             '''zero-shot''': TFDebertaVaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    hidden_size=3_7)




     def 		snake_case_		(					self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__)




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         self.assertIsNotNone(a__)







@require_tf
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @unittest.skip(reason='''Model not available yet''')
     def 		snake_case_		(					self):
         pass




     @slow
     def 		snake_case_		(					self):
         A__					=       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge''')
         A__					=       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
         A__					=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         A__					=       model(a__			,    attention_mask=a__)[0]

         A__					=       tf.constant(
             [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,    a__			,    atol=1e-4)
