



from __future__ import annotations

import unittest

from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class 				lowerCAmelCase_		:
       UpperCAmelCase__				:					List[str]        =  LEDConfig
       UpperCAmelCase__				:					Any        =  {}
       UpperCAmelCase__				:					Any        =  "gelu"
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=13,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=99,			SCREAMING_SNAKE_CASE_=32,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=37,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=20,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=4,			)     ->		Optional[Any]:
          UpperCamelCase    :  Optional[Any]							=	parent
          UpperCamelCase    :  Union[str, Any]							=	batch_size
          UpperCamelCase    :  Optional[Any]							=	seq_length
          UpperCamelCase    :  List[Any]							=	is_training
          UpperCamelCase    :  Union[str, Any]							=	use_labels
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  str							=	hidden_size
          UpperCamelCase    :  List[str]							=	num_hidden_layers
          UpperCamelCase    :  Any							=	num_attention_heads
          UpperCamelCase    :  List[str]							=	intermediate_size

          UpperCamelCase    :  List[str]							=	hidden_dropout_prob
          UpperCamelCase    :  List[Any]							=	attention_probs_dropout_prob
          UpperCamelCase    :  List[Any]							=	max_position_embeddings
          UpperCamelCase    :  Any							=	eos_token_id
          UpperCamelCase    :  Tuple							=	pad_token_id
          UpperCamelCase    :  Union[str, Any]							=	bos_token_id
          UpperCamelCase    :  Dict							=	attention_window

          # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
          # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
          # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
          # because its local attention only attends to `self.attention_window` and one before and one after
          UpperCamelCase    :  int							=	self.attention_window + 2

          # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
          # the `test_attention_outputs` and `test_hidden_states_output` tests
          UpperCamelCase    :  Optional[int]							=	(
              self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
          )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  str							=	ids_tensor([self.batch_size, self.seq_length - 1],			self.vocab_size       )
          UpperCamelCase    :  Optional[Any]							=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       ),			1       )
          UpperCamelCase    :  Any							=	tf.concat([input_ids, eos_tensor],			axis=1       )

          UpperCamelCase    :  List[Any]							=	ids_tensor([self.batch_size, self.seq_length],			self.vocab_size       )

          UpperCamelCase    :  Dict							=	self.config_cls(
              vocab_size=self.vocab_size,			d_model=self.hidden_size,			encoder_layers=self.num_hidden_layers,			decoder_layers=self.num_hidden_layers,			encoder_attention_heads=self.num_attention_heads,			decoder_attention_heads=self.num_attention_heads,			encoder_ffn_dim=self.intermediate_size,			decoder_ffn_dim=self.intermediate_size,			dropout=self.hidden_dropout_prob,			attention_dropout=self.attention_probs_dropout_prob,			max_position_embeddings=self.max_position_embeddings,			eos_token_ids=[2],			bos_token_id=self.bos_token_id,			pad_token_id=self.pad_token_id,			decoder_start_token_id=self.pad_token_id,			attention_window=self.attention_window,			**self.config_updates,			)
          UpperCamelCase    :  Any							=	prepare_led_inputs_dict(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	tf.concat(
              [tf.zeros_like(SCREAMING_SNAKE_CASE_       )[:, :-1], tf.ones_like(SCREAMING_SNAKE_CASE_       )[:, -1:]],			axis=-1,			)
          UpperCamelCase    :  Optional[Any]							=	global_attention_mask
          return config, inputs_dict






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          UpperCamelCase    :  str							=	TFLEDModel(config=SCREAMING_SNAKE_CASE_       ).get_decoder()
          UpperCamelCase    :  Any							=	inputs_dict['input_ids']

          UpperCamelCase    :  List[Any]							=	input_ids[:1, :]
          UpperCamelCase    :  Any							=	inputs_dict['attention_mask'][:1, :]
          UpperCamelCase    :  str							=	1

          # first forward pass
          UpperCamelCase    :  Union[str, Any]							=	model(SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			use_cache=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase	,     UpperCamelCase    :  List[str]							=	outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCamelCase    :  List[str]							=	ids_tensor((self.batch_size, 3),			config.vocab_size       )
          UpperCamelCase    :  Union[str, Any]							=	tf.cast(ids_tensor((self.batch_size, 3),			2       ),			tf.inta       )

          # append to next input_ids and
          UpperCamelCase    :  Tuple							=	tf.concat([input_ids, next_tokens],			axis=-1       )
          UpperCamelCase    :  str							=	tf.concat([attention_mask, next_attn_mask],			axis=-1       )

          UpperCamelCase    :  str							=	model(SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_       )[0]
          UpperCamelCase    :  List[str]							=	model(SCREAMING_SNAKE_CASE_,			attention_mask=SCREAMING_SNAKE_CASE_,			past_key_values=SCREAMING_SNAKE_CASE_       )[0]

          self.parent.assertEqual(next_tokens.shape[1],			output_from_past.shape[1]       )

          # select random slice
          UpperCamelCase    :  Dict							=	int(ids_tensor((1,),			output_from_past.shape[-1]       )       )
          UpperCamelCase    :  Optional[int]							=	output_from_no_past[:, -3:, random_slice_idx]
          UpperCamelCase    :  Union[str, Any]							=	output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			rtol=1e-3       )






def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int       ,     snake_case__       :    Union[str, Any]=None       ,     snake_case__       :    Optional[Any]=None       ,     snake_case__       :    Union[str, Any]=None       ,     snake_case__       :    Tuple=None       ,     )	->     Dict:
   if attention_mask is None:
      UpperCamelCase    :  Union[str, Any]							=	tf.cast(tf.math.not_equal(snake_case__       ,     config.pad_token_id	)       ,     tf.inta	)
   if decoder_attention_mask is None:
      UpperCamelCase    :  str							=	tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape       ,     dtype=tf.inta	),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,     config.pad_token_id	)       ,     tf.inta	),
          ]       ,     axis=-1       ,     )
   if head_mask is None:
      UpperCamelCase    :  int							=	tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
   if decoder_head_mask is None:
      UpperCamelCase    :  Dict							=	tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
       "decoder_input_ids": decoder_input_ids,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
   }



@require_tf
class 				lowerCAmelCase_		(  a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Union[str, Any]        =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
       UpperCAmelCase__				:					List[str]        =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
       UpperCAmelCase__				:					Any        =  (
           {
               "conversational": TFLEDForConditionalGeneration,
               "feature-extraction": TFLEDModel,
               "summarization": TFLEDForConditionalGeneration,
               "text2text-generation": TFLEDForConditionalGeneration,
               "translation": TFLEDForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCAmelCase__				:					Dict        =  True
       UpperCAmelCase__				:					Tuple        =  False
       UpperCAmelCase__				:					str        =  False
       UpperCAmelCase__				:					Union[str, Any]        =  False
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	TFLEDModelTester(self       )
          UpperCamelCase    :  Dict							=	ConfigTester(self,			config_class=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Any:
          self.config_tester.run_common_tests()
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  str							=	self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase	,     UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase    :  List[str]							=	tf.zeros_like(inputs_dict['attention_mask']       )
          UpperCamelCase    :  List[str]							=	2
          UpperCamelCase    :  List[Any]							=	tf.where(
              tf.range(self.model_tester.seq_length       )[None, :] < num_global_attn_indices,			1,			inputs_dict['global_attention_mask'],			)

          UpperCamelCase    :  List[str]							=	True
          UpperCamelCase    :  List[str]							=	self.model_tester.seq_length
          UpperCamelCase    :  int							=	self.model_tester.encoder_seq_length

          def check_decoder_attentions_output(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[Any]							=	outputs.decoder_attentions
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			self.model_tester.num_hidden_layers       )
             self.assertListEqual(
                 list(decoder_attentions[0].shape[-3:]       ),			[self.model_tester.num_attention_heads, seq_length, seq_length],			)

          def check_encoder_attentions_output(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  str							=	[t.numpy() for t in outputs.encoder_attentions]
             UpperCamelCase    :  Dict							=	[t.numpy() for t in outputs.encoder_global_attentions]
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			self.model_tester.num_hidden_layers       )
             self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			self.model_tester.num_hidden_layers       )
             self.assertListEqual(
                 list(attentions[0].shape[-3:]       ),			[self.model_tester.num_attention_heads, seq_length, seq_length],			)
             self.assertListEqual(
                 list(global_attentions[0].shape[-3:]       ),			[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices],			)

          for model_class in self.all_model_classes:
             UpperCamelCase    :  List[str]							=	True
             UpperCamelCase    :  Dict							=	False
             UpperCamelCase    :  Optional[int]							=	False
             UpperCamelCase    :  str							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  List[str]							=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )
             UpperCamelCase    :  Any							=	len(SCREAMING_SNAKE_CASE_       )
             self.assertEqual(config.output_hidden_states,			SCREAMING_SNAKE_CASE_       )
             check_encoder_attentions_output(SCREAMING_SNAKE_CASE_       )

             if self.is_encoder_decoder:
                UpperCamelCase    :  Optional[int]							=	model_class(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase    :  Any							=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )
                self.assertEqual(config.output_hidden_states,			SCREAMING_SNAKE_CASE_       )
                check_decoder_attentions_output(SCREAMING_SNAKE_CASE_       )

             # Check that output attentions can also be changed via the config
             del inputs_dict["output_attentions"]
             UpperCamelCase    :  Tuple							=	True
             UpperCamelCase    :  Optional[Any]							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Optional[Any]							=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )
             self.assertEqual(config.output_hidden_states,			SCREAMING_SNAKE_CASE_       )
             check_encoder_attentions_output(SCREAMING_SNAKE_CASE_       )

             # Check attention is always last and order is fine
             UpperCamelCase    :  List[Any]							=	True
             UpperCamelCase    :  List[Any]							=	True
             UpperCamelCase    :  Tuple							=	model_class(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  int							=	model(self._prepare_for_class(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )       )

             self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1),			len(SCREAMING_SNAKE_CASE_       )       )
             self.assertEqual(model.config.output_hidden_states,			SCREAMING_SNAKE_CASE_       )
             check_encoder_attentions_output(SCREAMING_SNAKE_CASE_       )
       @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          pass






       def 					snake_case_       (			self       )     ->		Optional[int]:
          # TODO: Head-masking not yet implement
          pass






def 		UpperCamelCase	(   snake_case__       :    Optional[Any]	)	->     Optional[int]:
   return tf.constant(snake_case__       ,     dtype=tf.intaa	)


__UpperCAmelCase          =       1e-4



@slow
@require_tf
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Optional[int]							=	TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'       ).led

          # change to intended input here
          UpperCamelCase    :  List[Any]							=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]       )
          UpperCamelCase    :  Optional[Any]							=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]       )
          UpperCamelCase    :  str							=	prepare_led_inputs_dict(model.config,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model(**SCREAMING_SNAKE_CASE_       )[0]
          UpperCamelCase    :  int							=	(1, 1024, 768)
          self.assertEqual(output.shape,			SCREAMING_SNAKE_CASE_       )
          # change to expected output here
          UpperCamelCase    :  Tuple							=	tf.convert_to_tensor(
              [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]],			)
          tf.debugging.assert_near(output[:, :3, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-3       )






       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'       )

          # change to intended input here
          UpperCamelCase    :  int							=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]       )
          UpperCamelCase    :  Dict							=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]       )
          UpperCamelCase    :  Optional[int]							=	prepare_led_inputs_dict(model.config,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	model(**SCREAMING_SNAKE_CASE_       )[0]
          UpperCamelCase    :  Any							=	(1, 1024, model.config.vocab_size)
          self.assertEqual(output.shape,			SCREAMING_SNAKE_CASE_       )
          # change to expected output here
          UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor(
              [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]],			)
          tf.debugging.assert_near(output[:, :3, :3],			SCREAMING_SNAKE_CASE_,			atol=1e-3,			rtol=1e-3       )








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 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    Optional[Any]	):
         return math.exp(t * -12.0	)

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

   UpperCamelCase    :  List[Any]							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  Optional[Any]							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Union[str, Any]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	torch.linspace(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase    :  int							=	(
                 torch.linspace(beta_start**0.5,			beta_end**0.5,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          UpperCamelCase    :  Optional[int]							=	1.0 - self.betas
          UpperCamelCase    :  int							=	torch.cumprod(self.alphas,			dim=0       )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

          UpperCamelCase    :  Dict							=	(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:
             UpperCamelCase    :  Optional[int]							=	1 if len(SCREAMING_SNAKE_CASE_       ) > 1 else 0
          else:
             UpperCamelCase    :  str							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
             UpperCamelCase    :  int							=	self._index_counter[timestep_int]

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          # 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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

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

          UpperCamelCase    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

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

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

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

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          # advance index counter by 1
          UpperCamelCase    :  Optional[int]							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             UpperCamelCase    :  Tuple							=	self.sigmas[step_index]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index + 1]
             UpperCamelCase    :  Optional[int]							=	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             UpperCamelCase    :  str							=	self.sigmas[step_index - 1]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index]
             UpperCamelCase    :  Any							=	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
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Any							=	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":
             UpperCamelCase    :  Any							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  List[Any]							=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             UpperCamelCase    :  Union[str, Any]							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  Optional[int]							=	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
             UpperCamelCase    :  int							=	(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase    :  Any							=	sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase    :  Optional[Any]							=	self.sigmas.to(device=original_samples.device,			dtype=original_samples.dtype       )
          if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_       ):
             # mps does not support float64
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

          UpperCamelCase    :  List[Any]							=	sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  int							=	sigma.unsqueeze(-1       )

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






       def __len__(			self       )     ->		Optional[int]:
          return self.config.num_train_timesteps



