

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =42

			# setable values
			lowerCamelCase__							     =42
			lowerCamelCase__							     =42
			lowerCamelCase__							     =None
			@classmethod
			def 				__UpperCamelCase			(      cls				:			Tuple   ,   a				:			CommonSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray						)				->							Dict:

				"""simple docstring"""

				return cls(common=a   ,   init_noise_sigma=a   ,   timesteps=a						)






@dataclass
class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =42






class _UpperCamelCase				(				__A				,						__A ):
			'''simple docstring'''







			lowerCamelCase__							     =[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase__							     =42
			@property
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(      self				:			str   ,   a				:			int = 1000   ,   a				:			float = 0.0001   ,   a				:			float = 0.02   ,   a				:			str = "linear"   ,   a				:			Optional[jnp.ndarray] = None   ,   a				:			str = "fixed_small"   ,   a				:			bool = True   ,   a				:			str = "epsilon"   ,   a				:			jnp.dtype = jnp.floataa   ,   )				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							dtype
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			Optional[CommonSchedulerState] = None						)				->							DDPMSchedulerState:

				"""simple docstring"""

				if common is None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							CommonSchedulerState.create(self						)

				# standard deviation of the initial noise distribution
				SCREAMING_SNAKE_CASE :   Tuple					  =							jnp.array(1.0   ,   dtype=self.dtype						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							jnp.arange(0   ,   self.config.num_train_timesteps						).round()[::-1]

				return DDPMSchedulerState.create(
				    common=a   ,   init_noise_sigma=a   ,   timesteps=a   ,   )
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			Optional[int] = None						)				->							jnp.ndarray:

				"""simple docstring"""

				return sample
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			DDPMSchedulerState   ,   a				:			int   ,   a				:			Tuple = ()						)				->							DDPMSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.config.num_train_timesteps // num_inference_steps
				# creates integer timesteps by multiplying by ratio
				# rounding to avoid issues when num_inference_step is power of 3
				SCREAMING_SNAKE_CASE :   Dict					  =							(jnp.arange(0   ,   a						) * step_ratio).round()[::-1]

				return state.replace(
				    num_inference_steps=a   ,   timesteps=a   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			DDPMSchedulerState   ,   a				:			List[str]   ,   a				:			Any=None   ,   a				:			Dict=None						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							jnp.where(t > 0   ,   state.common.alphas_cumprod[t - 1]   ,   jnp.array(1.0   ,   dtype=self.dtype						)						)

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
				# and sample from it to get previous sample
				# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
				SCREAMING_SNAKE_CASE :   List[str]					  =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

				if variance_type is None:
					SCREAMING_SNAKE_CASE :   int					  =							self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small":
					SCREAMING_SNAKE_CASE :   str					  =							jnp.clip(a   ,   a_min=1e-20						)
				# for rl-diffuser https://arxiv.org/abs/2205.09991
				elif variance_type == "fixed_small_log":
					SCREAMING_SNAKE_CASE :   Any					  =							jnp.log(jnp.clip(a   ,   a_min=1e-20						)						)
				elif variance_type == "fixed_large":
					SCREAMING_SNAKE_CASE :   int					  =							state.common.betas[t]
				elif variance_type == "fixed_large_log":
					# Glide max_log
					SCREAMING_SNAKE_CASE :   str					  =							jnp.log(state.common.betas[t]						)
				elif variance_type == "learned":
					return predicted_variance
				elif variance_type == "learned_range":
					SCREAMING_SNAKE_CASE :   Any					  =							variance
					SCREAMING_SNAKE_CASE :   int					  =							state.common.betas[t]
					SCREAMING_SNAKE_CASE :   Dict					  =							(predicted_variance + 1) / 2
					SCREAMING_SNAKE_CASE :   List[Any]					  =							frac * max_log + (1 - frac) * min_log

				return variance
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			int   ,   a				:			jnp.ndarray   ,   a				:			Optional[jax.random.KeyArray] = None   ,   a				:			bool = True   ,   )				->							Union[FlaxDDPMSchedulerOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							timestep

				if key is None:
					SCREAMING_SNAKE_CASE :   str					  =							jax.random.PRNGKey(0						)

				if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							jnp.split(a   ,   sample.shape[1]   ,   axis=1						)
				else:
					SCREAMING_SNAKE_CASE :   int					  =							None

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE :   Tuple					  =							state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							jnp.where(t > 0   ,   state.common.alphas_cumprod[t - 1]   ,   jnp.array(1.0   ,   dtype=self.dtype						)						)
				SCREAMING_SNAKE_CASE :   str					  =							1 - alpha_prod_t
				SCREAMING_SNAKE_CASE :   str					  =							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 self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE :   int					  =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model_output
				elif self.config.prediction_type == "v_prediction":
					SCREAMING_SNAKE_CASE :   List[Any]					  =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
				else:
					raise ValueError(
					    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
					    " for the FlaxDDPMScheduler."						)

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
					SCREAMING_SNAKE_CASE :   Tuple					  =							jnp.clip(a   ,   -1   ,   1						)

				# 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
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE :   int					  =							state.common.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
				SCREAMING_SNAKE_CASE :   Dict					  =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				def random_variance():
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							jax.random.split(a   ,   num=1						)
					SCREAMING_SNAKE_CASE :   Dict					  =							jax.random.normal(a   ,   shape=model_output.shape   ,   dtype=self.dtype						)
					return (self._get_variance(a   ,   a   ,   predicted_variance=a						) ** 0.5) * noise

				SCREAMING_SNAKE_CASE :   str					  =							jnp.where(t > 0   ,   random_variance()   ,   jnp.zeros(model_output.shape   ,   dtype=self.dtype						)						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=a   ,   state=a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   )				->							jnp.ndarray:

				"""simple docstring"""

				return add_noise_common(state.common   ,   a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   )				->							jnp.ndarray:

				"""simple docstring"""

				return get_velocity_common(state.common   ,   a   ,   a   ,   a						)






			def __len__(      self				:			int						)				->							Any:

				"""simple docstring"""

				return self.config.num_train_timesteps






import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     NystromformerForMaskedLM,
     NystromformerForMultipleChoice,
     NystromformerForQuestionAnswering,
     NystromformerForSequenceClassification,
     NystromformerForTokenClassification,
     NystromformerModel,
 )
 from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=7				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=99				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=16				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=4				,     snake_case_=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     =							scope




     def        lowerCAmelCase__		(    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     =							ids_tensor([self.batch_size]				,     self.num_choices							)

         _A     =							self.get_config()

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




     def        lowerCAmelCase__		(    self							):
         return NystromformerConfig(
             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				,     is_decoder=snake_case_				,     initializer_range=self.initializer_range				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForMaskedLM(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForQuestionAnswering(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     start_positions=snake_case_				,     end_positions=snake_case_				,     )
         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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForSequenceClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForTokenClassification(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_choices
         _A     =							NystromformerForMultipleChoice(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							input_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							token_type_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							input_mask.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							model(
             snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_				,     labels=snake_case_				,     )
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)							)




     def        lowerCAmelCase__		(    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_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': NystromformerModel,
             'fill-mask': NystromformerForMaskedLM,
             'question-answering': NystromformerForQuestionAnswering,
             'text-classification': NystromformerForSequenceClassification,
             'token-classification': NystromformerForTokenClassification,
             'zero-shot': NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             _A     =							type
             self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							NystromformerModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)







@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModel.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							torch.tensor([[0, 1, 2, 3, 4, 5]]							)

         with torch.no_grad():
             _A     =							model(snake_case_							)[0]

         _A     =							torch.Size((1, 6, 768)							)
         self.assertEqual(output.shape				,     snake_case_							)

         _A     =							torch.tensor(
             [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]							)

         self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case_				,     atol=1E-4							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							'the [MASK] of Belgium is Brussels'

         _A     =							AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'							)
         _A     =							NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'							)

         _A     =							tokenizer(snake_case_				,     return_tensors='pt'							)

         with torch.no_grad():
             _A     =							model(encoding.input_ids							).logits

         _A     =							token_logits[:, 2, :].argmax(-1							)[0]

         self.assertEqual(tokenizer.decode(snake_case_							)				,     'capital'							)





