import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list







class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Optional[int] 			=    '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(""" """			)
           __snake_case       :				Any 			=    dict(zip(UpperCAmelCase_			,			range(len(UpperCAmelCase_			)			)			)			)

           __snake_case       :				Any 			=    {
               '''pad_token''': '''<pad>''',
               '''unk_token''': '''<unk>''',
               '''bos_token''': '''<s>''',
               '''eos_token''': '''</s>''',
           }
           __snake_case       :				List[Any] 			=    {
               '''feature_size''': 1,
               '''padding_value''': 0.0,
               '''sampling_rate''': 1_60_00,
               '''return_attention_mask''': False,
               '''do_normalize''': True,
           }

           __snake_case       :				Tuple 			=    tempfile.mkdtemp()
           __snake_case       :				Union[str, Any] 			=    os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["""vocab_file"""]			)
           __snake_case       :				List[str] 			=    os.path.join(self.tmpdirname			,			UpperCAmelCase_			)
           with open(self.vocab_file			,			"""w"""			,			encoding="""utf-8"""			) as fp:
               fp.write(json.dumps(UpperCAmelCase_			) + """\n"""			)

           with open(self.feature_extraction_file			,			"""w"""			,			encoding="""utf-8"""			) as fp:
               fp.write(json.dumps(UpperCAmelCase_			) + """\n"""			)

       def 			snake_case__    (							self					:     Dict			,			**_lowerCAmelCase					:     str			):
           __snake_case       :				Union[str, Any] 			=    self.add_kwargs_tokens_map.copy()
           kwargs.update(UpperCAmelCase_			)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,			**UpperCAmelCase_			)

       def 			snake_case__    (							self					:     Optional[Any]			,			**_lowerCAmelCase					:     List[Any]			):
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,			**UpperCAmelCase_			)

       def 			snake_case__    (							self					:     Optional[Any]			):
           shutil.rmtree(self.tmpdirname			)

       def 			snake_case__    (							self					:     str			):
           __snake_case       :				int 			=    self.get_tokenizer()
           __snake_case       :				Dict 			=    self.get_feature_extractor()

           __snake_case       :				Union[str, Any] 			=    WavaVecaProcessor(tokenizer=UpperCAmelCase_			,			feature_extractor=UpperCAmelCase_			)

           processor.save_pretrained(self.tmpdirname			)
           __snake_case       :				List[str] 			=    WavaVecaProcessor.from_pretrained(self.tmpdirname			)

           self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer.get_vocab()			)
           self.assertIsInstance(processor.tokenizer			,			UpperCAmelCase_			)

           self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor.to_json_string()			)
           self.assertIsInstance(processor.feature_extractor			,			UpperCAmelCase_			)

       def 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case       :				str 			=    WavaVecaProcessor(tokenizer=self.get_tokenizer()			,			feature_extractor=self.get_feature_extractor()			)
           processor.save_pretrained(self.tmpdirname			)

           __snake_case       :				List[Any] 			=    self.get_tokenizer(bos_token="""(BOS)"""			,			eos_token="""(EOS)"""			)
           __snake_case       :				Optional[Any] 			=    self.get_feature_extractor(do_normalize=UpperCAmelCase_			,			padding_value=1.0			)

           __snake_case       :				Optional[Any] 			=    WavaVecaProcessor.from_pretrained(
               self.tmpdirname			,			bos_token="""(BOS)"""			,			eos_token="""(EOS)"""			,			do_normalize=UpperCAmelCase_			,			padding_value=1.0			)

           self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()			)
           self.assertIsInstance(processor.tokenizer			,			UpperCAmelCase_			)

           self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor_add_kwargs.to_json_string()			)
           self.assertIsInstance(processor.feature_extractor			,			UpperCAmelCase_			)

       def 			snake_case__    (							self					:     int			):
           __snake_case       :				Optional[int] 			=    self.get_feature_extractor()
           __snake_case       :				List[str] 			=    self.get_tokenizer()

           __snake_case       :				Optional[int] 			=    WavaVecaProcessor(tokenizer=UpperCAmelCase_			,			feature_extractor=UpperCAmelCase_			)

           __snake_case       :				int 			=    floats_list((3, 10_00)			)

           __snake_case       :				Any 			=    feature_extractor(UpperCAmelCase_			,			return_tensors="""np"""			)
           __snake_case       :				Optional[Any] 			=    processor(UpperCAmelCase_			,			return_tensors="""np"""			)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2			)

       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				List[Any] 			=    self.get_feature_extractor()
           __snake_case       :				Union[str, Any] 			=    self.get_tokenizer()

           __snake_case       :				Tuple 			=    WavaVecaProcessor(tokenizer=UpperCAmelCase_			,			feature_extractor=UpperCAmelCase_			)

           __snake_case       :				Optional[Any] 			=    '''This is a test string'''

           __snake_case       :				List[str] 			=    processor(text=UpperCAmelCase_			)

           __snake_case       :				List[str] 			=    tokenizer(UpperCAmelCase_			)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]			)

       def 			snake_case__    (							self					:     int			):
           __snake_case       :				Any 			=    self.get_feature_extractor()
           __snake_case       :				Dict 			=    self.get_tokenizer()

           __snake_case       :				str 			=    WavaVecaProcessor(tokenizer=UpperCAmelCase_			,			feature_extractor=UpperCAmelCase_			)

           __snake_case       :				Tuple 			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __snake_case       :				Tuple 			=    processor.batch_decode(UpperCAmelCase_			)
           __snake_case       :				List[Any] 			=    tokenizer.batch_decode(UpperCAmelCase_			)

           self.assertListEqual(UpperCAmelCase_			,			UpperCAmelCase_			)





       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				Optional[Any] 			=    self.get_feature_extractor()
           __snake_case       :				Any 			=    self.get_tokenizer()

           __snake_case       :				Dict 			=    WavaVecaProcessor(tokenizer=UpperCAmelCase_			,			feature_extractor=UpperCAmelCase_			)

           self.assertListEqual(
               processor.model_input_names			,			feature_extractor.model_input_names			,			msg="""`processor` and `feature_extractor` model input names do not match"""			,			)








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

import flax
import jax.numpy as jnp
from scipy import integrate

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





@flax.struct.dataclass
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

		# setable values
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	None

		# running values
		SCREAMING_SNAKE_CASE_       :			Optional[jnp.ndarray]					       =	None







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						if common is None:
										_UpperCAmelCase						:  int     					=	CommonSchedulerState.create(self					)

						_UpperCAmelCase						:  Any     					=	jnp.arange(0       ,		self.config.num_train_timesteps					).round()[::-1]
						_UpperCAmelCase						:  Any     					=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						def lms_derivative(UpperCAmelCase_  :   Optional[int]					):
										_UpperCAmelCase						:  Optional[Any]     					=	1.0
										for k in range(UpperCAmelCase_					):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

						_UpperCAmelCase						:  Optional[Any]     					=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						_UpperCAmelCase						:  Any     					=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						_UpperCAmelCase						:  Tuple     					=	jnp.concatenate([sigmas, jnp.array([0.0]       ,		dtype=self.dtype					)]					)

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

						return state.replace(
						    timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		num_inference_steps=UpperCAmelCase_       ,		derivatives=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''







						if state.num_inference_steps is None:
										raise ValueError(
										    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

						# 2. Convert to an ODE derivative
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  int     					=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_       ,		reversed(state.derivatives					)					)					)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase_       ,		state=UpperCAmelCase_					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
