

"""simple docstring"""


import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      BigBirdConfig
				SCREAMING_SNAKE_CASE_   :      jnp.dtype         =  jnp.floataa
				SCREAMING_SNAKE_CASE_   :      bool         =  True

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						super().setup()
						__lowerCAmelCase:       Any          =			nn.Dense(5     ,  dtype=self.dtype)




				def __call__(	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				int)->   Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			super().__call__(*UpperCamelCase__     ,  **UpperCamelCase__)
						__lowerCAmelCase:       Any          =			self.cls(outputs[2])
						return outputs[:2] + (cls_out,)





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  FlaxBigBirdForNaturalQuestionsModule






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	str:
		def cross_entropy(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=None ):
				__lowerCAmelCase:       List[str]          =			logits.shape[-1]
				__lowerCAmelCase:       Dict          =			(labels[..., None] == jnp.arange(__SCREAMING_SNAKE_CASE )[None]).astype("f4" )
				__lowerCAmelCase:       Optional[int]          =			jax.nn.log_softmax(__SCREAMING_SNAKE_CASE					, axis=-1 )
				__lowerCAmelCase:       Optional[int]          =			-jnp.sum(labels * logits					, axis=-1 )
				if reduction is not None:
						__lowerCAmelCase:       Dict          =			reduction(__SCREAMING_SNAKE_CASE )
				return loss

		__lowerCAmelCase:       Optional[int]          =			partial(__SCREAMING_SNAKE_CASE					, reduction=jnp.mean )
		__lowerCAmelCase:       Tuple          =			cross_entropy(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[Any]          =			cross_entropy(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       List[str]          =			cross_entropy(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      str         =  "google/bigbird-roberta-base"
				SCREAMING_SNAKE_CASE_   :      int         =  30_00
				SCREAMING_SNAKE_CASE_   :      int         =  1_05_00

				SCREAMING_SNAKE_CASE_   :      int         =  1_28
				SCREAMING_SNAKE_CASE_   :      int         =  3

				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      int         =  5

				# tx_args
				SCREAMING_SNAKE_CASE_   :      float         =  3E-5
				SCREAMING_SNAKE_CASE_   :      float         =  0.0
				SCREAMING_SNAKE_CASE_   :      int         =  2_00_00
				SCREAMING_SNAKE_CASE_   :      float         =  0.0095

				SCREAMING_SNAKE_CASE_   :      str         =  "bigbird-roberta-natural-questions"
				SCREAMING_SNAKE_CASE_   :      str         =  "training-expt"
				SCREAMING_SNAKE_CASE_   :      str         =  "data/nq-training.jsonl"
				SCREAMING_SNAKE_CASE_   :      str         =  "data/nq-validation.jsonl"

				def 							lowercase_     (	self							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						os.makedirs(self.base_dir     ,  exist_ok=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			os.path.join(self.base_dir     ,  self.save_dir)
						__lowerCAmelCase:       List[Any]          =			self.batch_size_per_device * jax.device_count()





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      int
				SCREAMING_SNAKE_CASE_   :      int         =  40_96  # no dynamic padding on TPUs

				def __call__(	self							:				Dict     ,  UpperCamelCase__							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.collate_fn(UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			jax.tree_util.tree_map(UpperCamelCase__     ,  UpperCamelCase__)
						return batch

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			self.fetch_inputs(features["input_ids"])
						__lowerCAmelCase:       int          =			{
						    "input_ids": jnp.array(UpperCamelCase__     ,  dtype=jnp.intaa),
						    "attention_mask": jnp.array(UpperCamelCase__     ,  dtype=jnp.intaa),
						    "start_labels": jnp.array(features["start_token"]     ,  dtype=jnp.intaa),
						    "end_labels": jnp.array(features["end_token"]     ,  dtype=jnp.intaa),
						    "pooled_labels": jnp.array(features["category"]     ,  dtype=jnp.intaa),
						}
						return batch

				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				list)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			[self._fetch_inputs(UpperCamelCase__) for ids in input_ids]
						return zip(*UpperCamelCase__)




				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				list)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			[1 for _ in range(len(UpperCamelCase__))]
						while len(UpperCamelCase__) < self.max_length:
								input_ids.append(self.pad_id)
								attention_mask.append(0)
						return input_ids, attention_mask






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=None )	->	List[Any]:
		if seed is not None:
				__lowerCAmelCase:       Optional[Any]          =			dataset.shuffle(seed=__SCREAMING_SNAKE_CASE )
		for i in range(len(__SCREAMING_SNAKE_CASE ) // batch_size ):
				__lowerCAmelCase:       Optional[int]          =			dataset[i * batch_size : (i + 1) * batch_size]
				yield dict(__SCREAMING_SNAKE_CASE )






@partial(jax.pmap					, axis_name="batch" )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE )	->	int:
		def loss_fn(__SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       Optional[int]          =			model_inputs.pop("start_labels" )
				__lowerCAmelCase:       Any          =			model_inputs.pop("end_labels" )
				__lowerCAmelCase:       Tuple          =			model_inputs.pop("pooled_labels" )

				__lowerCAmelCase:       Optional[int]          =			state.apply_fn(**__SCREAMING_SNAKE_CASE					, params=__SCREAMING_SNAKE_CASE					, dropout_rng=__SCREAMING_SNAKE_CASE					, train=__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       str          =			outputs

				return state.loss_fn(
				    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, )

		__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			jax.random.split(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			jax.value_and_grad(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase:       int          =			grad_fn(state.params )
		__lowerCAmelCase:       int          =			jax.lax.pmean({"loss": loss}					, axis_name="batch" )
		__lowerCAmelCase:       Tuple          =			jax.lax.pmean(__SCREAMING_SNAKE_CASE					, "batch" )

		__lowerCAmelCase:       Union[str, Any]          =			state.apply_gradients(grads=__SCREAMING_SNAKE_CASE )
		return state, metrics, new_drp_rng






@partial(jax.pmap					, axis_name="batch" )
def     a__    (    __SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       List[str]          =			model_inputs.pop("start_labels" )
		__lowerCAmelCase:       Union[str, Any]          =			model_inputs.pop("end_labels" )
		__lowerCAmelCase:       List[Any]          =			model_inputs.pop("pooled_labels" )

		__lowerCAmelCase:       List[str]          =			state.apply_fn(**__SCREAMING_SNAKE_CASE					, params=state.params					, train=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       str          =			outputs

		__lowerCAmelCase:       Any          =			state.loss_fn(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Union[str, Any]          =			jax.lax.pmean({"loss": loss}					, axis_name="batch" )
		return metrics





class     snake_case	(				train_state.TrainState  ):
				SCREAMING_SNAKE_CASE_   :      Callable         =  struct.field(pytree_node=__snake_case  )





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Args
				SCREAMING_SNAKE_CASE_   :      Callable
				SCREAMING_SNAKE_CASE_   :      Callable
				SCREAMING_SNAKE_CASE_   :      Callable
				SCREAMING_SNAKE_CASE_   :      Callable
				SCREAMING_SNAKE_CASE_   :      wandb
				SCREAMING_SNAKE_CASE_   :      Callable         =  None

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				str=None)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			model.params
						__lowerCAmelCase:       Optional[Any]          =			TrainState.create(
						    apply_fn=model.__call__     ,  params=UpperCamelCase__     ,  tx=UpperCamelCase__     ,  loss_fn=UpperCamelCase__     ,  )
						if ckpt_dir is not None:
								__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Tuple          =			restore_checkpoint(UpperCamelCase__     ,  UpperCamelCase__)
								__lowerCAmelCase:       Optional[Any]          =			{
								    "lr": args.lr,
								    "init_lr": args.init_lr,
								    "warmup_steps": args.warmup_steps,
								    "num_train_steps": num_train_steps,
								    "weight_decay": args.weight_decay,
								}
								__lowerCAmelCase      , __lowerCAmelCase:       Dict          =			build_tx(**UpperCamelCase__)
								__lowerCAmelCase:       List[str]          =			train_state.TrainState(
								    step=UpperCamelCase__     ,  apply_fn=model.__call__     ,  params=UpperCamelCase__     ,  tx=UpperCamelCase__     ,  opt_state=UpperCamelCase__     ,  )
								__lowerCAmelCase:       Optional[Any]          =			args
								__lowerCAmelCase:       Tuple          =			data_collator
								__lowerCAmelCase:       List[str]          =			lr
								__lowerCAmelCase:       Dict          =			params
						__lowerCAmelCase:       Optional[int]          =			jax_utils.replicate(UpperCamelCase__)
						return state

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Any)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			self.args
						__lowerCAmelCase:       Tuple          =			len(UpperCamelCase__) // args.batch_size

						__lowerCAmelCase:       List[str]          =			jax.random.PRNGKey(0)
						__lowerCAmelCase:       Optional[Any]          =			jax.random.split(UpperCamelCase__     ,  jax.device_count())
						for epoch in range(args.max_epochs):
								__lowerCAmelCase:       Union[str, Any]          =			jnp.array(0     ,  dtype=jnp.floataa)
								__lowerCAmelCase:       Optional[int]          =			get_batched_dataset(UpperCamelCase__     ,  args.batch_size     ,  seed=UpperCamelCase__)
								__lowerCAmelCase:       Dict          =			0
								for batch in tqdm(UpperCamelCase__     ,  total=UpperCamelCase__     ,  desc=f"Running EPOCH-{epoch}"):
										__lowerCAmelCase:       Dict          =			self.data_collator(UpperCamelCase__)
										__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			self.train_step_fn(UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__)
										running_loss += jax_utils.unreplicate(metrics["loss"])
										i += 1
										if i % args.logging_steps == 0:
												__lowerCAmelCase:       int          =			jax_utils.unreplicate(state.step)
												__lowerCAmelCase:       Any          =			running_loss.item() / i
												__lowerCAmelCase:       List[str]          =			self.scheduler_fn(state_step - 1)

												__lowerCAmelCase:       Optional[Any]          =			self.evaluate(UpperCamelCase__     ,  UpperCamelCase__)
												__lowerCAmelCase:       str          =			{
												    "step": state_step.item(),
												    "eval_loss": eval_loss.item(),
												    "tr_loss": tr_loss,
												    "lr": lr.item(),
												}
												tqdm.write(str(UpperCamelCase__))
												self.logger.log(UpperCamelCase__     ,  commit=UpperCamelCase__)

										if i % args.save_steps == 0:
												self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}"     ,  state=UpperCamelCase__)

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			get_batched_dataset(UpperCamelCase__     ,  self.args.batch_size)
						__lowerCAmelCase:       Tuple          =			len(UpperCamelCase__) // self.args.batch_size
						__lowerCAmelCase:       str          =			jnp.array(0     ,  dtype=jnp.floataa)
						__lowerCAmelCase:       Dict          =			0
						for batch in tqdm(UpperCamelCase__     ,  total=UpperCamelCase__     ,  desc="Evaluating ... "):
								__lowerCAmelCase:       Optional[Any]          =			self.data_collator(UpperCamelCase__)
								__lowerCAmelCase:       int          =			self.val_step_fn(UpperCamelCase__     ,  **UpperCamelCase__)
								running_loss += jax_utils.unreplicate(metrics["loss"])
								i += 1
						return running_loss / i




				def 							lowercase_     (	self							:				List[Any]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			jax_utils.unreplicate(UpperCamelCase__)
						print(f"SAVING CHECKPOINT IN {save_dir}"     ,  end=" ... ")
						self.model_save_fn(UpperCamelCase__     ,  params=state.params)
						with open(os.path.join(UpperCamelCase__     ,  "opt_state.msgpack")     ,  "wb") as f:
								f.write(to_bytes(state.opt_state))
						joblib.dump(self.args     ,  os.path.join(UpperCamelCase__     ,  "args.joblib"))
						joblib.dump(self.data_collator     ,  os.path.join(UpperCamelCase__     ,  "data_collator.joblib"))
						with open(os.path.join(UpperCamelCase__     ,  "training_state.json")     ,  "w") as f:
								json.dump({"step": state.step.item()}     ,  UpperCamelCase__)
						print("DONE")






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		print(F"RESTORING CHECKPOINT FROM {save_dir}"					, end=" ... " )
		with open(os.path.join(__SCREAMING_SNAKE_CASE					, "flax_model.msgpack" )					, "rb" ) as f:
				__lowerCAmelCase:       Any          =			from_bytes(state.params					, f.read() )

		with open(os.path.join(__SCREAMING_SNAKE_CASE					, "opt_state.msgpack" )					, "rb" ) as f:
				__lowerCAmelCase:       int          =			from_bytes(state.opt_state					, f.read() )

		__lowerCAmelCase:       Union[str, Any]          =			joblib.load(os.path.join(__SCREAMING_SNAKE_CASE					, "args.joblib" ) )
		__lowerCAmelCase:       Optional[Any]          =			joblib.load(os.path.join(__SCREAMING_SNAKE_CASE					, "data_collator.joblib" ) )

		with open(os.path.join(__SCREAMING_SNAKE_CASE					, "training_state.json" )					, "r" ) as f:
				__lowerCAmelCase:       str          =			json.load(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Tuple          =			training_state["step"]

		print("DONE" )
		return params, opt_state, step, args, data_collator






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Any:
		__lowerCAmelCase:       Dict          =			num_train_steps - warmup_steps
		__lowerCAmelCase:       str          =			optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE					, end_value=__SCREAMING_SNAKE_CASE					, transition_steps=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Union[str, Any]          =			optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE					, end_value=1E-7					, transition_steps=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Tuple          =			optax.join_schedules(schedules=[warmup_fn, decay_fn]					, boundaries=[warmup_steps] )
		return lr






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Any:
		def weight_decay_mask(__SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       List[str]          =			traverse_util.flatten_dict(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Tuple          =			{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
				return traverse_util.unflatten_dict(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Tuple          =			scheduler_fn(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Optional[int]          =			optax.adamw(learning_rate=__SCREAMING_SNAKE_CASE					, weight_decay=__SCREAMING_SNAKE_CASE					, mask=__SCREAMING_SNAKE_CASE )
		return tx, lr





"""simple docstring"""


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       int          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						self.assertTrue(all(len(UpperCamelCase__) == len(UpperCamelCase__) for x, y in zip(UpperCamelCase__     ,  processed_features[input_name])))

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_torch
				def 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="pt")

						__lowerCAmelCase:       str          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Any          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				Tuple):
								__lowerCAmelCase:       Optional[int]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				str     ,  UpperCamelCase__							:				str):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[int]          =			self.feat_extract_tester.seq_length_diff
						__lowerCAmelCase:       str          =			self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.min_seq_length
						__lowerCAmelCase:       Union[str, Any]          =			self.feat_extract_tester.batch_size
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						# max_length parameter has to be provided when setting `padding="max_length"`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length")[input_name]

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						self.assertTrue(len(input_a[0]) == pad_min_length)
						self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
						self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
						self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

						# test padding for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == expected_mult_pad_length for x in input_a))
						self.assertEqual(input_a.shape[:2]     ,  (batch_size, expected_mult_pad_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == feature_size)

						# Check padding value is correct
						__lowerCAmelCase:       Any          =			(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
						    < 1e-3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				List[Any]):
								__lowerCAmelCase:       List[str]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						# truncate to smallest
						__lowerCAmelCase:       int          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to smallest with np
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np"     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

						self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  truncation=UpperCamelCase__)[input_name]

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase:       Optional[Any]          =			len(speech_inputs[0])
						if expected_length % pad_to_multiple_of != 0:
								__lowerCAmelCase:       Optional[Any]          =			((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

						self.assertTrue(len(input_a[0]) == expected_length)
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       str          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="pt")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)

				@require_tf
				def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="tf")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)

				def 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       Any          =			True
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Tuple          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Optional[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(list(processed.attention_mask.shape)     ,  list(processed[input_name].shape[:2]))
						self.assertListEqual(processed.attention_mask.sum(-1).tolist()     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape)     ,  [processed_pad[input_name].shape[0], max_length])
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1).tolist()     ,  [max_length for x in speech_inputs])


