
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCamelCase_							=     logging.get_logger(__name__)




class 	__A(				_lowerCAmelCase ):

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				feature_size
       UpperCamelCase__				=				sampling_rate
       UpperCamelCase__				=				padding_value

       UpperCamelCase__				=				kwargs.pop("""padding_side"""       ,			"""right"""						)
       UpperCamelCase__				=				kwargs.pop("""return_attention_mask"""       ,			SCREAMING_SNAKE_CASE_						)

       super().__init__(**SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = True       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = False       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			):
       # If we have a list of dicts, let's convert it in a dict of lists
       # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
       if isinstance(SCREAMING_SNAKE_CASE_       ,			(list, tuple)						) and isinstance(processed_features[0]       ,			(dict, BatchFeature)						):
            UpperCamelCase__				=				{
                key: [example[key] for example in processed_features] for key in processed_features[0].keys()
            }

       # The model's main input name, usually `input_values`, has be passed for padding
       if self.model_input_names[0] not in processed_features:
            raise ValueError(
                """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
                F" to this method that includes {self.model_input_names[0]}, but you provided"
                F" {list(processed_features.keys()						)}"						)

       UpperCamelCase__				=				processed_features[self.model_input_names[0]]
       UpperCamelCase__				=				(
           return_attention_mask if return_attention_mask is not None else self.return_attention_mask
       )

       if len(SCREAMING_SNAKE_CASE_						) == 0:
            if return_attention_mask:
                 UpperCamelCase__				=				[]
            return processed_features

       # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
       # and rebuild them afterwards if no return_tensors is specified
       # Note that we lose the specific device the tensor may be on for PyTorch

       UpperCamelCase__				=				required_input[0]
       if isinstance(SCREAMING_SNAKE_CASE_       ,			(list, tuple)						):
            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
            UpperCamelCase__				=				0
            while len(required_input[index]						) == 0:
                 index += 1
            if index < len(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=				required_input[index][0]

       if return_tensors is None:
            if is_tf_tensor(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=				"""tf"""
            elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=				"""pt"""
            elif isinstance(SCREAMING_SNAKE_CASE_       ,			(int, float, list, tuple, np.ndarray)						):
                 UpperCamelCase__				=				"""np"""
            else:
                 raise ValueError(
                     F"type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE_						)}. "
                     """Should be one of a python, numpy, pytorch or tensorflow object."""						)

       for key, value in processed_features.items():
            if isinstance(value[0]       ,			(int, float)						):
                 UpperCamelCase__				=				to_numpy(SCREAMING_SNAKE_CASE_						)
            else:
                 UpperCamelCase__				=				[to_numpy(SCREAMING_SNAKE_CASE_						) for v in value]

        # Convert padding_strategy in PaddingStrategy
       UpperCamelCase__				=				self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE_       ,			max_length=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=				processed_features[self.model_input_names[0]]

       UpperCamelCase__				=				len(SCREAMING_SNAKE_CASE_						)
       if not all(len(SCREAMING_SNAKE_CASE_						) == batch_size for v in processed_features.values()						):
            raise ValueError("""Some items in the output dictionary have a different batch size than others."""						)

       UpperCamelCase__				=				[]
       for i in range(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=				{k: v[i] for k, v in processed_features.items()}
            # truncation
            UpperCamelCase__				=				self._truncate(
                SCREAMING_SNAKE_CASE_       ,			max_length=SCREAMING_SNAKE_CASE_       ,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_       ,			truncation=SCREAMING_SNAKE_CASE_       ,			)
            truncated_inputs.append(SCREAMING_SNAKE_CASE_						)

       if padding_strategy == PaddingStrategy.LONGEST:
            # make sure that `max_length` cannot be longer than the longest truncated length
            UpperCamelCase__				=				max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
            UpperCamelCase__				=				PaddingStrategy.MAX_LENGTH

       UpperCamelCase__				=				{}
       for i in range(SCREAMING_SNAKE_CASE_						):
            # padding
            UpperCamelCase__				=				self._pad(
                truncated_inputs[i]       ,			max_length=SCREAMING_SNAKE_CASE_       ,			padding_strategy=SCREAMING_SNAKE_CASE_       ,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_       ,			return_attention_mask=SCREAMING_SNAKE_CASE_       ,			)

            for key, value in outputs.items():
                 if key not in batch_outputs:
                      UpperCamelCase__				=				[]
                 if value.dtype is np.dtype(np.floataa						):
                      UpperCamelCase__				=				value.astype(np.floataa						)
                 batch_outputs[key].append(SCREAMING_SNAKE_CASE_						)

       return BatchFeature(SCREAMING_SNAKE_CASE_       ,			tensor_type=SCREAMING_SNAKE_CASE_						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = PaddingStrategy.DO_NOT_PAD       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			):
       UpperCamelCase__				=				processed_features[self.model_input_names[0]]

       if padding_strategy == PaddingStrategy.LONGEST:
            UpperCamelCase__				=				len(SCREAMING_SNAKE_CASE_						)

       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            UpperCamelCase__				=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       UpperCamelCase__				=				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE_						) < max_length

       if return_attention_mask and "attention_mask" not in processed_features:
            UpperCamelCase__				=				np.ones(len(SCREAMING_SNAKE_CASE_						)       ,			dtype=np.intaa						)

       if needs_to_be_padded:
            UpperCamelCase__				=				max_length - len(SCREAMING_SNAKE_CASE_						)
            if self.padding_side == "right":
                 if return_attention_mask:
                      UpperCamelCase__				=				np.pad(
                          processed_features["""attention_mask"""]       ,			(0, difference)						)
                 UpperCamelCase__				=				((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                 UpperCamelCase__				=				np.pad(
                     SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			"""constant"""       ,			constant_values=self.padding_value						)
            elif self.padding_side == "left":
                 if return_attention_mask:
                      UpperCamelCase__				=				np.pad(
                          processed_features["""attention_mask"""]       ,			(difference, 0)						)
                 UpperCamelCase__				=				((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                 UpperCamelCase__				=				np.pad(
                     SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			"""constant"""       ,			constant_values=self.padding_value						)
            else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

       return processed_features







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			SCREAMING_SNAKE_CASE_ = None       ,			):
       if not truncation:
            return processed_features
       elif truncation and max_length is None:
            raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""						)

       UpperCamelCase__				=				processed_features[self.model_input_names[0]]

       # find `max_length` that fits `pad_to_multiple_of`
       if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            UpperCamelCase__				=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

       UpperCamelCase__				=				len(SCREAMING_SNAKE_CASE_						) > max_length

       if needs_to_be_truncated:
            UpperCamelCase__				=				processed_features[self.model_input_names[0]][:max_length]
            if "attention_mask" in processed_features:
                 UpperCamelCase__				=				processed_features["""attention_mask"""][:max_length]

       return processed_features







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_=False       ,			SCREAMING_SNAKE_CASE_=None						):

       # Get padding strategy
       if padding is not False:
            if padding is True:
                 UpperCamelCase__				=				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
            elif not isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=				PaddingStrategy(SCREAMING_SNAKE_CASE_						)
            elif isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=				padding
       else:
            UpperCamelCase__				=				PaddingStrategy.DO_NOT_PAD

       # Set max length if needed
       if max_length is None:
            if padding_strategy == PaddingStrategy.MAX_LENGTH:
                 raise ValueError(
                     F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"						)

        # Test if we have a padding value
       if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
            raise ValueError(
                """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
                """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""						)

       return padding_strategy










"""simple docstring"""
import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							List[str]						,     snake_case:							Optional[Any]=13						,     snake_case:							List[str]=7						,     snake_case:							Dict=True						,     snake_case:							List[str]=True						,     snake_case:							Optional[int]=True						,     snake_case:							Any=True						,     snake_case:							Optional[Any]=99						,     snake_case:							Tuple=32						,     snake_case:							Tuple=5						,     snake_case:							Dict=4						,     snake_case:							Optional[Any]=37						,     snake_case:							Union[str, Any]="gelu"						,     snake_case:							Tuple=0.1						,     snake_case:							List[Any]=0.1						,     snake_case:							List[str]=512						,     snake_case:							Optional[int]=16						,     snake_case:							int=2						,     snake_case:							List[Any]=0.0_2						,     snake_case:							Union[str, Any]=4						,     )   ->	List[str]:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :Any						=			seq_length
											snake_case_ :List[str]						=			is_training
											snake_case_ :Optional[Any]						=			use_attention_mask
											snake_case_ :Dict						=			use_token_type_ids
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :str						=			vocab_size
											snake_case_ :int						=			hidden_size
											snake_case_ :List[str]						=			num_hidden_layers
											snake_case_ :Dict						=			num_attention_heads
											snake_case_ :Any						=			intermediate_size
											snake_case_ :Tuple						=			hidden_act
											snake_case_ :int						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Any						=			max_position_embeddings
											snake_case_ :Union[str, Any]						=			type_vocab_size
											snake_case_ :Optional[int]						=			type_sequence_label_size
											snake_case_ :Union[str, Any]						=			initializer_range
											snake_case_ :Tuple						=			num_choices






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	str:
											snake_case_ :Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

											snake_case_ :Union[str, Any]						=			None
											if self.use_attention_mask:
																	snake_case_ :str						=			random_attention_mask([self.batch_size, self.seq_length]						)

											snake_case_ :Any						=			None
											if self.use_token_type_ids:
																	snake_case_ :List[str]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

											snake_case_ :int						=			BertConfig(
											    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						,     )

											return config, input_ids, token_type_ids, attention_mask






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											snake_case_ :str						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Optional[int]						=			config_and_inputs
											snake_case_ :Union[str, Any]						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Any:
											snake_case_ :int						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Dict						=			config_and_inputs

											snake_case_ :Union[str, Any]						=			True
											snake_case_ :Optional[int]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											snake_case_ :Tuple						=			ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2						)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)

@require_flax
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     True

					_A		:			Dict						=     (
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			FlaxBertModelTester(self						)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											snake_case_ :Dict						=			FlaxBertModel.from_pretrained("""bert-base-cased"""						)
											snake_case_ :Dict						=			model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)
