
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


__A				=logging.get_logger(__name__)




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       str:
					lowerCamelCase_						=			feature_size
					lowerCamelCase_						=			sampling_rate
					lowerCamelCase_						=			padding_value

					lowerCamelCase_						=			kwargs.pop("padding_side"						,  "right" )
					lowerCamelCase_						=			kwargs.pop("return_attention_mask"						,  lowercase )

					super().__init__(**lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = True						,  lowercase = None						,  lowercase = False						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       BatchFeature:
					# 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(lowercase						,  (list, tuple) ) and isinstance(processed_features[0]						,  (dict, BatchFeature) ):
									lowerCamelCase_						=			{
									    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() )}' )

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

					if len(lowercase ) == 0:
									if return_attention_mask:
													lowerCamelCase_						=			[]
									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

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

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

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

        # Convert padding_strategy in PaddingStrategy
					lowerCamelCase_						=			self._get_padding_strategies(padding=lowercase						,  max_length=lowercase )

					lowerCamelCase_						=			processed_features[self.model_input_names[0]]

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

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

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

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

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

					return BatchFeature(lowercase						,  tensor_type=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = PaddingStrategy.DO_NOT_PAD						,  lowercase = None						,  lowercase = None						,  )				->       dict:
					lowerCamelCase_						=			processed_features[self.model_input_names[0]]

					if padding_strategy == PaddingStrategy.LONGEST:
									lowerCamelCase_						=			len(lowercase )

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

					lowerCamelCase_						=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length

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

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

					return processed_features



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       Dict:
					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." )

					lowerCamelCase_						=			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):
									lowerCamelCase_						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

					lowerCamelCase_						=			len(lowercase ) > max_length

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

					return processed_features



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=False						,  lowercase=None )				->       Any:

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


