

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     _a						(__snake_case				):


					'''simple docstring'''






					UpperCAmelCase__:   List[str]  							=       None
					UpperCAmelCase__:   Dict  							=       None






					@property
					def   __A			( self			):
						return self.feat_extract_tester.prepare_feat_extract_dict()






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,       """feature_size"""			)			)
						self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,       """sampling_rate"""			)			)
						self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,       """padding_value"""			)			)






					def   __A			( self			):
						A__							:    int        =		self.feat_extract_tester.prepare_inputs_for_common()
						A__							:    str        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    str        =		feat_extract.model_input_names[0]

						A__							:    Dict        =		BatchFeature({input_name: speech_inputs}			)

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

						A__							:    List[str]        =		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE			)
						A__							:    List[str]        =		BatchFeature({input_name: speech_inputs}    ,       tensor_type="""np"""			)

						A__							:    Optional[Any]        =		processed_features[input_name]

						if len(batch_features_input.shape			) < 3:
							A__							:    Tuple        =		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   __A			( self			):
						A__							:    Any        =		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE			)
						A__							:    Optional[int]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Optional[Any]        =		feat_extract.model_input_names[0]

						A__							:    Optional[Any]        =		BatchFeature({input_name: speech_inputs}    ,       tensor_type="""pt"""			)

						A__							:    Tuple        =		processed_features[input_name]

						if len(batch_features_input.shape			) < 3:
							A__							:    Tuple        =		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   __A			( self			):
						A__							:    Union[str, Any]        =		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE			)
						A__							:    List[Any]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    List[Any]        =		feat_extract.model_input_names[0]

						A__							:    Any        =		BatchFeature({input_name: speech_inputs}    ,       tensor_type="""tf"""			)

						A__							:    int        =		processed_features[input_name]

						if len(batch_features_input.shape			) < 3:
							A__							:    Union[str, 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)			)






					def   __A			( self    ,       A__=False			):
						def _inputs_have_equal_length(A__			):
							A__							:    int        =		len(input[0]			)
							for input_slice in input[1:]:
								if len(__SCREAMING_SNAKE_CASE			) != length:
									return False
							return True

						def _inputs_are_equal(A__    ,       A__			):
							if len(__SCREAMING_SNAKE_CASE			) != len(__SCREAMING_SNAKE_CASE			):
								return False

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

						A__							:    Optional[Any]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Union[str, Any]        =		self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE			)
						A__							:    Any        =		feat_extract.model_input_names[0]

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

						A__							:    str        =		self.feat_extract_tester.seq_length_diff
						A__							:    int        =		self.feat_extract_tester.max_seq_length + pad_diff
						A__							:    List[Any]        =		self.feat_extract_tester.min_seq_length
						A__							:    str        =		self.feat_extract_tester.batch_size
						A__							:    Any        =		self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						A__							:    Union[str, Any]        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding=__SCREAMING_SNAKE_CASE			)
						A__							:    int        =		input_a[input_name]

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

						A__							:    List[Any]        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[-1]			)			)
						A__							:    Tuple        =		input_a[input_name]

						A__							:    Dict        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding="""longest"""    ,       return_tensors="""np"""			)
						A__							:    Dict        =		input_a[input_name]

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

						A__							:    Tuple        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=__SCREAMING_SNAKE_CASE    ,       return_tensors="""np"""			)
						A__							:    Optional[int]        =		input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
						self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
						self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
						self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			)			)
						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
						A__							:    List[str]        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       pad_to_multiple_of=10			)
						A__							:    List[Any]        =		input_a[input_name]

						A__							:    Tuple        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding="""longest"""    ,       pad_to_multiple_of=10			)
						A__							:    Union[str, Any]        =		input_a[input_name]

						A__							:    int        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       pad_to_multiple_of=10    ,       max_length=__SCREAMING_SNAKE_CASE			)
						A__							:    str        =		input_a[input_name]

						A__							:    List[Any]        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       pad_to_multiple_of=10    ,       max_length=__SCREAMING_SNAKE_CASE    ,       return_tensors="""np"""    ,       )
						A__							:    Optional[int]        =		input_a[input_name]

						self.assertTrue(all(len(__SCREAMING_SNAKE_CASE			) % 10 == 0 for x in input_a			)			)
						self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE			)			)

						A__							:    Any        =		pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
						self.assertTrue(all(len(__SCREAMING_SNAKE_CASE			) == 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
						A__							:    str        =		(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   __A			( self    ,       A__=False			):
						def _inputs_have_equal_length(A__			):
							A__							:    List[Any]        =		len(input[0]			)
							for input_slice in input[1:]:
								if len(__SCREAMING_SNAKE_CASE			) != length:
									return False
							return True

						def _inputs_are_equal(A__    ,       A__			):
							if len(__SCREAMING_SNAKE_CASE			) != len(__SCREAMING_SNAKE_CASE			):
								return False

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

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

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

						# truncate to smallest
						A__							:    Union[str, Any]        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[0]			)    ,       truncation=__SCREAMING_SNAKE_CASE			)
						A__							:    str        =		input_a[input_name]

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

						self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
						self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)

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

						A__							:    Optional[int]        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[0]			)    ,       return_tensors="""np"""			)
						A__							:    str        =		input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
						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(__SCREAMING_SNAKE_CASE			)			)

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

						A__							:    str        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[1]			)    ,       truncation=__SCREAMING_SNAKE_CASE			)
						A__							:    Dict        =		input_a[input_name]

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

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

						# 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(__SCREAMING_SNAKE_CASE			)			)
						self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						A__							:    Tuple        =		12
						A__							:    Any        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[0]			)    ,       pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,       truncation=__SCREAMING_SNAKE_CASE    ,       )
						A__							:    Tuple        =		input_a[input_name]

						A__							:    str        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=len(speech_inputs[0]			)    ,       pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,       )
						A__							:    Union[str, Any]        =		input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						A__							:    str        =		len(speech_inputs[0]			)
						if expected_length % pad_to_multiple_of != 0:
							A__							:    Optional[int]        =		((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(__SCREAMING_SNAKE_CASE			)			)
						self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)






					def   __A			( self			):
						self._check_padding(numpify=__SCREAMING_SNAKE_CASE			)






					def   __A			( self			):
						self._check_padding(numpify=__SCREAMING_SNAKE_CASE			)






					def   __A			( self			):
						self._check_truncation(numpify=__SCREAMING_SNAKE_CASE			)






					def   __A			( self			):
						self._check_truncation(numpify=__SCREAMING_SNAKE_CASE			)






					@require_torch
					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    str        =		self.feat_extract_tester.prepare_inputs_for_common()
						A__							:    List[Any]        =		feat_extract.model_input_names[0]

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

						A__							:    Optional[Any]        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding="""longest"""    ,       return_tensors="""np"""			)[input_name]
						A__							:    Optional[Any]        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       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   __A			( self			):
						A__							:    Optional[int]        =		self.feature_extraction_class(**self.feat_extract_dict			)
						A__							:    Dict        =		self.feat_extract_tester.prepare_inputs_for_common()
						A__							:    Any        =		feat_extract.model_input_names[0]

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

						A__							:    int        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       padding="""longest"""    ,       return_tensors="""np"""			)[input_name]
						A__							:    str        =		feat_extract.pad(__SCREAMING_SNAKE_CASE    ,       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   __A			( self			):
						A__							:    Tuple        =		self.feat_extract_dict
						A__							:    Any        =		True
						A__							:    Union[str, Any]        =		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE			)
						A__							:    Optional[int]        =		self.feat_extract_tester.prepare_inputs_for_common()
						A__							:    List[Any]        =		[len(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
						A__							:    Any        =		feat_extract.model_input_names[0]

						A__							:    int        =		BatchFeature({input_name: speech_inputs}			)

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






					def   __A			( self			):
						A__							:    Optional[Any]        =		self.feat_extract_dict
						A__							:    int        =		True
						A__							:    List[Any]        =		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE			)
						A__							:    List[Any]        =		self.feat_extract_tester.prepare_inputs_for_common()
						A__							:    Dict        =		[len(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
						A__							:    Tuple        =		feat_extract.model_input_names[0]

						A__							:    Tuple        =		BatchFeature({input_name: speech_inputs}			)
						A__							:    Optional[Any]        =		min(__SCREAMING_SNAKE_CASE			)

						A__							:    Dict        =		feat_extract.pad(
						    __SCREAMING_SNAKE_CASE    ,       padding="""max_length"""    ,       max_length=__SCREAMING_SNAKE_CASE    ,       truncation=__SCREAMING_SNAKE_CASE    ,       return_tensors="""np"""			)
						self.assertIn("""attention_mask"""    ,       __SCREAMING_SNAKE_CASE			)
						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]			)











"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Dict						= """Salesforce/blip-image-captioning-base"""
    SCREAMING_SNAKE_CASE						:     int						= (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    SCREAMING_SNAKE_CASE						:     Optional[int]						= """image_captioner"""
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= AutoModelForVisionaSeq

    SCREAMING_SNAKE_CASE						:     int						= ["""image"""]
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ["""text"""]






    def __init__(       self     :							Optional[Any]       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      requires_backends(self       ,						['vision']					)
      super().__init__(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							"Image"					)     ->		Dict:
      return self.pre_processor(images=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		str:
      return self.model.generate(**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Optional[Any]:
      return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE       ,						skip_special_tokens=__SCREAMING_SNAKE_CASE					)[0].strip()




