





"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DeformableDetrImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=7		,     _UpperCAmelCase=3		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=True		,     _UpperCAmelCase=1 / 255		,     _UpperCAmelCase=True		,     ):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									__a	:  Dict         =					size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
									__a	:  int         =					parent
									__a	:  Tuple         =					batch_size
									__a	:  Tuple         =					num_channels
									__a	:  Union[str, Any]         =					min_resolution
									__a	:  Tuple         =					max_resolution
									__a	:  int         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  Optional[Any]         =					do_normalize
									__a	:  int         =					image_mean
									__a	:  int         =					image_std
									__a	:  Any         =					do_rescale
									__a	:  Tuple         =					rescale_factor
									__a	:  Union[str, Any]         =					do_pad







					def 	_lowerCamelCase						(  self       ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=False       ):
									if not batched:
													__a	:  str         =					image_inputs[0]
													if isinstance(__lowerCAmelCase		,     Image.Image       ):
																	__a    ,  __a	:  Optional[Any]         =					image.size
													else:
																	__a    ,  __a	:  Tuple         =					image.shape[1], image.shape[2]
													if w < h:
																	__a	:  Any         =					int(self.size['''shortest_edge'''] * h / w       )
																	__a	:  str         =					self.size['''shortest_edge''']
													elif w > h:
																	__a	:  List[str]         =					self.size['''shortest_edge''']
																	__a	:  List[Any]         =					int(self.size['''shortest_edge'''] * w / h       )
													else:
																	__a	:  Any         =					self.size['''shortest_edge''']
																	__a	:  Tuple         =					self.size['''shortest_edge''']

									else:
													__a	:  List[str]         =					[]
													for image in image_inputs:
																	__a    ,  __a	:  Union[str, Any]         =					self.get_expected_values([image]       )
																	expected_values.append((expected_height, expected_width)       )
													__a	:  Optional[int]         =					max(__lowerCAmelCase		,     key=lambda _UpperCAmelCase       : item[0]       )[0]
													__a	:  Optional[Any]         =					max(__lowerCAmelCase		,     key=lambda _UpperCAmelCase       : item[1]       )[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 			__lowercase     (     _a				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					DeformableDetrImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					DeformableDetrImageProcessingTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''image_mean'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''image_std'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''do_normalize'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''do_rescale'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''do_pad'''       )       )
									self.assertTrue(hasattr(__lowerCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size		,     {'''shortest_edge''': 18, '''longest_edge''': 1333}       )
									self.assertEqual(image_processor.do_pad		,     __lowerCAmelCase       )

									__a	:  Tuple         =					self.image_processing_class.from_dict(
									    self.image_processor_dict		,     size=42		,     max_size=84		,     pad_and_return_pixel_mask=__lowerCAmelCase       )
									self.assertEqual(image_processor.size		,     {'''shortest_edge''': 42, '''longest_edge''': 84}       )
									self.assertEqual(image_processor.do_pad		,     __lowerCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processing
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  List[str]         =					prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Any         =					image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

									__a    ,  __a	:  List[str]         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

									self.assertEqual(
									    encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

									# Test batched
									__a    ,  __a	:  Dict         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase       )

									__a	:  Dict         =					image_processing(__lowerCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processing
									__a	:  List[Any]         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  Dict         =					prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     numpify=__lowerCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  str         =					image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

									__a    ,  __a	:  Union[str, Any]         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

									self.assertEqual(
									    encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

									# Test batched
									__a	:  Tuple         =					image_processing(__lowerCAmelCase		,     return_tensors='''pt'''       ).pixel_values

									__a    ,  __a	:  Union[str, Any]         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase       )

									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processing
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  Optional[int]         =					prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     torchify=__lowerCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  List[str]         =					image_processing(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values

									__a    ,  __a	:  Any         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

									self.assertEqual(
									    encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

									# Test batched
									__a	:  Union[str, Any]         =					image_processing(__lowerCAmelCase		,     return_tensors='''pt'''       ).pixel_values

									__a    ,  __a	:  int         =					self.image_processor_tester.get_expected_values(__lowerCAmelCase		,     batched=__lowerCAmelCase       )

									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,     )







					@slow
					def 	_lowerCamelCase						(  self       ):
									# prepare image and target
									__a	:  Tuple         =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,     '''r'''       ) as f:
													__a	:  List[Any]         =					json.loads(f.read()       )

									__a	:  Optional[Any]         =					{'''image_id''': 39769, '''annotations''': target}

									# encode them
									__a	:  List[Any]         =					DeformableDetrImageProcessor()
									__a	:  Dict         =					image_processing(images=__lowerCAmelCase		,     annotations=__lowerCAmelCase		,     return_tensors='''pt'''       )

									# verify pixel values
									__a	:  Any         =					torch.Size([1, 3, 800, 1066]       )
									self.assertEqual(encoding['''pixel_values'''].shape		,     __lowerCAmelCase       )

									__a	:  List[Any]         =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,     __lowerCAmelCase		,     atol=1e-4       )       )

									# verify area
									__a	:  Optional[Any]         =					torch.tensor([5_8_8_7.9_6_0_0, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,     __lowerCAmelCase       )       )
									# verify boxes
									__a	:  Any         =					torch.Size([6, 4]       )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,     __lowerCAmelCase       )
									__a	:  Dict         =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,     __lowerCAmelCase		,     atol=1e-3       )       )
									# verify image_id
									__a	:  int         =					torch.tensor([39769]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,     __lowerCAmelCase       )       )
									# verify is_crowd
									__a	:  Tuple         =					torch.tensor([0, 0, 0, 0, 0, 0]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,     __lowerCAmelCase       )       )
									# verify class_labels
									__a	:  List[Any]         =					torch.tensor([75, 75, 63, 65, 17, 17]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,     __lowerCAmelCase       )       )
									# verify orig_size
									__a	:  Optional[int]         =					torch.tensor([480, 640]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,     __lowerCAmelCase       )       )
									# verify size
									__a	:  Any         =					torch.tensor([800, 1066]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,     __lowerCAmelCase       )       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									# prepare image, target and masks_path
									__a	:  str         =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
									with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		,     '''r'''       ) as f:
													__a	:  Dict         =					json.loads(f.read()       )

									__a	:  str         =					{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

									__a	:  List[Any]         =					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

									# encode them
									__a	:  List[str]         =					DeformableDetrImageProcessor(format='''coco_panoptic'''       )
									__a	:  Dict         =					image_processing(images=__lowerCAmelCase		,     annotations=__lowerCAmelCase		,     masks_path=__lowerCAmelCase		,     return_tensors='''pt'''       )

									# verify pixel values
									__a	:  Optional[Any]         =					torch.Size([1, 3, 800, 1066]       )
									self.assertEqual(encoding['''pixel_values'''].shape		,     __lowerCAmelCase       )

									__a	:  Optional[Any]         =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,     __lowerCAmelCase		,     atol=1e-4       )       )

									# verify area
									__a	:  Union[str, Any]         =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,     __lowerCAmelCase       )       )
									# verify boxes
									__a	:  Union[str, Any]         =					torch.Size([6, 4]       )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,     __lowerCAmelCase       )
									__a	:  Dict         =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,     __lowerCAmelCase		,     atol=1e-3       )       )
									# verify image_id
									__a	:  Dict         =					torch.tensor([39769]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,     __lowerCAmelCase       )       )
									# verify is_crowd
									__a	:  str         =					torch.tensor([0, 0, 0, 0, 0, 0]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,     __lowerCAmelCase       )       )
									# verify class_labels
									__a	:  Any         =					torch.tensor([17, 17, 63, 75, 75, 93]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,     __lowerCAmelCase       )       )
									# verify masks
									__a	:  Union[str, Any]         =					822873
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,     __lowerCAmelCase       )
									# verify orig_size
									__a	:  Optional[int]         =					torch.tensor([480, 640]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,     __lowerCAmelCase       )       )
									# verify size
									__a	:  Dict         =					torch.tensor([800, 1066]       )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,     __lowerCAmelCase       )       )







"""simple docstring"""






import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf




class 			__lowercase     (     enum.Enum							):







					'''simple docstring'''




					__lowerCAmelCase					=					0
					__lowerCAmelCase					=					1
					__lowerCAmelCase					=					2






@add_end_docstrings(_UpperCamelCase							)
class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )
									self.check_model_type(
									    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING       )
									if "prefix" not in self._preprocess_params:
													# This is very specific. The logic is quite complex and needs to be done
													# as a "default".
													# It also defines both some preprocess_kwargs and generate_kwargs
													# which is why we cannot put them in their respective methods.
													__a	:  List[Any]         =					None
													if self.model.config.prefix is not None:
																	__a	:  Tuple         =					self.model.config.prefix
													if prefix is None and self.model.__class__.__name__ in [
													    "XLNetLMHeadModel",
													    "TransfoXLLMHeadModel",
													    "TFXLNetLMHeadModel",
													    "TFTransfoXLLMHeadModel",
													]:
																	# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																	__a	:  Tuple         =					self.XL_PREFIX
													if prefix is not None:
																	# Recalculate some generate_kwargs linked to prefix.
																	__a    ,  __a    ,  __a	:  Optional[int]         =					self._sanitize_parameters(prefix=_UpperCAmelCase		,     **self._forward_params       )
																	__a	:  Dict         =					{**self._preprocess_params, **preprocess_params}
																	__a	:  str         =					{**self._forward_params, **forward_params}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase		,     ):
									__a	:  str         =					{}
									if prefix is not None:
													__a	:  Tuple         =					prefix
									if prefix:
													__a	:  str         =					self.tokenizer(
													    _UpperCAmelCase		,     padding=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase		,     return_tensors=self.framework       )
													__a	:  List[str]         =					prefix_inputs['''input_ids'''].shape[-1]

									if handle_long_generation is not None:
													if handle_long_generation not in {"hole"}:
																	raise ValueError(
																	    f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
																	    ''' [None, \'hole\']'''       )
													__a	:  str         =					handle_long_generation

									preprocess_params.update(_UpperCAmelCase       )
									__a	:  int         =					generate_kwargs

									__a	:  List[Any]         =					{}
									if return_full_text is not None and return_type is None:
													if return_text is not None:
																	raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''       )
													if return_tensors is not None:
																	raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''       )
													__a	:  Optional[Any]         =					ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
									if return_tensors is not None and return_type is None:
													if return_text is not None:
																	raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''       )
													__a	:  List[Any]         =					ReturnType.TENSORS
									if return_type is not None:
													__a	:  Union[str, Any]         =					return_type
									if clean_up_tokenization_spaces is not None:
													__a	:  Optional[int]         =					clean_up_tokenization_spaces

									if stop_sequence is not None:
													__a	:  Any         =					self.tokenizer.encode(_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase       )
													if len(_UpperCAmelCase       ) > 1:
																	warnings.warn(
																	    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
																	    ''' the stop sequence will be used as the stop sequence string in the interim.'''       )
													__a	:  List[str]         =					stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									# Parse arguments
									if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
													kwargs.update({'''add_space_before_punct_symbol''': True}       )

									return super()._parse_and_tokenize(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def __call__(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									return super().__call__(_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=""		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Tuple         =					self.tokenizer(
									    prefix + prompt_text		,     padding=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase		,     return_tensors=self.framework       )
									__a	:  int         =					prompt_text

									if handle_long_generation == "hole":
													__a	:  str         =					inputs['''input_ids'''].shape[-1]
													if "max_new_tokens" in generate_kwargs:
																	__a	:  Tuple         =					generate_kwargs['''max_new_tokens''']
													else:
																	__a	:  List[Any]         =					generate_kwargs.get('''max_length'''		,     self.model.config.max_length       ) - cur_len
																	if new_tokens < 0:
																					raise ValueError('''We cannot infer how many new tokens are expected'''       )
													if cur_len + new_tokens > self.tokenizer.model_max_length:
																	__a	:  Dict         =					self.tokenizer.model_max_length - new_tokens
																	if keep_length <= 0:
																					raise ValueError(
																					    '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
																					    ''' models max length'''       )

																	__a	:  int         =					inputs['''input_ids'''][:, -keep_length:]
																	if "attention_mask" in inputs:
																					__a	:  List[str]         =					inputs['''attention_mask'''][:, -keep_length:]

									return inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									__a	:  str         =					model_inputs['''input_ids''']
									__a	:  Dict         =					model_inputs.get('''attention_mask'''		,     _UpperCAmelCase       )
									# Allow empty prompts
									if input_ids.shape[1] == 0:
													__a	:  str         =					None
													__a	:  List[Any]         =					None
													__a	:  Any         =					1
									else:
													__a	:  List[Any]         =					input_ids.shape[0]
									__a	:  str         =					model_inputs.pop('''prompt_text'''       )

									# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
									# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
									__a	:  List[str]         =					generate_kwargs.pop('''prefix_length'''		,     0       )
									if prefix_length > 0:
													__a	:  str         =					'''max_new_tokens''' in generate_kwargs or (
													    '''generation_config''' in generate_kwargs
													    and generate_kwargs['''generation_config'''].max_new_tokens is not None
													)
													if not has_max_new_tokens:
																	__a	:  Tuple         =					generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
																	generate_kwargs["max_length"] += prefix_length
													__a	:  Dict         =					'''min_new_tokens''' in generate_kwargs or (
													    '''generation_config''' in generate_kwargs
													    and generate_kwargs['''generation_config'''].min_new_tokens is not None
													)
													if not has_min_new_tokens and "min_length" in generate_kwargs:
																	generate_kwargs["min_length"] += prefix_length

        # BS x SL
									__a	:  List[str]         =					self.model.generate(input_ids=_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     **_UpperCAmelCase       )
									__a	:  int         =					generated_sequence.shape[0]
									if self.framework == "pt":
													__a	:  Union[str, Any]         =					generated_sequence.reshape(_UpperCAmelCase		,     out_b // in_b		,     *generated_sequence.shape[1:]       )
									elif self.framework == "tf":
													__a	:  Dict         =					tf.reshape(_UpperCAmelCase		,     (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
									return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=ReturnType.FULL_TEXT		,     _UpperCAmelCase=True       ):
									__a	:  Optional[Any]         =					model_outputs['''generated_sequence'''][0]
									__a	:  List[str]         =					model_outputs['''input_ids''']
									__a	:  Optional[Any]         =					model_outputs['''prompt_text''']
									__a	:  str         =					generated_sequence.numpy().tolist()
									__a	:  List[Any]         =					[]
									for sequence in generated_sequence:
													if return_type == ReturnType.TENSORS:
																	__a	:  Union[str, Any]         =					{'''generated_token_ids''': sequence}
													elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																	# Decode text
																	__a	:  List[str]         =					self.tokenizer.decode(
																	    _UpperCAmelCase		,     skip_special_tokens=_UpperCAmelCase		,     clean_up_tokenization_spaces=_UpperCAmelCase		,     )

																	# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																	if input_ids is None:
																					__a	:  Dict         =					0
																	else:
																					__a	:  Any         =					len(
																					    self.tokenizer.decode(
																					        input_ids[0]		,     skip_special_tokens=_UpperCAmelCase		,     clean_up_tokenization_spaces=_UpperCAmelCase		,     )       )

																	if return_type == ReturnType.FULL_TEXT:
																					__a	:  Any         =					prompt_text + text[prompt_length:]
																	else:
																					__a	:  Any         =					text[prompt_length:]

																	__a	:  Dict         =					{'''generated_text''': all_text}
													records.append(_UpperCAmelCase       )

									return records