

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
							import torch

if is_vision_available():
							import PIL


lowerCAmelCase__								=		logging.get_logger(__name__)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Any	)						->				List[Any]:




	'''simple docstring'''




	def constraint_to_multiple_of(UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Optional[int]=0							,       UpperCAmelCase_							:     str=None	):
		_UpperCamelCase   :		Optional[Any]			=  round(val / multiple	) * multiple

		if max_val is not None and x > max_val:
			_UpperCamelCase   :		Optional[Any]			=  math.floor(val / multiple	) * multiple

		if x < min_val:
			_UpperCamelCase   :		Tuple			=  math.ceil(val / multiple	) * multiple

		return x

	_UpperCamelCase   :		Tuple			=  (output_size, output_size) if isinstance(lowercase__							,       lowercase__	) else output_size

	_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=  get_image_size(lowercase__	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=  output_size

	# determine new height and width
	_UpperCamelCase   :		Any			=  output_height / input_height
	_UpperCamelCase   :		Optional[int]			=  output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width	) < abs(1 - scale_height	):
			# fit width
			_UpperCamelCase   :		Any			=  scale_width
		else:
			# fit height
			_UpperCamelCase   :		Tuple			=  scale_height

	_UpperCamelCase   :		List[str]			=  constraint_to_multiple_of(scale_height * input_height							,       multiple=lowercase__	)
	_UpperCamelCase   :		List[str]			=  constraint_to_multiple_of(scale_width * input_width							,       multiple=lowercase__	)

	return (new_height, new_width)







class 	lowercase    (				__a							):


		"""simple docstring"""




		a__					       =  ["pixel_values"]


		def __init__(				self	,       __snake_case = True	,       __snake_case = None	,       __snake_case = PILImageResampling.BILINEAR	,       __snake_case = False	,       __snake_case = 1	,       __snake_case = True	,       __snake_case = 1 / 2_55	,       __snake_case = True	,       __snake_case = None	,       __snake_case = None	,       **__snake_case	,       ):
			super().__init__(**lowerCAmelCase_)
			_UpperCamelCase   :		List[str]			=  size if size is not None else {'height': 3_84, 'width': 3_84}
			_UpperCamelCase   :		Any			=  get_size_dict(lowerCAmelCase_)
			_UpperCamelCase   :		Optional[Any]			=  do_resize
			_UpperCamelCase   :		int			=  size
			_UpperCamelCase   :		List[Any]			=  keep_aspect_ratio
			_UpperCamelCase   :		Tuple			=  ensure_multiple_of
			_UpperCamelCase   :		int			=  resample
			_UpperCamelCase   :		Optional[int]			=  do_rescale
			_UpperCamelCase   :		List[Any]			=  rescale_factor
			_UpperCamelCase   :		Optional[int]			=  do_normalize
			_UpperCamelCase   :		Any			=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			_UpperCamelCase   :		Optional[int]			=  image_std if image_std is not None else IMAGENET_STANDARD_STD


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = False	,       __snake_case = 1	,       __snake_case = PILImageResampling.BICUBIC	,       __snake_case = None	,       **__snake_case	,       ):
			_UpperCamelCase   :		List[str]			=  get_size_dict(lowerCAmelCase_)
			if "height" not in size or "width" not in size:
				raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''')
			_UpperCamelCase   :		str			=  get_resize_output_image_size(
			    lowerCAmelCase_	,       output_size=(size['height'], size['width'])	,       keep_aspect_ratio=lowerCAmelCase_	,       multiple=lowerCAmelCase_	,       )
			return resize(lowerCAmelCase_	,       size=lowerCAmelCase_	,       resample=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
			return rescale(lowerCAmelCase_	,       scale=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
			return normalize(lowerCAmelCase_	,       mean=lowerCAmelCase_	,       std=lowerCAmelCase_	,       data_format=lowerCAmelCase_	,       **lowerCAmelCase_)


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = ChannelDimension.FIRST	,       **__snake_case	,       ):
			_UpperCamelCase   :		Any			=  do_resize if do_resize is not None else self.do_resize
			_UpperCamelCase   :		int			=  size if size is not None else self.size
			_UpperCamelCase   :		List[Any]			=  get_size_dict(lowerCAmelCase_)
			_UpperCamelCase   :		List[Any]			=  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
			_UpperCamelCase   :		List[Any]			=  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
			_UpperCamelCase   :		Optional[Any]			=  resample if resample is not None else self.resample
			_UpperCamelCase   :		Optional[int]			=  do_rescale if do_rescale is not None else self.do_rescale
			_UpperCamelCase   :		Any			=  rescale_factor if rescale_factor is not None else self.rescale_factor
			_UpperCamelCase   :		Optional[Any]			=  do_normalize if do_normalize is not None else self.do_normalize
			_UpperCamelCase   :		Tuple			=  image_mean if image_mean is not None else self.image_mean
			_UpperCamelCase   :		str			=  image_std if image_std is not None else self.image_std

			_UpperCamelCase   :		Tuple			=  make_list_of_images(lowerCAmelCase_)

			if not valid_images(lowerCAmelCase_):
				raise ValueError(
				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
				    'torch.Tensor, tf.Tensor or jax.ndarray.')

			if do_resize and size is None or resample is None:
				raise ValueError('Size and resample must be specified if do_resize is True.')

			if do_rescale and rescale_factor is None:
				raise ValueError('Rescale factor must be specified if do_rescale is True.')

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('Image mean and std must be specified if do_normalize is True.')

			# All transformations expect numpy arrays.
			_UpperCamelCase   :		Union[str, Any]			=  [to_numpy_array(lowerCAmelCase_) for image in images]

			if do_resize:
				_UpperCamelCase   :		Tuple			=  [self.resize(image=lowerCAmelCase_	,       size=lowerCAmelCase_	,       resample=lowerCAmelCase_) for image in images]

			if do_rescale:
				_UpperCamelCase   :		Dict			=  [self.rescale(image=lowerCAmelCase_	,       scale=lowerCAmelCase_) for image in images]

			if do_normalize:
				_UpperCamelCase   :		List[str]			=  [self.normalize(image=lowerCAmelCase_	,       mean=lowerCAmelCase_	,       std=lowerCAmelCase_) for image in images]

			_UpperCamelCase   :		Tuple			=  [to_channel_dimension_format(lowerCAmelCase_	,       lowerCAmelCase_) for image in images]

			_UpperCamelCase   :		Tuple			=  {'pixel_values': images}
			return BatchFeature(data=lowerCAmelCase_	,       tensor_type=lowerCAmelCase_)






		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		int			=  outputs.logits

			# Resize logits and compute semantic segmentation maps
			if target_sizes is not None:
				if len(lowerCAmelCase_) != len(lowerCAmelCase_):
					raise ValueError(
					    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

				if is_torch_tensor(lowerCAmelCase_):
					_UpperCamelCase   :		Tuple			=  target_sizes.numpy()

				_UpperCamelCase   :		str			=  []

				for idx in range(len(lowerCAmelCase_)):
					_UpperCamelCase   :		List[str]			=  torch.nn.functional.interpolate(
					    logits[idx].unsqueeze(dim=0)	,       size=target_sizes[idx]	,       mode='bilinear'	,       align_corners=lowerCAmelCase_)
					_UpperCamelCase   :		int			=  resized_logits[0].argmax(dim=0)
					semantic_segmentation.append(lowerCAmelCase_)
			else:
				_UpperCamelCase   :		Optional[int]			=  logits.argmax(dim=1)
				_UpperCamelCase   :		Optional[int]			=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

			return semantic_segmentation







# 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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
		    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
		    "It returns the most likely label in the list of provided `labels` for the input text."
		)
		a__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			if self.entailment_id == -1:
				raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




