



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase_   )
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							int					,       **_snake_case       :							Dict       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )

								requires_backends(self					,       'vision'       )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING       )



					def __call__(  self       :							Dict					,       _snake_case       :							Union[str, List[str], "Image", List["Image"]]					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								return super().__call__(_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Tuple					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     {}
								if "candidate_labels" in kwargs:
											A__	  =     kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
											A__	  =     kwargs['hypothesis_template']

								return preprocess_params, {}, {}



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							int=None					,       _snake_case       :							Optional[Any]="This is a photo of {}."       ):






								"""simple docstring"""


								A__	  =     load_image(_snake_case       )
								A__	  =     self.image_processor(images=[image]					,       return_tensors=self.framework       )
								A__	  =     candidate_labels
								A__	  =     [hypothesis_template.format(_snake_case       ) for x in candidate_labels]
								A__	  =     self.tokenizer(_snake_case					,       return_tensors=self.framework					,       padding=_snake_case       )
								A__	  =     [text_inputs]
								return inputs



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     model_inputs.pop('candidate_labels'       )
								A__	  =     model_inputs.pop('text_inputs'       )
								if isinstance(text_inputs[0]					,       _snake_case       ):
											A__	  =     text_inputs[0]
								else:
											# Batching case.
											A__	  =     text_inputs[0][0]

								A__	  =     self.model(**_snake_case					,       **_snake_case       )

								A__	  =     {
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs



					def      _a				(  self       :							List[Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     model_outputs.pop('candidate_labels'       )
								A__	  =     model_outputs['logits'][0]
								if self.framework == "pt":
											A__	  =     logits.softmax(dim=-1       ).squeeze(-1       )
											A__	  =     probs.tolist()
											if not isinstance(_snake_case					,       _snake_case       ):
														A__	  =     [scores]
								elif self.framework == "tf":
											A__	  =     stable_softmax(_snake_case					,       axis=-1       )
											A__	  =     probs.numpy().tolist()
								else:
											raise ValueError(F'''Unsupported framework: {self.framework}'''       )

								A__	  =     [
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(_snake_case					,       _snake_case       )					,       key=lambda _snake_case       : -x[0]       )
								]
								return result









from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




