


from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
			import numpy as np
			from decord import VideoReader


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

_snake_case	:						Dict         =							logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase )
class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def __init__(				self			:    int       ,      *lowerCamelCase			:    List[Any]       ,      **lowerCamelCase			:    List[Any]							)      ->       Optional[int]:
														super().__init__(*lowerCamelCase       ,      **lowerCamelCase							)
														requires_backends(self       ,      "decord"							)
														self.check_model_type(lowerCamelCase							)

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    str=None       ,      lowerCamelCase			:    Any=None       ,      lowerCamelCase			:    Any=None							)      ->       Optional[int]:
														__snake_case						:     Any									=  {}
														if frame_sampling_rate is not None:
																					__snake_case						:     Union[str, Any]									=  frame_sampling_rate
														if num_frames is not None:
																					__snake_case						:     Optional[Any]									=  num_frames

														__snake_case						:     int									=  {}
														if top_k is not None:
																					__snake_case						:     Union[str, Any]									=  top_k
														return preprocess_params, {}, postprocess_params

							def __call__(				self			:    Tuple       ,      lowerCamelCase			:    Union[str, List[str]]       ,      **lowerCamelCase			:    Optional[int]							)      ->       Tuple:
														return super().__call__(lowerCamelCase       ,      **lowerCamelCase							)

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    Optional[Any]=None       ,      lowerCamelCase			:    Dict=1							)      ->       str:
														if num_frames is None:
																					__snake_case						:     List[str]									=  self.model.config.num_frames

														if video.startswith("http://"							) or video.startswith("https://"							):
																					__snake_case						:     Optional[Any]									=  BytesIO(requests.get(lowerCamelCase							).content							)

														__snake_case						:     Optional[int]									=  VideoReader(lowerCamelCase							)
														videoreader.seek(0							)

														__snake_case						:     Optional[Any]									=  0
														__snake_case						:     Dict									=  num_frames * frame_sampling_rate - 1
														__snake_case						:     Union[str, Any]									=  np.linspace(lowerCamelCase       ,      lowerCamelCase       ,      num=lowerCamelCase       ,      dtype=np.intaa							)

														__snake_case						:     Optional[Any]									=  videoreader.get_batch(lowerCamelCase							).asnumpy()
														__snake_case						:     Any									=  list(lowerCamelCase							)

														__snake_case						:     List[Any]									=  self.image_processor(lowerCamelCase       ,      return_tensors=self.framework							)
														return model_inputs

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    Tuple							)      ->       Tuple:
														__snake_case						:     List[str]									=  self.model(**lowerCamelCase							)
														return model_outputs






							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    int=5							)      ->       List[Any]:
														if top_k > self.model.config.num_labels:
																					__snake_case						:     str									=  self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case						:     List[str]									=  model_outputs.logits.softmax(-1							)[0]
																					__snake_case					,     __snake_case						:     Union[str, Any]									=  probs.topk(lowerCamelCase							)
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'							)

														__snake_case						:     Any									=  scores.tolist()
														__snake_case						:     Any									=  ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase       ,      lowerCamelCase							)]








def  __magic_name__  (				lowercase				)				-> list[list]:

							"""simple docstring"""



							lowercase_	:   int  				=     current_set.copy()
							for row_index, row in enumerate(lowercase				):
														lowercase_	:   Tuple  				=     row[0]
														for column_index, column in enumerate(lowercase				):
																					if magnitude == 0:
																												lowercase_	:   Optional[int]  				=     column
																												continue
																					lowercase_	:   List[str]  				=     column / magnitude
    # Subtract to cancel term
							lowercase_	:   List[str]  				=     current_set[0]
							lowercase_	:   Optional[int]  				=     [first_row]
							lowercase_	:   Optional[Any]  				=     current_set[1::]
							for row in current_set:
														lowercase_	:   Union[str, Any]  				=     []
														# If first term is 0, it is already in form we want, so we preserve it
														if row[0] == 0:
																					final_set.append(lowercase				)
																					continue
														for column_index in range(len(lowercase				)				):
																					temp_row.append(first_row[column_index] - row[column_index]				)
														final_set.append(lowercase				)
							# Create next recursion iteration set
							if len(final_set[0]				) != 3:
														lowercase_	:   Tuple  				=     final_set[0]
														lowercase_	:   Dict  				=     []
														lowercase_	:   Any  				=     []
														for row in final_set[1::]:
																					current_first_column.append(row[0]				)
																					next_iteration.append(row[1::]				)
														lowercase_	:   Optional[Any]  				=     simplify(lowercase				)
														for i in range(len(lowercase				)				):
																					resultant[i].insert(0				,       current_first_column[i]				)
														resultant.insert(0				,       lowercase				)
														lowercase_	:   Tuple  				=     resultant
							return final_set





def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if len(lowercase				) == 0:
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""				)
							lowercase_	:   Tuple  				=     len(lowercase				) + 1
							if any(len(lowercase				) != _length for item in equations				):
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""				)
							for row in equations:
														if any(not isinstance(lowercase				,       (int, float)				) for column in row				):
																					raise ValueError("""solve_simultaneous() requires lists of integers"""				)
							if len(lowercase				) == 1:
														return [equations[0][-1] / equations[0][0]]
							lowercase_	:   List[str]  				=     equations.copy()
							if any(0 in row for row in data_set				):
														lowercase_	:   int  				=     data_set.copy()
														lowercase_	:   Dict  				=     []
														for row_index, row in enumerate(lowercase				):
																					if 0 not in row:
																												lowercase_	:   Dict  				=     data_set.pop(lowercase				)
																												break
														if not full_row:
																					raise ValueError("""solve_simultaneous() requires at least 1 full equation"""				)
														data_set.insert(0				,       lowercase				)
							lowercase_	:   Tuple  				=     data_set.copy()
							lowercase_	:   List[Any]  				=     simplify(lowercase				)
							lowercase_	:   Dict  				=     simplified[::-1]
							lowercase_	:   list  				=     []
							for row in simplified:
														lowercase_	:   Union[str, Any]  				=     row[-1]
														if not solutions:
																					if row[-2] == 0:
																												solutions.append(0				)
																												continue
																					solutions.append(current_solution / row[-2]				)
																					continue
														lowercase_	:   Any  				=     row.copy()[: len(lowercase				) - 1 :]
														while temp_row[0] == 0:
																					temp_row.pop(0				)
														if len(lowercase				) == 0:
																					solutions.append(0				)
																					continue
														lowercase_	:   Any  				=     temp_row[1::]
														lowercase_	:   Union[str, Any]  				=     temp_row[::-1]
														for column_index, column in enumerate(lowercase				):
																					current_solution -= column * solutions[column_index]
														solutions.append(lowercase				)
							lowercase_	:   Any  				=     []
							for item in solutions:
														final.append(float(round(lowercase				,       5				)				)				)
							return final[::-1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCAmelCase_							=				[
							    [2, 1, 1, 1, 1, 4],
							    [1, 2, 1, 1, 1, 5],
							    [1, 1, 2, 1, 1, 6],
							    [1, 1, 1, 2, 1, 7],
							    [1, 1, 1, 1, 2, 8],
							]
							print(solve_simultaneous(eq))
							print(solve_simultaneous([[4, 2]]))