






"""simple docstring"""



from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						List[str]			,    snake_case      :						Collection[float] | None = None							):
													'''simple docstring'''





													if components is None:
																			A__						:  Any		  =  []
													A__						:  Tuple		  =  list(snake_case							)


							def __len__(			self      :						List[str]							):
													'''simple docstring'''





													return len(self.__components							)


							def __str__(			self      :						Union[str, Any]							):
													'''simple docstring'''





													return "(" + ",".join(map(snake_case			,    self.__components							)							) + ")"


							def __add__(			self      :						Any			,    snake_case      :						Vector							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  len(self							)
													if size == len(snake_case							):
																			A__						:  int		  =  [self.__components[i] + other.component(snake_case							) for i in range(snake_case							)]
																			return Vector(snake_case							)
													else:
																			raise Exception("""must have the same size"""							)


							def __sub__(			self      :						str			,    snake_case      :						Vector							):
													'''simple docstring'''





													A__						:  Dict		  =  len(self							)
													if size == len(snake_case							):
																			A__						:  Dict		  =  [self.__components[i] - other.component(snake_case							) for i in range(snake_case							)]
																			return Vector(snake_case							)
													else:  # error case
																			raise Exception("""must have the same size"""							)


							@overload
							def __mul__(			self      :						int			,    snake_case      :						float							):
													'''simple docstring'''





													...


							@overload
							def __mul__(			self      :						List[str]			,    snake_case      :						Vector							):
													'''simple docstring'''





													...


							def __mul__(			self      :						Dict			,    snake_case      :						float | Vector							):
													'''simple docstring'''





													if isinstance(snake_case			,    (float, int)							):
																			A__						:  List[str]		  =  [c * other for c in self.__components]
																			return Vector(snake_case							)
													elif isinstance(snake_case			,    snake_case							) and len(self							) == len(snake_case							):
																			A__						:  Union[str, Any]		  =  len(self							)
																			A__						:  Union[str, Any]		  =  [self.__components[i] * other.component(snake_case							) for i in range(snake_case							)]
																			return sum(snake_case							)
													else:  # error case
																			raise Exception("""invalid operand!"""							)


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													return Vector(self.__components							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													if isinstance(snake_case			,    snake_case							) and -len(self.__components							) <= i < len(self.__components							):
																			return self.__components[i]
													else:
																			raise Exception("""index out of range"""							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int			,    snake_case      :						float							):
													'''simple docstring'''





													assert -len(self.__components							) <= pos < len(self.__components							)
													A__						:  Union[str, Any]		  =  value


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													if len(self.__components							) == 0:
																			raise Exception("""Vector is empty"""							)
													A__						:  List[Any]		  =  [c**2 for c in self.__components]
													return math.sqrt(sum(snake_case							)							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Vector			,    snake_case      :						bool = False							):
													'''simple docstring'''





													A__						:  str		  =  self * other
													A__						:  List[str]		  =  self.euclidean_length() * other.euclidean_length()
													if deg:
																			return math.degrees(math.acos(num / den							)							)
													else:
																			return math.acos(num / den							)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Vector:
						assert isinstance(UpperCAmelCase__,						UpperCAmelCase__      )
						return Vector([0] * dimension      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->Vector:
						assert isinstance(UpperCAmelCase__,						UpperCAmelCase__      ) and (isinstance(UpperCAmelCase__,						UpperCAmelCase__      ))
						A__						:  Union[str, Any]		  =  [0] * dimension
						A__						:  List[str]		  =  1
						return Vector(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					float,						UpperCAmelCase__       :					Vector,						UpperCAmelCase__       :					Vector      )		->Vector:
						assert (
						    isinstance(UpperCAmelCase__,						UpperCAmelCase__      )
						    and isinstance(UpperCAmelCase__,						UpperCAmelCase__      )
						    and (isinstance(UpperCAmelCase__,						(int, float)      ))
						)
						return x * scalar + y




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->Vector:
						random.seed(UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  [random.randint(UpperCAmelCase__,						UpperCAmelCase__      ) for _ in range(UpperCAmelCase__      )]
						return Vector(UpperCAmelCase__      )






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						str			,    snake_case      :						list[list[float]]			,    snake_case      :						int			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Any		  =  matrix
													A__						:  Optional[int]		  =  w
													A__						:  List[Any]		  =  h


							def __str__(			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  str		  =  """"""
													for i in range(self.__height							):
																			ans += "|"
																			for j in range(self.__width							):
																									if j < self.__width - 1:
																															ans += str(self.__matrix[i][j]							) + ","
																									else:
																															ans += str(self.__matrix[i][j]							) + "|\n"
													return ans


							def __add__(			self      :						Union[str, Any]			,    snake_case      :						Matrix							):
													'''simple docstring'''





													if self.__width == other.width() and self.__height == other.height():
																			A__						:  Union[str, Any]		  =  []
																			for i in range(self.__height							):
																									A__						:  Optional[Any]		  =  [
																									    self.__matrix[i][j] + other.component(snake_case			,    snake_case							)
																									    for j in range(self.__width							)
																									]
																									matrix.append(snake_case							)
																			return Matrix(snake_case			,    self.__width			,    self.__height							)
													else:
																			raise Exception("""matrix must have the same dimension!"""							)


							def __sub__(			self      :						int			,    snake_case      :						Matrix							):
													'''simple docstring'''





													if self.__width == other.width() and self.__height == other.height():
																			A__						:  List[str]		  =  []
																			for i in range(self.__height							):
																									A__						:  Any		  =  [
																									    self.__matrix[i][j] - other.component(snake_case			,    snake_case							)
																									    for j in range(self.__width							)
																									]
																									matrix.append(snake_case							)
																			return Matrix(snake_case			,    self.__width			,    self.__height							)
													else:
																			raise Exception("""matrices must have the same dimension!"""							)


							@overload
							def __mul__(			self      :						Any			,    snake_case      :						float							):
													'''simple docstring'''





													...


							@overload
							def __mul__(			self      :						List[str]			,    snake_case      :						Vector							):
													'''simple docstring'''





													...


							def __mul__(			self      :						Tuple			,    snake_case      :						float | Vector							):
													'''simple docstring'''





													if isinstance(snake_case			,    snake_case							):  # matrix-vector
																			if len(snake_case							) == self.__width:
																									A__						:  str		  =  zero_vector(self.__height							)
																									for i in range(self.__height							):
																															A__						:  List[Any]		  =  [
																															    self.__matrix[i][j] * other.component(snake_case							)
																															    for j in range(self.__width							)
																															]
																															ans.change_component(snake_case			,    sum(snake_case							)							)
																									return ans
																			else:
																									raise Exception(
																									    """vector must have the same size as the """
																									    """number of columns of the matrix!"""							)
													elif isinstance(snake_case			,    (int, float)							):  # matrix-scalar
																			A__						:  Union[str, Any]		  =  [
																			    [self.__matrix[i][j] * other for j in range(self.__width							)]
																			    for i in range(self.__height							)
																			]
																			return Matrix(snake_case			,    self.__width			,    self.__height							)
													return None


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.__height


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return self.__width


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int			,    snake_case      :						int							):
													'''simple docstring'''





													if 0 <= x < self.__height and 0 <= y < self.__width:
																			return self.__matrix[x][y]
													else:
																			raise Exception("""change_component: indices out of bounds"""							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						float							):
													'''simple docstring'''





													if 0 <= x < self.__height and 0 <= y < self.__width:
																			A__						:  Union[str, Any]		  =  value
													else:
																			raise Exception("""change_component: indices out of bounds"""							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						int			,    snake_case      :						int							):
													'''simple docstring'''





													if self.__height != self.__width:
																			raise Exception("""Matrix is not square"""							)
													A__						:  Optional[int]		  =  self.__matrix[:x] + self.__matrix[x + 1 :]
													for i in range(len(snake_case							)							):
																			A__						:  int		  =  minor[i][:y] + minor[i][y + 1 :]
													return Matrix(snake_case			,    self.__width - 1			,    self.__height - 1							).determinant()


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int			,    snake_case      :						int							):
													'''simple docstring'''





													if self.__height != self.__width:
																			raise Exception("""Matrix is not square"""							)
													if 0 <= x < self.__height and 0 <= y < self.__width:
																			return (-1) ** (x + y) * self.minor(snake_case			,    snake_case							)
													else:
																			raise Exception("""Indices out of bounds"""							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													if self.__height != self.__width:
																			raise Exception("""Matrix is not square"""							)
													if self.__height < 1:
																			raise Exception("""Matrix has no element"""							)
													elif self.__height == 1:
																			return self.__matrix[0][0]
													elif self.__height == 2:
																			return (
																			    self.__matrix[0][0] * self.__matrix[1][1]
																			    - self.__matrix[0][1] * self.__matrix[1][0]
																			)
													else:
																			A__						:  List[Any]		  =  [
																			    self.__matrix[0][y] * self.cofactor(0			,    snake_case							) for y in range(self.__width							)
																			]
																			return sum(snake_case							)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Matrix:
						A__						:  list[list[float]]		  =  [[0] * n for _ in range(UpperCAmelCase__      )]
						return Matrix(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->Matrix:
						random.seed(UpperCAmelCase__      )
						A__						:  list[list[float]]		  =  [
						    [random.randint(UpperCAmelCase__,						UpperCAmelCase__      ) for _ in range(UpperCAmelCase__      )] for _ in range(UpperCAmelCase__      )
						]
						return Matrix(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )














"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A_    =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}






@is_pipeline_test
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
							snake_case_     =							MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							snake_case_     =							TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														snake_case_     =							{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														snake_case_     =							{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}


							@require_torch
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Tuple		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""pt"""							)

													A__						:  Dict		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													A__						:  Optional[int]		  =  text_classifier("""This is great !"""			,    top_k=2							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]							)

													A__						:  Any		  =  text_classifier(["""This is great !""", """This is bad"""]			,    top_k=2							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]			,    )

													A__						:  Optional[Any]		  =  text_classifier("""This is great !"""			,    top_k=1							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													# Legacy behavior
													A__						:  str		  =  text_classifier("""This is great !"""			,    return_all_scores=snake_case							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													A__						:  int		  =  text_classifier("""This is great !"""			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]							)

													A__						:  str		  =  text_classifier(["""This is great !""", """Something else"""]			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]			,    )

													A__						:  Optional[int]		  =  text_classifier(["""This is great !""", """Something else"""]			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        {"""label""": """LABEL_0""", """score""": 0.504},
													        {"""label""": """LABEL_0""", """score""": 0.504},
													    ]			,    )


							@require_torch
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													import torch

													A__						:  Tuple		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""pt"""			,    device=torch.device("""cpu"""							)			,    )

													A__						:  List[str]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)


							@require_tf
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  str		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""tf"""							)

													A__						:  Optional[Any]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)


							@slow
							@require_torch
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  pipeline("""text-classification"""							)

													A__						:  Optional[int]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													A__						:  Optional[Any]		  =  text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													A__						:  Dict		  =  text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 0.988}]							)


							@slow
							@require_tf
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  pipeline("""text-classification"""			,    framework="""tf"""							)

													A__						:  Dict		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													A__						:  Optional[int]		  =  text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													A__						:  Tuple		  =  text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 0.988}]							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Any			,    snake_case      :						List[Any]			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  TextClassificationPipeline(model=snake_case			,    tokenizer=snake_case							)
													return text_classifier, ["HuggingFace is in", "This is another test"]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  List[str]		  =  text_classifier.model
													# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
													A__						:  int		  =  """HuggingFace is in"""
													A__						:  List[Any]		  =  text_classifier(snake_case							)

													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}]							)
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)

													A__						:  Any		  =  ["""HuggingFace is in """, """Paris is in France"""]
													A__						:  Any		  =  text_classifier(snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [{"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}, {"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}]			,    )
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)
													self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()							)

													# Forcing to get all results with `top_k=None`
													# This is NOT the legacy format
													A__						:  List[Any]		  =  text_classifier(snake_case			,    top_k=snake_case							)
													A__						:  Tuple		  =  len(model.config.idalabel.values()							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [[{"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}] * N, [{"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}] * N]			,    )

													A__						:  Optional[Any]		  =  {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
													A__						:  Union[str, Any]		  =  text_classifier(snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    {"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}			,    )
													self.assertTrue(outputs["""label"""] in model.config.idalabel.values()							)

													# This might be used a text pair, but tokenizer + pipe interaction
													# makes it hard to understand that it's not using the pair properly
													# https://github.com/huggingface/transformers/issues/17305
													# We disabled this usage instead as it was outputting wrong outputs.
													A__						:  Tuple		  =  [["""HuggingFace is in """, """Paris is in France"""]]
													with self.assertRaises(snake_case							):
																			text_classifier(snake_case							)

													# This used to be valid for doing text pairs
													# We're keeping it working because of backward compatibility
													A__						:  List[Any]		  =  text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [{"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}]			,    )
													self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()							)






