'''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.
__lowerCAmelCase	:       Tuple						       =	{"LayoutLMv2Config", "LayoutLMv3Config"}






@is_pipeline_test
class 						A   (   unittest.TestCase   ):
							a_        =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							a_        =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





							@require_torch
							def       snake_case__						(      self				:							Union[str, Any]	)							->	Dict:
								__UpperCAmelCase							=			pipeline(
								    task='''text-classification'''     ,		model='''hf-internal-testing/tiny-random-distilbert'''     ,		framework='''pt'''	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''     ,		top_k=2	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]	)

								__UpperCAmelCase							=			text_classifier(['''This is great !''', '''This is bad''']     ,		top_k=2	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								    ]     ,		)

								__UpperCAmelCase							=			text_classifier('''This is great !'''     ,		top_k=1	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]	)

								# Legacy behavior
								__UpperCAmelCase							=			text_classifier('''This is great !'''     ,		return_all_scores=__a	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''     ,		return_all_scores=__a	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]]	)

								__UpperCAmelCase							=			text_classifier(['''This is great !''', '''Something else''']     ,		return_all_scores=__a	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								    ]     ,		)

								__UpperCAmelCase							=			text_classifier(['''This is great !''', '''Something else''']     ,		return_all_scores=__a	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[
								        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
								        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
								    ]     ,		)





							@require_torch
							def       snake_case__						(      self				:							Optional[Any]	)							->	Tuple:
								import torch

								__UpperCAmelCase							=			pipeline(
								    task='''text-classification'''     ,		model='''hf-internal-testing/tiny-random-distilbert'''     ,		framework='''pt'''     ,		device=torch.device('''cpu'''	)     ,		)

								__UpperCAmelCase							=			text_classifier('''This is great !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]	)





							@require_tf
							def       snake_case__						(      self				:							int	)							->	Tuple:
								__UpperCAmelCase							=			pipeline(
								    task='''text-classification'''     ,		model='''hf-internal-testing/tiny-random-distilbert'''     ,		framework='''tf'''	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]	)





							@slow
							@require_torch
							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			pipeline('''text-classification'''	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''POSITIVE''', '''score''': 1.0}]	)
								__UpperCAmelCase							=			text_classifier('''This is bad !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''NEGATIVE''', '''score''': 1.0}]	)
								__UpperCAmelCase							=			text_classifier('''Birds are a type of animal'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]	)





							@slow
							@require_tf
							def       snake_case__						(      self				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			pipeline('''text-classification'''     ,		framework='''tf'''	)

								__UpperCAmelCase							=			text_classifier('''This is great !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''POSITIVE''', '''score''': 1.0}]	)
								__UpperCAmelCase							=			text_classifier('''This is bad !'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''NEGATIVE''', '''score''': 1.0}]	)
								__UpperCAmelCase							=			text_classifier('''Birds are a type of animal'''	)
								self.assertEqual(nested_simplify(__a	)     ,		[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]	)





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							List[Any]     ,		__a				:							Optional[int]     ,		__a				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			TextClassificationPipeline(model=__a     ,		tokenizer=__a	)
								return text_classifier, ["HuggingFace is in", "This is another test"]






							def       snake_case__						(      self				:							int     ,		__a				:							List[str]     ,		__a				:							str	)							->	Dict:
								__UpperCAmelCase							=			text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								__UpperCAmelCase							=			'''HuggingFace is in'''
								__UpperCAmelCase							=			text_classifier(__a	)

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

								__UpperCAmelCase							=			['''HuggingFace is in ''', '''Paris is in France''']
								__UpperCAmelCase							=			text_classifier(__a	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[{'''label''': ANY(__a	), '''score''': ANY(__a	)}, {'''label''': ANY(__a	), '''score''': ANY(__a	)}]     ,		)
								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
								__UpperCAmelCase							=			text_classifier(__a     ,		top_k=__a	)
								__UpperCAmelCase							=			len(model.config.idalabel.values()	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		[[{'''label''': ANY(__a	), '''score''': ANY(__a	)}] * N, [{'''label''': ANY(__a	), '''score''': ANY(__a	)}] * N]     ,		)

								__UpperCAmelCase							=			{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
								__UpperCAmelCase							=			text_classifier(__a	)
								self.assertEqual(
								    nested_simplify(__a	)     ,		{'''label''': ANY(__a	), '''score''': ANY(__a	)}     ,		)
								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.
								__UpperCAmelCase							=			[['''HuggingFace is in ''', '''Paris is in France''']]
								with self.assertRaises(__a	):
									text_classifier(__a	)

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

'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	if velocity > c:
		raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''       )
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError('''Speed must be greater than or equal to 1!'''       )

	return velocity / c





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowerCAmelCase	:       Dict						       =	transform(29_979_245)
					print("Example of four vector: ")
					print(F"""ct' = {four_vector[0]}""")
					print(F"""x' = {four_vector[1]}""")
					print(F"""y' = {four_vector[2]}""")
					print(F"""z' = {four_vector[3]}""")

					# Substitute symbols with numerical values
					__lowerCAmelCase	:       Union[str, Any]						       =	{ct: c, x: 1, y: 1, z: 1}
					__lowerCAmelCase	:       Optional[int]						       =	[four_vector[i].subs(sub_dict) for i in range(4)]

					print(F"""\n{numerical_vector}""")
