
'''simple docstring'''


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					def is_in_circle(_lowercase			,       _lowercase				) -> bool:
										lowerCamelCase_					:      List[str]     =		sqrt((x**2) + (y**2)				)
										# Our circle has a radius of 1, so a distance
										# greater than 1 would land outside the circle.
										return distance_from_centre <= 1

					# The proportion of guesses that landed in the circle
					lowerCamelCase_					:      Tuple     =		mean(
					    int(is_in_circle(uniform(-1.0			,       1.0				)			,       uniform(-1.0			,       1.0				)				)				)
					    for _ in range(_lowercase				)				)
					# The ratio of the area for circle to square is pi/4.
					lowerCamelCase_					:      Tuple     =		proportion * 4
					print(F"""The estimated value of pi is {pi_estimate}"""				)
					print(F"""The numpy value of pi is {pi}"""				)
					print(F"""The total error is {abs(pi - pi_estimate				)}"""				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase = 0.0			,       _lowercase = 1.0			,       )     ->							float:
					'''simple docstring'''

					return mean(
					    function_to_integrate(uniform(_lowercase			,       _lowercase				)				) for _ in range(_lowercase				)				) * (max_value - min_value)




def lowercase_	(   _lowercase			,       _lowercase = 0.0			,       _lowercase = 1.0				)     ->							None:
					'''simple docstring'''

					def identity_function(_lowercase				) -> float:
										return x

					lowerCamelCase_					:      Tuple     =		area_under_curve_estimator(
					    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
					lowerCamelCase_					:      Optional[Any]     =		(max_value * max_value - min_value * min_value) / 2

					print('''******************'''				)
					print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""				)
					print(F"""Estimated value is {estimated_value}"""				)
					print(F"""Expected value is {expected_value}"""				)
					print(F"""Total error is {abs(estimated_value - expected_value				)}"""				)
					print('''******************'''				)




def lowercase_	(   _lowercase				)     ->							None:
					'''simple docstring'''

					def function_to_integrate(_lowercase				) -> float:
										return sqrt(4.0 - x * x				)

					lowerCamelCase_					:      Union[str, Any]     =		area_under_curve_estimator(
					    _lowercase			,       _lowercase			,       0.0			,       2.0				)

					print('''******************'''				)
					print('''Estimating pi using area_under_curve_estimator'''				)
					print(F"""Estimated value is {estimated_value}"""				)
					print(F"""Expected value is {pi}"""				)
					print(F"""Total error is {abs(estimated_value - pi				)}"""				)
					print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




'''simple docstring'''


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFTransfoXLForSequenceClassification,
					    TFTransfoXLLMHeadModel,
					    TFTransfoXLModel,
					)

class 	__lowercase				:

			def __init__(self			,   A			,   ):
								lowerCamelCase_					:      int     =		parent
								lowerCamelCase_					:      Dict     =		1_3
								lowerCamelCase_					:      Any     =		7
								lowerCamelCase_					:      Dict     =		3_0
								lowerCamelCase_					:      Optional[Any]     =		self.seq_length + self.mem_len
								lowerCamelCase_					:      Tuple     =		1_5
								lowerCamelCase_					:      Tuple     =		True
								lowerCamelCase_					:      List[Any]     =		True
								lowerCamelCase_					:      Dict     =		9_9
								lowerCamelCase_					:      Any     =		[1_0, 5_0, 8_0]
								lowerCamelCase_					:      List[str]     =		3_2
								lowerCamelCase_					:      Tuple     =		3_2
								lowerCamelCase_					:      Optional[Any]     =		4
								lowerCamelCase_					:      Union[str, Any]     =		8
								lowerCamelCase_					:      Dict     =		1_2_8
								lowerCamelCase_					:      Dict     =		2
								lowerCamelCase_					:      Tuple     =		2
								lowerCamelCase_					:      Any     =		None
								lowerCamelCase_					:      Optional[Any]     =		1
								lowerCamelCase_					:      Union[str, Any]     =		0
								lowerCamelCase_					:      str     =		3
								lowerCamelCase_					:      Dict     =		self.vocab_size - 1
								lowerCamelCase_					:      Dict     =		0.01

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )
								lowerCamelCase_					:      Optional[int]     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

								lowerCamelCase_					:      Union[str, Any]     =		None
								if self.use_labels:
													lowerCamelCase_					:      Tuple     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

								lowerCamelCase_					:      str     =		TransfoXLConfig(
								    vocab_size=self.vocab_size			,   mem_len=self.mem_len			,   clamp_len=self.clamp_len			,   cutoffs=self.cutoffs			,   d_model=self.hidden_size			,   d_embed=self.d_embed			,   n_head=self.num_attention_heads			,   d_head=self.d_head			,   d_inner=self.d_inner			,   div_val=self.div_val			,   n_layer=self.num_hidden_layers			,   eos_token_id=self.eos_token_id			,   pad_token_id=self.vocab_size - 1			,   init_range=self.init_range			,   num_labels=self.num_labels			,   )

								return (config, input_ids_a, input_ids_a, lm_labels)

			def        UpperCAmelCase__ (self      ):
								random.seed(self.seed      )
								tf.random.set_seed(self.seed      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      int     =		TFTransfoXLModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

								lowerCamelCase_,      lowerCamelCase_					:      Any     =		model(A      ).to_tuple()

								self.parent.assertEqual(hidden_states_a.shape			,   (self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertEqual(hidden_states_a.shape			,   (self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]			,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,   )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]			,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,   )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      List[Any]     =		TFTransfoXLLMHeadModel(A      )

								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		model(A      ).to_tuple()

								lowerCamelCase_					:      Optional[int]     =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		model(A      ).to_tuple()

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		model([input_ids_a, mems_a]      ).to_tuple()

								lowerCamelCase_					:      List[str]     =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

								lowerCamelCase_,      lowerCamelCase_					:      str     =		model(A      ).to_tuple()

								self.parent.assertEqual(lm_logits_a.shape			,   (self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]			,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,   )

								self.parent.assertEqual(lm_logits_a.shape			,   (self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]			,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,   )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		TFTransfoXLForSequenceClassification(A      )
								lowerCamelCase_					:      Optional[int]     =		model(A      )
								self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.prepare_config_and_inputs()
								((lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_),      (lowerCamelCase_))					:      List[Any]     =		config_and_inputs
								lowerCamelCase_					:      Tuple     =		{'''input_ids''': input_ids_a}
								return config, inputs_dict

@require_tf
class 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Any	      =			(
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			lowerCamelCase			:		Tuple	      =			() if is_tf_available() else ()
			lowerCamelCase			:		Any	      =			(
			    {
			        "feature-extraction": TFTransfoXLModel,
			        "text-classification": TFTransfoXLForSequenceClassification,
			        "text-generation": TFTransfoXLLMHeadModel,
			        "zero-shot": TFTransfoXLForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
			lowerCamelCase			:		str	      =			False
			lowerCamelCase			:		Optional[int]	      =			False
			lowerCamelCase			:		Dict	      =			False
			lowerCamelCase			:		Optional[int]	      =			False

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A			,   A      ):
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
													# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
													# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
													# tokenizer.
													return True

								return False

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		TFTransfoXLModelTester(self      )
								lowerCamelCase_					:      Optional[Any]     =		ConfigTester(self			,   config_class=A			,   d_embed=3_7      )

			def        UpperCAmelCase__ (self      ):
								self.config_tester.run_common_tests()

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      List[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*A      )

			def        UpperCAmelCase__ (self      ):
								self.model_tester.set_seed()
								lowerCamelCase_					:      Optional[Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase_					:      List[Any]     =		[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      Optional[Any]     =		model_class(A      )
													assert isinstance(model.get_input_embeddings()			,   tf.keras.layers.Layer      )
													if model_class in list_other_models_with_output_ebd:
																		lowerCamelCase_					:      str     =		model.get_output_embeddings()
																		assert isinstance(A			,   tf.keras.layers.Layer      )
																		lowerCamelCase_					:      int     =		model.get_bias()
																		assert name is None
													else:
																		lowerCamelCase_					:      Tuple     =		model.get_output_embeddings()
																		assert x is None
																		lowerCamelCase_					:      int     =		model.get_bias()
																		assert name is None

			def        UpperCAmelCase__ (self      ):
								# TODO JP: Make TransfoXL XLA compliant
								pass

			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Optional[Any]     =		TFTransfoXLModel.from_pretrained(A      )
													self.assertIsNotNone(A      )






			@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
			def        UpperCAmelCase__ (self      ):
								pass

@require_tf
class 	__lowercase				(  unittest.TestCase    ):

			@unittest.skip('''Skip test until #12651 is resolved.'''      )
			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
								# fmt: off
								lowerCamelCase_					:      Optional[Any]     =		tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]			,   dtype=tf.intaa      )  # noqa: E231
								# fmt: on
								#  In 1991 , the remains of Russian Tsar Nicholas II and his family
								#  ( except for Alexei and Maria ) are discovered .
								#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
								#  remainder of the story . 1883 Western Siberia ,
								#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
								#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
								#  father initially slaps him for making such an accusation , Rasputin watches as the
								#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
								#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
								#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

								# fmt: off
								lowerCamelCase_					:      Union[str, Any]     =		[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
								# fmt: on
								#  In 1991, the remains of Russian Tsar Nicholas II and his family (
								#  except for Alexei and Maria ) are discovered. The voice of young son,
								#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
								#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
								#  and a group of men to perform magic. Rasputin has a vision and
								#  denounces one of the men as a horse thief. Although his father initially
								#  slaps him for making such an accusation, Rasputin watches as the man
								#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
								#  of the Virgin Mary, prompting him to become a priest.
								#  Rasputin quickly becomes famous, with people, even a bishop, begging for
								#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
								# Nicholas II and his family were discovered. The voice of <unk> young son,
								# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

								lowerCamelCase_					:      Optional[Any]     =		model.generate(A			,   max_length=2_0_0			,   do_sample=A      )
								self.assertListEqual(output_ids[0].numpy().tolist()			,   A      )


