
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMModel,
						    TFXLMWithLMHeadModel,
						    XLMConfig,
						)




class    __lowerCamelCase     :




				def __init__(							self       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     parent
							lowerCamelCase_ 		=     13
							lowerCamelCase_ 		=     7
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     False
							lowerCamelCase_ 		=     False
							lowerCamelCase_ 		=     False
							lowerCamelCase_ 		=     2
							lowerCamelCase_ 		=     99
							lowerCamelCase_ 		=     0
							lowerCamelCase_ 		=     32
							lowerCamelCase_ 		=     2
							lowerCamelCase_ 		=     4
							lowerCamelCase_ 		=     0.1
							lowerCamelCase_ 		=     0.1
							lowerCamelCase_ 		=     512
							lowerCamelCase_ 		=     16
							lowerCamelCase_ 		=     2
							lowerCamelCase_ 		=     0.0_2
							lowerCamelCase_ 		=     3
							lowerCamelCase_ 		=     4
							lowerCamelCase_ 		=     '''last'''
							lowerCamelCase_ 		=     True
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     0




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )
							lowerCamelCase_ 		=     random_attention_mask([self.batch_size, self.seq_length]       ,	dtype=tf.floataa      )

							lowerCamelCase_ 		=     None
							if self.use_input_lengths:
										lowerCamelCase_ 		=     (
										    ids_tensor([self.batch_size]       ,	vocab_size=2      ) + self.seq_length - 2
										)  # small variation of seq_length

							lowerCamelCase_ 		=     None
							if self.use_token_type_ids:
										lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	self.n_langs      )

							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     None
							if self.use_labels:
										lowerCamelCase_ 		=     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size      )
										lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels      )
										lowerCamelCase_ 		=     ids_tensor([self.batch_size]       ,	2       ,	dtype=tf.floataa      )
										lowerCamelCase_ 		=     ids_tensor([self.batch_size]       ,	self.num_choices      )

							lowerCamelCase_ 		=     XLMConfig(
							    vocab_size=self.vocab_size       ,	n_special=self.n_special       ,	emb_dim=self.hidden_size       ,	n_layers=self.num_hidden_layers       ,	n_heads=self.num_attention_heads       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	gelu_activation=self.gelu_activation       ,	sinusoidal_embeddings=self.sinusoidal_embeddings       ,	asm=self.asm       ,	causal=self.causal       ,	n_langs=self.n_langs       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	summary_type=self.summary_type       ,	use_proj=self.use_proj       ,	bos_token_id=self.bos_token_id       ,	)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     TFXLMModel(config=UpperCAmelCase      )
							lowerCamelCase_ 		=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
							lowerCamelCase_ 		=     model(UpperCAmelCase      )

							lowerCamelCase_ 		=     [input_ids, input_mask]
							lowerCamelCase_ 		=     model(UpperCAmelCase      )
							self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     TFXLMWithLMHeadModel(UpperCAmelCase      )

							lowerCamelCase_ 		=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
							lowerCamelCase_ 		=     model(UpperCAmelCase      )

							lowerCamelCase_ 		=     outputs

							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     TFXLMForQuestionAnsweringSimple(UpperCAmelCase      )

							lowerCamelCase_ 		=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

							lowerCamelCase_ 		=     model(UpperCAmelCase      )

							self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     TFXLMForSequenceClassification(UpperCAmelCase      )

							lowerCamelCase_ 		=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

							lowerCamelCase_ 		=     model(UpperCAmelCase      )

							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     self.num_labels
							lowerCamelCase_ 		=     TFXLMForTokenClassification(config=UpperCAmelCase      )
							lowerCamelCase_ 		=     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
							lowerCamelCase_ 		=     model(UpperCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     self.num_choices
							lowerCamelCase_ 		=     TFXLMForMultipleChoice(config=UpperCAmelCase      )
							lowerCamelCase_ 		=     tf.tile(tf.expand_dims(UpperCAmelCase       ,	1      )       ,	(1, self.num_choices, 1)      )
							lowerCamelCase_ 		=     tf.tile(tf.expand_dims(UpperCAmelCase       ,	1      )       ,	(1, self.num_choices, 1)      )
							lowerCamelCase_ 		=     tf.tile(tf.expand_dims(UpperCAmelCase       ,	1      )       ,	(1, self.num_choices, 1)      )
							lowerCamelCase_ 		=     {
							    '''input_ids''': multiple_choice_inputs_ids,
							    '''attention_mask''': multiple_choice_input_mask,
							    '''token_type_ids''': multiple_choice_token_type_ids,
							}
							lowerCamelCase_ 		=     model(UpperCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_choices)      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   
							) 		=     config_and_inputs
							lowerCamelCase_ 		=     {
							    '''input_ids''': input_ids,
							    '''token_type_ids''': token_type_ids,
							    '''langs''': token_type_ids,
							    '''lengths''': input_lengths,
							}
							return config, inputs_dict




@require_tf
class    __lowerCamelCase     (   lowerCAmelCase	,  lowerCAmelCase	,  unittest.TestCase ):
				a__:			List[str]									=						(
				    (
				        TFXLMModel,
				        TFXLMWithLMHeadModel,
				        TFXLMForSequenceClassification,
				        TFXLMForQuestionAnsweringSimple,
				        TFXLMForTokenClassification,
				        TFXLMForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				a__:			Optional[int]									=						(
				    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				a__:			str									=						(
				    {
				        'feature-extraction': TFXLMModel,
				        'fill-mask': TFXLMWithLMHeadModel,
				        'question-answering': TFXLMForQuestionAnsweringSimple,
				        'text-classification': TFXLMForSequenceClassification,
				        'text-generation': TFXLMWithLMHeadModel,
				        'token-classification': TFXLMForTokenClassification,
				        'zero-shot': TFXLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a__:			Dict									=						False
				a__:			Dict									=						False




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith('''Fast'''      )
							):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

							return False




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     TFXLMModelTester(self      )
							lowerCamelCase_ 		=     ConfigTester(self       ,	config_class=UpperCAmelCase       ,	emb_dim=37      )




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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_token_classification(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase      )





				@slow
				def 					UpperCAmelCase__	(							self      ):
							for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase_ 		=     TFXLMModel.from_pretrained(UpperCAmelCase      )
										self.assertIsNotNone(UpperCAmelCase      )




@require_tf
class    __lowerCamelCase     (   unittest.TestCase ):




				@slow
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''      )
							lowerCamelCase_ 		=     tf.convert_to_tensor([[14, 447]]       ,	dtype=tf.intaa      )  # the president
							lowerCamelCase_ 		=     [
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							lowerCamelCase_ 		=     model.generate(UpperCAmelCase       ,	do_sample=UpperCAmelCase      )
							self.assertListEqual(output_ids[0].numpy().tolist()       ,	UpperCAmelCase      )





"""simple docstring"""


import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     abs_z_final[::-1, :]
			pyplot.matshow(abs_z_final < escape_radius	)
			pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
			pyplot.show()






def    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

						A_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				12
						A_						       =				10000.0
						A_						       =				iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0e10,
						)
						show_results("""e^z+c""", c_exponential, escape_radius, z_final)



