




"""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       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,):
										snake_case__       :					int										=					parent
										snake_case__       :					Any										=					1_3
										snake_case__       :					str										=					7
										snake_case__       :					str										=					True
										snake_case__       :					Optional[Any]										=					True
										snake_case__       :					Union[str, Any]										=					True
										snake_case__       :					int										=					True
										snake_case__       :					Tuple										=					True
										snake_case__       :					Tuple										=					False
										snake_case__       :					Optional[int]										=					False
										snake_case__       :					List[Any]										=					False
										snake_case__       :					str										=					2
										snake_case__       :					Any										=					9_9
										snake_case__       :					Optional[int]										=					0
										snake_case__       :					List[str]										=					3_2
										snake_case__       :					Optional[Any]										=					2
										snake_case__       :					List[str]										=					4
										snake_case__       :					List[str]										=					0.1
										snake_case__       :					Tuple										=					0.1
										snake_case__       :					List[Any]										=					5_1_2
										snake_case__       :					int										=					1_6
										snake_case__       :					Optional[int]										=					2
										snake_case__       :					Union[str, Any]										=					0.02
										snake_case__       :					Union[str, Any]										=					3
										snake_case__       :					int										=					4
										snake_case__       :					Dict										=					'''last'''
										snake_case__       :					Optional[Any]										=					True
										snake_case__       :					str										=					None
										snake_case__       :					str										=					0

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					Tuple										=					ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size      )
										snake_case__       :					Tuple										=					random_attention_mask([self.batch_size, self.seq_length]      ,dtype=tf.floataa      )

										snake_case__       :					Optional[int]										=					None
										if self.use_input_lengths:
																snake_case__       :					Union[str, Any]										=					(
																    ids_tensor([self.batch_size]      ,vocab_size=2      ) + self.seq_length - 2
																)  # small variation of seq_length

										snake_case__       :					Dict										=					None
										if self.use_token_type_ids:
																snake_case__       :					Optional[int]										=					ids_tensor([self.batch_size, self.seq_length]      ,self.n_langs      )

										snake_case__       :					Tuple										=					None
										snake_case__       :					Optional[Any]										=					None
										snake_case__       :					Optional[Any]										=					None
										if self.use_labels:
																snake_case__       :					Dict										=					ids_tensor([self.batch_size]      ,self.type_sequence_label_size      )
																snake_case__       :					Any										=					ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels      )
																snake_case__       :					int										=					ids_tensor([self.batch_size]      ,2      ,dtype=tf.floataa      )
																snake_case__       :					Union[str, Any]										=					ids_tensor([self.batch_size]      ,self.num_choices      )

										snake_case__       :					List[str]										=					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       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,):
										snake_case__       :					Optional[Any]										=					TFXLMModel(config=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					int										=					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
										snake_case__       :					Any										=					model(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Union[str, Any]										=					[input_ids, input_mask]
										snake_case__       :					Any										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)      )

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,):
										snake_case__       :					Any										=					TFXLMWithLMHeadModel(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Union[str, Any]										=					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
										snake_case__       :					List[Any]										=					model(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Any										=					outputs

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

				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Dict      ,):
										snake_case__       :					List[str]										=					TFXLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Dict										=					{'''input_ids''': input_ids, '''lengths''': input_lengths}

										snake_case__       :					Tuple										=					model(SCREAMING_SNAKE_CASE_      )

										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       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Dict      ,):
										snake_case__       :					str										=					TFXLMForSequenceClassification(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Tuple										=					{'''input_ids''': input_ids, '''lengths''': input_lengths}

										snake_case__       :					str										=					model(SCREAMING_SNAKE_CASE_      )

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

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,):
										snake_case__       :					List[str]										=					self.num_labels
										snake_case__       :					Optional[int]										=					TFXLMForTokenClassification(config=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Tuple										=					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
										snake_case__       :					Tuple										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     str      ,):
										snake_case__       :					List[Any]										=					self.num_choices
										snake_case__       :					str										=					TFXLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Dict										=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_      ,1      )      ,(1, self.num_choices, 1)      )
										snake_case__       :					Union[str, Any]										=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_      ,1      )      ,(1, self.num_choices, 1)      )
										snake_case__       :					str										=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_      ,1      )      ,(1, self.num_choices, 1)      )
										snake_case__       :					Optional[Any]										=					{
										    '''input_ids''': multiple_choice_inputs_ids,
										    '''attention_mask''': multiple_choice_input_mask,
										    '''token_type_ids''': multiple_choice_token_type_ids,
										}
										snake_case__       :					List[Any]										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)      )





				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					List[str]										=					self.prepare_config_and_inputs()
										(
										    (
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	
										)       :					Tuple										=					config_and_inputs
										snake_case__       :					int										=					{
										    '''input_ids''': input_ids,
										    '''token_type_ids''': token_type_ids,
										    '''langs''': token_type_ids,
										    '''lengths''': input_lengths,
										}
										return config, inputs_dict







@require_tf
class       __lowercase       (			snake_case_						,   snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


				_lowerCAmelCase				=(
				    (
				        TFXLMModel,
				        TFXLMWithLMHeadModel,
				        TFXLMForSequenceClassification,
				        TFXLMForQuestionAnsweringSimple,
				        TFXLMForTokenClassification,
				        TFXLMForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase				=(
				    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				_lowerCAmelCase				=(
				    {
				        "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 {}
				)
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     List[str]      ):
										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       _lowerCamelCase					(   self   :     Optional[int]      ):
										snake_case__       :					Any										=					TFXLMModelTester(self      )
										snake_case__       :					Optional[int]										=					ConfigTester(self      ,config_class=SCREAMING_SNAKE_CASE_      ,emb_dim=3_7      )

				def       _lowerCamelCase					(   self   :     str      ):
										self.config_tester.run_common_tests()

				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					List[str]										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					Any										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					Tuple										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					Tuple										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     str      ):
										snake_case__       :					Any										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_for_token_classification(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Dict      ):
										snake_case__       :					Dict										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_      )





				@slow
				def       _lowerCamelCase					(   self   :     Tuple      ):
										for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case__       :					int										=					TFXLMModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







@require_tf
class       __lowercase       (			unittest.TestCase	):


				'''simple docstring'''


				@slow
				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					Union[str, Any]										=					TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''      )
										snake_case__       :					int										=					tf.convert_to_tensor([[1_4, 4_4_7]]      ,dtype=tf.intaa      )  # the president
										snake_case__       :					List[str]										=					[
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										    1_4,
										    4_4_7,
										]  # 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
										snake_case__       :					Union[str, Any]										=					model.generate(SCREAMING_SNAKE_CASE_      ,do_sample=SCREAMING_SNAKE_CASE_      )
										self.assertListEqual(output_ids[0].numpy().tolist()      ,SCREAMING_SNAKE_CASE_      )








"""simple docstring"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))


