



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 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[str]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     parent
					__UpperCAmelCase   :					Tuple													=     13
					__UpperCAmelCase   :					List[Any]													=     7
					__UpperCAmelCase   :					Union[str, Any]													=     True
					__UpperCAmelCase   :					Optional[int]													=     True
					__UpperCAmelCase   :					Optional[Any]													=     True
					__UpperCAmelCase   :					Dict													=     True
					__UpperCAmelCase   :					Optional[Any]													=     True
					__UpperCAmelCase   :					Any													=     False
					__UpperCAmelCase   :					Any													=     False
					__UpperCAmelCase   :					Tuple													=     False
					__UpperCAmelCase   :					Union[str, Any]													=     2
					__UpperCAmelCase   :					List[Any]													=     99
					__UpperCAmelCase   :					str													=     0
					__UpperCAmelCase   :					Dict													=     32
					__UpperCAmelCase   :					List[str]													=     2
					__UpperCAmelCase   :					Optional[Any]													=     4
					__UpperCAmelCase   :					List[str]													=     0.1
					__UpperCAmelCase   :					Any													=     0.1
					__UpperCAmelCase   :					Tuple													=     5_12
					__UpperCAmelCase   :					str													=     16
					__UpperCAmelCase   :					Dict													=     2
					__UpperCAmelCase   :					Dict													=     0.0_2
					__UpperCAmelCase   :					Any													=     3
					__UpperCAmelCase   :					List[Any]													=     4
					__UpperCAmelCase   :					str													=     '''last'''
					__UpperCAmelCase   :					Optional[int]													=     True
					__UpperCAmelCase   :					List[Any]													=     None
					__UpperCAmelCase   :					str													=     0




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





					__UpperCAmelCase   :					Optional[int]													=     ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size			)
					__UpperCAmelCase   :					Optional[Any]													=     random_attention_mask([self.batch_size, self.seq_length]	,	dtype=tf.floataa			)

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

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

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

					__UpperCAmelCase   :					Any													=     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 			snake_case__					(       self							:			Optional[Any]	,	a_							:			int	,	a_							:			Union[str, Any]	,	a_							:			List[str]	,	a_							:			List[str]	,	a_							:			List[str]	,	a_							:			Any	,	a_							:			List[str]	,	a_							:			Dict	,	a_							:			Dict	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     TFXLMModel(config=a_			)
					__UpperCAmelCase   :					Tuple													=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
					__UpperCAmelCase   :					Tuple													=     model(a_			)

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




			def 			snake_case__					(       self							:			int	,	a_							:			Optional[int]	,	a_							:			int	,	a_							:			Any	,	a_							:			Union[str, Any]	,	a_							:			str	,	a_							:			Tuple	,	a_							:			str	,	a_							:			Any	,	a_							:			Union[str, Any]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     TFXLMWithLMHeadModel(a_			)

					__UpperCAmelCase   :					int													=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
					__UpperCAmelCase   :					Optional[int]													=     model(a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     outputs

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




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Dict	,	a_							:			Tuple	,	a_							:			str	,	a_							:			Dict	,	a_							:			Dict	,	a_							:			List[Any]	,	a_							:			List[Any]	,	a_							:			Tuple	,	a_							:			int	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     TFXLMForQuestionAnsweringSimple(a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

					__UpperCAmelCase   :					Tuple													=     model(a_			)

					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 			snake_case__					(       self							:			List[str]	,	a_							:			Dict	,	a_							:			int	,	a_							:			int	,	a_							:			int	,	a_							:			Tuple	,	a_							:			Dict	,	a_							:			str	,	a_							:			Union[str, Any]	,	a_							:			Any	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     TFXLMForSequenceClassification(a_			)

					__UpperCAmelCase   :					Optional[int]													=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

					__UpperCAmelCase   :					Union[str, Any]													=     model(a_			)

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




			def 			snake_case__					(       self							:			int	,	a_							:			Optional[Any]	,	a_							:			Optional[Any]	,	a_							:			Union[str, Any]	,	a_							:			Union[str, Any]	,	a_							:			Dict	,	a_							:			Tuple	,	a_							:			List[str]	,	a_							:			Optional[Any]	,	a_							:			int	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     self.num_labels
					__UpperCAmelCase   :					List[str]													=     TFXLMForTokenClassification(config=a_			)
					__UpperCAmelCase   :					List[str]													=     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					__UpperCAmelCase   :					int													=     model(a_			)
					self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels)			)




			def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			Dict	,	a_							:			List[Any]	,	a_							:			Union[str, Any]	,	a_							:			Tuple	,	a_							:			Dict	,	a_							:			Any	,	a_							:			Optional[Any]	,	a_							:			Dict	,	a_							:			List[Any]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     self.num_choices
					__UpperCAmelCase   :					Tuple													=     TFXLMForMultipleChoice(config=a_			)
					__UpperCAmelCase   :					Dict													=     tf.tile(tf.expand_dims(a_	,	1			)	,	(1, self.num_choices, 1)			)
					__UpperCAmelCase   :					Dict													=     tf.tile(tf.expand_dims(a_	,	1			)	,	(1, self.num_choices, 1)			)
					__UpperCAmelCase   :					Dict													=     tf.tile(tf.expand_dims(a_	,	1			)	,	(1, self.num_choices, 1)			)
					__UpperCAmelCase   :					Dict													=     {
					    '''input_ids''': multiple_choice_inputs_ids,
					    '''attention_mask''': multiple_choice_input_mask,
					    '''token_type_ids''': multiple_choice_token_type_ids,
					}
					__UpperCAmelCase   :					int													=     model(a_			)
					self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_choices)			)




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     self.prepare_config_and_inputs()
					(
					    (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, (
					    __UpperCAmelCase
					)							, 
					)   :					List[Any]													=     config_and_inputs
					__UpperCAmelCase   :					Union[str, Any]													=     {
					    '''input_ids''': input_ids,
					    '''token_type_ids''': token_type_ids,
					    '''langs''': token_type_ids,
					    '''lengths''': input_lengths,
					}
					return config, inputs_dict

@require_tf
class 		UpperCAmelCase__      (      __UpperCamelCase						,__UpperCamelCase						,unittest.TestCase       ):




			'''simple docstring'''






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




			def 			snake_case__					(       self							:			List[str]	,	a_							:			Union[str, Any]	,	a_							:			str	,	a_							:			Optional[Any]	,	a_							:			List[Any]	,	a_							:			int			):
					'''simple docstring'''





					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 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     TFXLMModelTester(self			)
					__UpperCAmelCase   :					List[Any]													=     ConfigTester(self	,	config_class=a_	,	emb_dim=37			)




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					self.config_tester.run_common_tests()




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





					__UpperCAmelCase   :					Union[str, Any]													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_model(*a_			)




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





					__UpperCAmelCase   :					Optional[int]													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_lm_head(*a_			)




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





					__UpperCAmelCase   :					Tuple													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_qa(*a_			)




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_sequence_classif(*a_			)




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





					__UpperCAmelCase   :					Any													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_for_token_classification(*a_			)




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





					__UpperCAmelCase   :					Optional[Any]													=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_for_multiple_choice(*a_			)




			@slow
			def 			snake_case__					(       self							:			Dict			):
					'''simple docstring'''





					for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__UpperCAmelCase   :					Tuple													=     TFXLMModel.from_pretrained(a_			)
							self.assertIsNotNone(a_			)

@require_tf
class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			@slow
			def 			snake_case__					(       self							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''			)
					__UpperCAmelCase   :					Optional[int]													=     tf.convert_to_tensor([[14, 4_47]]	,	dtype=tf.intaa			)  # the president
					__UpperCAmelCase   :					List[str]													=     [
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					]  # 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
					__UpperCAmelCase   :					Union[str, Any]													=     model.generate(a_	,	do_sample=a_			)
					self.assertListEqual(output_ids[0].numpy().tolist()	,	a_			)










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 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
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

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

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





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


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

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





