



"""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   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,):
			_lowercase				: Optional[int]     =   parent
			_lowercase				: Any     =   13
			_lowercase				: str     =   7
			_lowercase				: Dict     =   True
			_lowercase				: Optional[int]     =   True
			_lowercase				: Optional[Any]     =   True
			_lowercase				: Any     =   True
			_lowercase				: List[str]     =   True
			_lowercase				: int     =   False
			_lowercase				: Dict     =   False
			_lowercase				: Optional[Any]     =   False
			_lowercase				: Optional[int]     =   2
			_lowercase				: Optional[Any]     =   99
			_lowercase				: Optional[Any]     =   0
			_lowercase				: Optional[Any]     =   32
			_lowercase				: Union[str, Any]     =   2
			_lowercase				: Optional[int]     =   4
			_lowercase				: Dict     =   0.1
			_lowercase				: List[Any]     =   0.1
			_lowercase				: Dict     =   5_12
			_lowercase				: str     =   16
			_lowercase				: Optional[Any]     =   2
			_lowercase				: Dict     =   0.02
			_lowercase				: int     =   3
			_lowercase				: Union[str, Any]     =   4
			_lowercase				: Union[str, Any]     =   """last"""
			_lowercase				: Optional[int]     =   True
			_lowercase				: str     =   None
			_lowercase				: str     =   0






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   ids_tensor([self.batch_size, self.seq_length]				,self.vocab_size   )
			_lowercase				: Dict     =   random_attention_mask([self.batch_size, self.seq_length]				,dtype=tf.floataa   )

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

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

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

			_lowercase				: 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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: Dict     =   TFXLMModel(config=UpperCAmelCase_   )
			_lowercase				: List[str]     =   {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
			_lowercase				: Union[str, Any]     =   model(UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   [input_ids, input_mask]
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: Optional[int]     =   TFXLMWithLMHeadModel(UpperCAmelCase_   )

			_lowercase				: List[Any]     =   {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
			_lowercase				: Union[str, Any]     =   model(UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   outputs

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: Optional[int]     =   TFXLMForQuestionAnsweringSimple(UpperCAmelCase_   )

			_lowercase				: int     =   {"""input_ids""": input_ids, """lengths""": input_lengths}

			_lowercase				: int     =   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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: List[Any]     =   TFXLMForSequenceClassification(UpperCAmelCase_   )

			_lowercase				: List[str]     =   {"""input_ids""": input_ids, """lengths""": input_lengths}

			_lowercase				: Any     =   model(UpperCAmelCase_   )

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






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: int     =   self.num_labels
			_lowercase				: List[str]     =   TFXLMForTokenClassification(config=UpperCAmelCase_   )
			_lowercase				: List[Any]     =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
			_lowercase				: Any     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.num_labels)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: List[Any]     =   self.num_choices
			_lowercase				: Optional[Any]     =   TFXLMForMultipleChoice(config=UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   tf.tile(tf.expand_dims(UpperCAmelCase_				,1   )				,(1, self.num_choices, 1)   )
			_lowercase				: Optional[Any]     =   tf.tile(tf.expand_dims(UpperCAmelCase_				,1   )				,(1, self.num_choices, 1)   )
			_lowercase				: int     =   tf.tile(tf.expand_dims(UpperCAmelCase_				,1   )				,(1, self.num_choices, 1)   )
			_lowercase				: Optional[Any]     =   {
			    """input_ids""": multiple_choice_inputs_ids,
			    """attention_mask""": multiple_choice_input_mask,
			    """token_type_ids""": multiple_choice_token_type_ids,
			}
			_lowercase				: Tuple     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_choices)   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   self.prepare_config_and_inputs()
			(
			    (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  
			)				: Any     =   config_and_inputs
			_lowercase				: Tuple     =   {
			    """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	(     snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  (
	    (
	        TFXLMModel,
	        TFXLMWithLMHeadModel,
	        TFXLMForSequenceClassification,
	        TFXLMForQuestionAnsweringSimple,
	        TFXLMForTokenClassification,
	        TFXLMForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  (
	    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	SCREAMING_SNAKE_CASE_							:       Dict						       =  (
	    {
	        "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 {}
	)
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  False
	SCREAMING_SNAKE_CASE_							:       Any						       =  False






	def    lowerCamelCase__					(     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    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   TFXLMModelTester(self   )
			_lowercase				: Any     =   ConfigTester(self				,config_class=UpperCAmelCase_				,emb_dim=37   )






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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_model(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_for_token_classification(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase_   )






	@slow
	def    lowerCamelCase__					(     self   ):
			for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_lowercase				: Dict     =   TFXLMModel.from_pretrained(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )







@require_tf
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	@slow
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""   )
			_lowercase				: str     =   tf.convert_to_tensor([[14, 4_47]]				,dtype=tf.intaa   )  # the president
			_lowercase				: 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
			_lowercase				: int     =   model.generate(UpperCAmelCase_				,do_sample=UpperCAmelCase_   )
			self.assertListEqual(output_ids[0].numpy().tolist()				,UpperCAmelCase_   )










def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if not root or root not in binary_tree_mirror_dictionary:
		return
	snake_case_    ,snake_case_		:      Tuple           =						binary_tree_mirror_dictionary[root][:2]
	snake_case_		:      List[str]           =						[right_child, left_child]
	binary_tree_mirror_dict(__a ,			__a )
	binary_tree_mirror_dict(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = 1 ):
	if not binary_tree:
		raise ValueError('binary tree cannot be empty' )
	if root not in binary_tree:
		snake_case_		:      Dict           =						f"""root {root} is not present in the binary_tree"""
		raise ValueError(__a )
	snake_case_		:      Any           =						dict(__a )
	binary_tree_mirror_dict(__a ,			__a )
	return binary_tree_mirror_dictionary


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
						print(F'''Binary tree: {binary_tree}''')
						_SCREAMING_SNAKE_CASE   						=		binary_tree_mirror(binary_tree, 5)
						print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')

