

from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.models.ctrl.modeling_tf_ctrl import (
     TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFCTRLForSequenceClassification,
     TFCTRLLMHeadModel,
     TFCTRLModel,
 )






class 		__magic_name__ (				snake_case							):






     def __init__(							self						,		_lowercase						,		)->			Tuple:
         UpperCamelCase_          =		parent
         UpperCamelCase_          =		13
         UpperCamelCase_          =		7
         UpperCamelCase_          =		True
         UpperCamelCase_          =		True
         UpperCamelCase_          =		True
         UpperCamelCase_          =		True
         UpperCamelCase_          =		True
         UpperCamelCase_          =		99
         UpperCamelCase_          =		32
         UpperCamelCase_          =		2
         UpperCamelCase_          =		4
         UpperCamelCase_          =		37
         UpperCamelCase_          =		"gelu"
         UpperCamelCase_          =		0.1
         UpperCamelCase_          =		0.1
         UpperCamelCase_          =		512
         UpperCamelCase_          =		16
         UpperCamelCase_          =		2
         UpperCamelCase_          =		0.02
         UpperCamelCase_          =		3
         UpperCamelCase_          =		4
         UpperCamelCase_          =		None
         UpperCamelCase_          =		self.vocab_size - 1






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size     )

         UpperCamelCase_          =		None
         if self.use_input_mask:
             UpperCamelCase_          =		random_attention_mask([self.batch_size, self.seq_length]     )

         UpperCamelCase_          =		None
         if self.use_token_type_ids:
             UpperCamelCase_          =		ids_tensor([self.batch_size, self.seq_length]						,		self.type_vocab_size     )

         UpperCamelCase_          =		None
         if self.use_mc_token_ids:
             UpperCamelCase_          =		ids_tensor([self.batch_size, self.num_choices]						,		self.seq_length     )

         UpperCamelCase_          =		None
         UpperCamelCase_          =		None
         UpperCamelCase_          =		None
         if self.use_labels:
             UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.type_sequence_label_size     )
             UpperCamelCase_          =		ids_tensor([self.batch_size, self.seq_length]						,		self.num_labels     )
             UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.num_choices     )

         UpperCamelCase_          =		CTRLConfig(
             vocab_size=self.vocab_size						,		n_embd=self.hidden_size						,		n_layer=self.num_hidden_layers						,		n_head=self.num_attention_heads						,		n_positions=self.max_position_embeddings						,		pad_token_id=self.pad_token_id						,		)

         UpperCamelCase_          =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]						,		2     )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		*_lowercase     )->			int:
         UpperCamelCase_          =		TFCTRLModel(config=_lowercase     )
         UpperCamelCase_          =		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
         UpperCamelCase_          =		model(_lowercase     )

         UpperCamelCase_          =		[input_ids, None, input_mask]  # None is the input for 'past'
         UpperCamelCase_          =		model(_lowercase     )

         UpperCamelCase_          =		model(_lowercase     )

         self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size)     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		*_lowercase     )->			str:
         UpperCamelCase_          =		TFCTRLLMHeadModel(config=_lowercase     )
         UpperCamelCase_          =		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
         UpperCamelCase_          =		model(_lowercase     )
         self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size)     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		*_lowercase     )->			Dict:
         UpperCamelCase_          =		self.num_labels
         UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.type_sequence_label_size     )
         UpperCamelCase_          =		{
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
             "labels": sequence_labels,
         }
         UpperCamelCase_          =		TFCTRLForSequenceClassification(_lowercase     )
         UpperCamelCase_          =		model(_lowercase     )
         self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)     )






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_          =		self.prepare_config_and_inputs()

         (
             (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    
         )          =		config_and_inputs

         UpperCamelCase_          =		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict






@require_tf
class 		__magic_name__ (				snake_case       ,	snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :int									=	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
     UpperCamelCase_     :int									=	(TFCTRLLMHeadModel,) if is_tf_available() else ()
     UpperCamelCase_     :List[str]									=	(
         {
             """feature-extraction""": TFCTRLModel,
             """text-classification""": TFCTRLForSequenceClassification,
             """text-generation""": TFCTRLLMHeadModel,
             """zero-shot""": TFCTRLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase_     :Union[str, Any]									=	False
     UpperCamelCase_     :List[Any]									=	False






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			int:
         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
             # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
             # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
             # config could not be created.
             return True

         return False






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		TFCTRLModelTester(self     )
         UpperCamelCase_          =		ConfigTester(self						,		config_class=_lowercase						,		n_embd=37     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         self.config_tester.run_common_tests()






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_          =		[TFCTRLLMHeadModel]
         UpperCamelCase_          =		[TFCTRLForSequenceClassification]

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             model.build()  # may be needed for the get_bias() call below
             assert isinstance(model.get_input_embeddings()						,		tf.keras.layers.Layer     )

             if model_class in list_lm_models:
                 UpperCamelCase_          =		model.get_output_embeddings()
                 assert isinstance(_lowercase						,		tf.keras.layers.Layer     )
                 UpperCamelCase_          =		model.get_bias()
                 assert isinstance(_lowercase						,		_lowercase     )
                 for k, v in name.items():
                     assert isinstance(_lowercase						,		tf.Variable     )
             elif model_class in list_other_models_with_output_ebd:
                 UpperCamelCase_          =		model.get_output_embeddings()
                 assert isinstance(_lowercase						,		tf.keras.layers.Layer     )
                 UpperCamelCase_          =		model.get_bias()
                 assert name is None
             else:
                 UpperCamelCase_          =		model.get_output_embeddings()
                 assert x is None
                 UpperCamelCase_          =		model.get_bias()
                 assert name is None






     @slow
     def      UpperCAmelCase_							(							self     )->			Any:
         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_          =		TFCTRLModel.from_pretrained(_lowercase     )
             self.assertIsNotNone(_lowercase     )






@require_tf
class 		__magic_name__ (				unittest.TestCase							):






     @slow
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		TFCTRLLMHeadModel.from_pretrained("ctrl"     )
         UpperCamelCase_          =		tf.convert_to_tensor([[11_859, 0, 1_611, 8]]						,		dtype=tf.intaa     )  # Legal the president is
         UpperCamelCase_          =		[
             11_859,
             0,
             1_611,
             8,
             5,
             150,
             26_449,
             2,
             19,
             348,
             469,
             3,
             2_595,
             48,
             20_740,
             246_533,
             246_533,
             19,
             30,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         UpperCamelCase_          =		model.generate(_lowercase						,		do_sample=_lowercase     )
         self.assertListEqual(output_ids[0].numpy().tolist()						,		_lowercase     )



def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


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