





'''simple docstring'''

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      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     parent
							__UpperCAmelCase	:							Union[str, Any]     =     13
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Dict     =     True
							__UpperCAmelCase	:							List[Any]     =     True
							__UpperCAmelCase	:							Union[str, Any]     =     True
							__UpperCAmelCase	:							Dict     =     True
							__UpperCAmelCase	:							Tuple     =     True
							__UpperCAmelCase	:							Union[str, Any]     =     99
							__UpperCAmelCase	:							int     =     32
							__UpperCAmelCase	:							List[Any]     =     2
							__UpperCAmelCase	:							Any     =     4
							__UpperCAmelCase	:							Any     =     37
							__UpperCAmelCase	:							List[str]     =     "gelu"
							__UpperCAmelCase	:							Optional[int]     =     0.1
							__UpperCAmelCase	:							List[Any]     =     0.1
							__UpperCAmelCase	:							Union[str, Any]     =     512
							__UpperCAmelCase	:							Tuple     =     16
							__UpperCAmelCase	:							Dict     =     2
							__UpperCAmelCase	:							str     =     0.02
							__UpperCAmelCase	:							Dict     =     3
							__UpperCAmelCase	:							Any     =     4
							__UpperCAmelCase	:							Dict     =     None
							__UpperCAmelCase	:							str     =     self.vocab_size - 1
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size		)

							__UpperCAmelCase	:							List[Any]     =     None
							if self.use_input_mask:
									__UpperCAmelCase	:							Union[str, Any]     =     random_attention_mask([self.batch_size, self.seq_length]		)

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

							__UpperCAmelCase	:							Optional[Any]     =     None
							if self.use_mc_token_ids:
									__UpperCAmelCase	:							Union[str, Any]     =     ids_tensor([self.batch_size, self.num_choices]		,	self.seq_length		)

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

							__UpperCAmelCase	:							str     =     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	:							List[Any]     =     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 		lowerCamelCase_   (   self       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      str		,	*UpperCAmelCase_       :      List[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     TFCTRLModel(config=UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							__UpperCAmelCase	:							List[str]     =     model(UpperCAmelCase_		)

							__UpperCAmelCase	:							Union[str, Any]     =     [input_ids, None, input_mask]  # None is the input for 'past'
							__UpperCAmelCase	:							Any     =     model(UpperCAmelCase_		)

							__UpperCAmelCase	:							Any     =     model(UpperCAmelCase_		)

							self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)		)
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      Optional[int]		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      Tuple		,	UpperCAmelCase_       :      str		,	*UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     TFCTRLLMHeadModel(config=UpperCAmelCase_		)
							__UpperCAmelCase	:							str     =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							__UpperCAmelCase	:							List[str]     =     model(UpperCAmelCase_		)
							self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)		)
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		,	*UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     self.num_labels
							__UpperCAmelCase	:							Tuple     =     ids_tensor([self.batch_size]		,	self.type_sequence_label_size		)
							__UpperCAmelCase	:							str     =     {
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "labels": sequence_labels,
							}
							__UpperCAmelCase	:							List[Any]     =     TFCTRLForSequenceClassification(UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[Any]     =     model(UpperCAmelCase_		)
							self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)		)






					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     self.prepare_config_and_inputs()

							(
							    (
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			(
							    __UpperCAmelCase
							)  ,			
							)	:							Any     =     config_and_inputs

							__UpperCAmelCase	:							List[str]     =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict






@require_tf
class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__							,unittest.TestCase		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE							=	(TFCTRLLMHeadModel,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE							=	(
					    {
					        '''feature-extraction''': TFCTRLModel,
					        '''text-classification''': TFCTRLForSequenceClassification,
					        '''text-generation''': TFCTRLLMHeadModel,
					        '''zero-shot''': TFCTRLForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE							=	False
					SCREAMING_SNAKE_CASE							=	False
					def 		lowerCamelCase_   (   self       :      str		,	UpperCAmelCase_       :      Dict		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      Tuple		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      List[Any]		):







							"""simple docstring"""
							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 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     TFCTRLModelTester(self		)
							__UpperCAmelCase	:							Tuple     =     ConfigTester(self		,	config_class=UpperCAmelCase_		,	n_embd=37		)
					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							self.config_tester.run_common_tests()
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_ctrl_lm_head(*UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase	:							int     =     [TFCTRLLMHeadModel]
							__UpperCAmelCase	:							Dict     =     [TFCTRLForSequenceClassification]

							for model_class in self.all_model_classes:
									__UpperCAmelCase	:							Any     =     model_class(UpperCAmelCase_		)
									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	:							Optional[Any]     =     model.get_output_embeddings()
											assert isinstance(UpperCAmelCase_		,	tf.keras.layers.Layer		)
											__UpperCAmelCase	:							str     =     model.get_bias()
											assert isinstance(UpperCAmelCase_		,	UpperCAmelCase_		)
											for k, v in name.items():
													assert isinstance(UpperCAmelCase_		,	tf.Variable		)
									elif model_class in list_other_models_with_output_ebd:
											__UpperCAmelCase	:							Union[str, Any]     =     model.get_output_embeddings()
											assert isinstance(UpperCAmelCase_		,	tf.keras.layers.Layer		)
											__UpperCAmelCase	:							List[str]     =     model.get_bias()
											assert name is None
									else:
											__UpperCAmelCase	:							Optional[Any]     =     model.get_output_embeddings()
											assert x is None
											__UpperCAmelCase	:							str     =     model.get_bias()
											assert name is None






					@slow
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__UpperCAmelCase	:							List[Any]     =     TFCTRLModel.from_pretrained(UpperCAmelCase_		)
									self.assertIsNotNone(UpperCAmelCase_		)






@require_tf
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					@slow
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[Any]     =     TFCTRLLMHeadModel.from_pretrained("ctrl"		)
							__UpperCAmelCase	:							Tuple     =     tf.convert_to_tensor([[11_859, 0, 1_611, 8]]		,	dtype=tf.intaa		)  # Legal the president is
							__UpperCAmelCase	:							Optional[int]     =     [
							    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	:							Any     =     model.generate(UpperCAmelCase_		,	do_sample=UpperCAmelCase_		)
							self.assertListEqual(output_ids[0].numpy().tolist()		,	UpperCAmelCase_		)











'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

		for i in range(1,       len_destination_seq + 1  ):
				__UpperCAmelCase	:							Optional[int]     =     i * insert_cost
				__UpperCAmelCase	:							List[Any]     =     F"I{destination_seq[i - 1]:c}"

		for i in range(1,       len_source_seq + 1  ):
				for j in range(1,       len_destination_seq + 1  ):
						if source_seq[i - 1] == destination_seq[j - 1]:
								__UpperCAmelCase	:							List[str]     =     costs[i - 1][j - 1] + copy_cost
								__UpperCAmelCase	:							Tuple     =     F"C{source_seq[i - 1]:c}"
						else:
								__UpperCAmelCase	:							Optional[int]     =     costs[i - 1][j - 1] + replace_cost
								__UpperCAmelCase	:							Dict     =     F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]  )

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

			lowerCAmelCase__      :   Optional[Any]      	=			len(operations)
			lowerCAmelCase__      :   int      	=			len(operations[0])
			lowerCAmelCase__      :   Dict      	=			assemble_transformation(operations, m - 1, n - 1)

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			0

			with open("min_cost.txt", "w") as file:
						for op in sequence:
									print("".join(string))

									if op[0] == "C":
												file.write("%-16s" % "Copy %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost -= 1
									elif op[0] == "R":
												lowerCAmelCase__      :   Optional[int]      	=			op[2]

												file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
												file.write("\t\t" + "".join(string))
												file.write("\r\n")

												cost += 1
									elif op[0] == "D":
												string.pop(i)

												file.write("%-16s" % "Delete %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2
									else:
												string.insert(i, op[1])

												file.write("%-16s" % "Insert %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2

									i += 1

						print("".join(string))
						print("Cost: ", cost)

						file.write("\r\nMinimum cost: " + str(cost))




