
'''simple docstring'''
import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class   snake_case__					:







		"""simple docstring"""

		lowerCamelCase           =	()
		lowerCamelCase           =	True



		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> Dict:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.config_class

				# test default config
				snake_case     :					Optional[int]     =	config_class()
				self.assertIsNotNone(UpperCamelCase__			)
				snake_case     :					Union[str, Any]     =	['''stem'''] + [f'stage{idx}' for idx in range(1     ,  len(config.depths			) + 1			)]
				self.assertEqual(config.stage_names     ,  UpperCamelCase__			)
				self.assertTrue(set(config.out_features			).issubset(set(config.stage_names			)			)			)

				# Test out_features and out_indices are correctly set
				# out_features and out_indices both None
				snake_case     :					Optional[int]     =	config_class(out_features=UpperCamelCase__     ,  out_indices=UpperCamelCase__			)
				self.assertEqual(config.out_features     ,  [config.stage_names[-1]]			)
				self.assertEqual(config.out_indices     ,  [len(config.stage_names			) - 1]			)

				# out_features and out_indices both set
				snake_case     :					List[Any]     =	config_class(out_features=['''stem''', '''stage1''']     ,  out_indices=[0, 1]			)
				self.assertEqual(config.out_features     ,  ['''stem''', '''stage1''']			)
				self.assertEqual(config.out_indices     ,  [0, 1]			)

				# Only out_features set
				snake_case     :					Optional[Any]     =	config_class(out_features=['''stage1''', '''stage3''']			)
				self.assertEqual(config.out_features     ,  ['''stage1''', '''stage3''']			)
				self.assertEqual(config.out_indices     ,  [1, 3]			)

				# Only out_indices set
				snake_case     :					Any     =	config_class(out_indices=[0, 2]			)
				self.assertEqual(config.out_features     ,  [config.stage_names[0], config.stage_names[2]]			)
				self.assertEqual(config.out_indices     ,  [0, 2]			)

				# Error raised when out_indices do not correspond to out_features
				with self.assertRaises(UpperCamelCase__			):
						snake_case     :					Tuple     =	config_class(out_features=['''stage1''', '''stage2''']     ,  out_indices=[0, 2]			)



		def 			lowerCAmelCase							(			self							: str			)	-> Tuple:






				"""simple docstring"""







				snake_case     :					Optional[Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						snake_case     :					Any     =	model_class(UpperCamelCase__			)
						snake_case     :					Union[str, Any]     =	inspect.signature(model.forward			)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						snake_case     :					int     =	[*signature.parameters.keys()]
						snake_case     :					List[Any]     =	['''pixel_values''']
						self.assertListEqual(arg_names[:1]     ,  UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Optional[int]:






				"""simple docstring"""







				snake_case     :					Any     =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						snake_case     :					int     =	model_class(UpperCamelCase__			)
						self.assertEqual(len(model.channels			)     ,  len(config.out_features			)			)
						snake_case     :					Any     =	model.num_features
						snake_case     :					Optional[Any]     =	[config.stage_names.index(UpperCamelCase__			) for feat in config.out_features]
						snake_case     :					Dict     =	[num_features[idx] for idx in out_indices]
						self.assertListEqual(model.channels     ,  UpperCamelCase__			)

						snake_case     :					Optional[Any]     =	copy.deepcopy(UpperCamelCase__			)
						snake_case     :					Tuple     =	None
						snake_case     :					Optional[int]     =	model_class(UpperCamelCase__			)
						self.assertEqual(len(model.channels			)     ,  1			)
						self.assertListEqual(model.channels     ,  [num_features[-1]]			)

						snake_case     :					Optional[int]     =	copy.deepcopy(UpperCamelCase__			)
						snake_case     :					int     =	None
						snake_case     :					Union[str, Any]     =	model_class(UpperCamelCase__			)
						self.assertEqual(len(model.channels			)     ,  1			)
						self.assertListEqual(model.channels     ,  [num_features[-1]]			)



		def 			lowerCAmelCase							(			self							: Dict			)	-> List[Any]:






				"""simple docstring"""







				snake_case     :					Tuple     =	self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						snake_case     :					Any     =	model_class(UpperCamelCase__			)
						model.to(UpperCamelCase__			)
						model.eval()
						snake_case     :					int     =	model(**UpperCamelCase__			)

						self.assertEqual(len(result.feature_maps			)     ,  len(config.out_features			)			)
						self.assertEqual(len(model.channels			)     ,  len(config.out_features			)			)
						self.assertEqual(len(result.feature_maps			)     ,  len(config.out_indices			)			)
						self.assertEqual(len(model.channels			)     ,  len(config.out_indices			)			)

						# Check output of last stage is taken if out_features=None, out_indices=None
						snake_case     :					Union[str, Any]     =	copy.deepcopy(UpperCamelCase__			)
						snake_case     :					Dict     =	None
						snake_case     :					Any     =	model_class(UpperCamelCase__			)
						model.to(UpperCamelCase__			)
						model.eval()
						snake_case     :					Any     =	model(**UpperCamelCase__			)

						self.assertEqual(len(result.feature_maps			)     ,  1			)
						self.assertEqual(len(model.channels			)     ,  1			)

						snake_case     :					Union[str, Any]     =	copy.deepcopy(UpperCamelCase__			)
						snake_case     :					str     =	None
						snake_case     :					List[Any]     =	model_class(UpperCamelCase__			)
						model.to(UpperCamelCase__			)
						model.eval()
						snake_case     :					List[str]     =	model(**UpperCamelCase__			)

						self.assertEqual(len(result.feature_maps			)     ,  1			)
						self.assertEqual(len(model.channels			)     ,  1			)

						# Check backbone can be initialized with fresh weights
						snake_case     :					Optional[Any]     =	copy.deepcopy(UpperCamelCase__			)
						snake_case     :					int     =	False
						snake_case     :					List[str]     =	model_class(UpperCamelCase__			)
						model.to(UpperCamelCase__			)
						model.eval()
						snake_case     :					Optional[Any]     =	model(**UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> Optional[int]:






				"""simple docstring"""







				snake_case     :					Optional[Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

				for backbone_class in self.all_model_classes:
						snake_case     :					int     =	backbone_class(UpperCamelCase__			)

						self.assertTrue(hasattr(UpperCamelCase__     ,  '''backbone_type'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''stage_names'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''num_features'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''out_indices'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''out_features'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''out_feature_channels'''			)			)
						self.assertTrue(hasattr(UpperCamelCase__     ,  '''channels'''			)			)

						self.assertIsInstance(backbone.backbone_type     ,  UpperCamelCase__			)
						# Verify num_features has been initialized in the backbone init
						self.assertIsNotNone(backbone.num_features			)
						self.assertTrue(len(backbone.channels			) == len(backbone.out_indices			)			)
						self.assertTrue(len(backbone.stage_names			) == len(backbone.num_features			)			)
						self.assertTrue(len(backbone.channels			) <= len(backbone.num_features			)			)
						self.assertTrue(len(backbone.out_feature_channels			) == len(backbone.stage_names			)			)



		def 			lowerCAmelCase							(			self							: Dict			)	-> str:






				"""simple docstring"""







				snake_case     :					Any     =	self.model_tester.prepare_config_and_inputs_for_common()
				snake_case     :					Union[str, Any]     =	inputs_dict['''pixel_values'''].shape[0]

				for backbone_class in self.all_model_classes:
						snake_case     :					Optional[int]     =	backbone_class(UpperCamelCase__			)
						backbone.to(UpperCamelCase__			)
						backbone.eval()

						snake_case     :					List[str]     =	backbone(**UpperCamelCase__			)

						# Test default outputs and verify feature maps
						self.assertIsInstance(outputs.feature_maps     ,  UpperCamelCase__			)
						self.assertTrue(len(outputs.feature_maps			) == len(backbone.channels			)			)
						for feature_map, n_channels in zip(outputs.feature_maps     ,  backbone.channels			):
								self.assertTrue(feature_map.shape[:2]     ,  (batch_size, n_channels)			)
						self.assertIsNone(outputs.hidden_states			)
						self.assertIsNone(outputs.attentions			)

						# Test output_hidden_states=True
						snake_case     :					Any     =	backbone(**UpperCamelCase__     ,  output_hidden_states=UpperCamelCase__			)
						self.assertIsNotNone(outputs.hidden_states			)
						self.assertTrue(len(outputs.hidden_states			)     ,  len(backbone.stage_names			)			)
						for hidden_state, n_channels in zip(outputs.hidden_states     ,  backbone.channels			):
								self.assertTrue(hidden_state.shape[:2]     ,  (batch_size, n_channels)			)

						# Test output_attentions=True
						if self.has_attentions:
								snake_case     :					int     =	backbone(**UpperCamelCase__     ,  output_attentions=UpperCamelCase__			)
								self.assertIsNotNone(outputs.attentions			)





'''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   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]     ,  )	-> List[Any]:






				"""simple docstring"""







				snake_case     :					List[str]     =	parent
				snake_case     :					str     =	13
				snake_case     :					Any     =	7
				snake_case     :					str     =	True
				snake_case     :					int     =	True
				snake_case     :					Optional[Any]     =	True
				snake_case     :					Optional[Any]     =	True
				snake_case     :					str     =	True
				snake_case     :					Any     =	99
				snake_case     :					int     =	32
				snake_case     :					str     =	2
				snake_case     :					Optional[Any]     =	4
				snake_case     :					str     =	37
				snake_case     :					Optional[int]     =	'''gelu'''
				snake_case     :					List[Any]     =	0.1
				snake_case     :					Dict     =	0.1
				snake_case     :					str     =	512
				snake_case     :					List[str]     =	16
				snake_case     :					Tuple     =	2
				snake_case     :					List[str]     =	0.02
				snake_case     :					Optional[int]     =	3
				snake_case     :					Optional[int]     =	4
				snake_case     :					List[str]     =	None
				snake_case     :					List[str]     =	self.vocab_size - 1



		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)

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

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

				snake_case     :					Optional[int]     =	None
				if self.use_mc_token_ids:
						snake_case     :					List[str]     =	ids_tensor([self.batch_size, self.num_choices]     ,  self.seq_length			)

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

				snake_case     :					List[Any]     =	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     ,  )

				snake_case     :					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							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: str			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Tuple     =	TFCTRLModel(config=UpperCamelCase__			)
				snake_case     :					Union[str, Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				snake_case     :					Optional[int]     =	model(UpperCamelCase__			)

				snake_case     :					Optional[Any]     =	[input_ids, None, input_mask]  # None is the input for 'past'
				snake_case     :					str     =	model(UpperCamelCase__			)

				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)

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



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: List[str]			)	-> List[Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
				snake_case     :					int     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				snake_case     :					Any     =	model(UpperCamelCase__			)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)



		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  *UpperCamelCase__							: Tuple			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					int     =	self.num_labels
				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
				snake_case     :					Any     =	{
				    '''input_ids''': input_ids,
				    '''token_type_ids''': token_type_ids,
				    '''labels''': sequence_labels,
				}
				snake_case     :					Dict     =	TFCTRLForSequenceClassification(UpperCamelCase__			)
				snake_case     :					int     =	model(UpperCamelCase__			)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)



		def 			lowerCAmelCase							(			self							: int			)	-> str:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.prepare_config_and_inputs()

				(
				    (
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,(
				    snake_case
				) ,
				)     :					str     =	config_and_inputs

				snake_case     :					Any     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
				return config, inputs_dict







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







		"""simple docstring"""

		lowerCamelCase           =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
		lowerCamelCase           =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
		lowerCamelCase           =	(
		    {
		        """feature-extraction""": TFCTRLModel,
		        """text-classification""": TFCTRLForSequenceClassification,
		        """text-generation""": TFCTRLLMHeadModel,
		        """zero-shot""": TFCTRLForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase           =	False
		lowerCamelCase           =	False



		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Dict			)	-> str:






				"""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							: Optional[Any]			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					str     =	TFCTRLModelTester(self			)
				snake_case     :					Dict     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  n_embd=37			)



		def 			lowerCAmelCase							(			self							: int			)	-> Optional[int]:






				"""simple docstring"""







				self.config_tester.run_common_tests()



		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:






				"""simple docstring"""







				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:






				"""simple docstring"""







				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[Any]:






				"""simple docstring"""







				snake_case ,snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
				snake_case     :					int     =	[TFCTRLLMHeadModel]
				snake_case     :					Tuple     =	[TFCTRLForSequenceClassification]

				for model_class in self.all_model_classes:
						snake_case     :					List[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:
								snake_case     :					Optional[Any]     =	model.get_output_embeddings()
								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
								snake_case     :					Any     =	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:
								snake_case     :					List[Any]     =	model.get_output_embeddings()
								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
								snake_case     :					int     =	model.get_bias()
								assert name is None
						else:
								snake_case     :					Tuple     =	model.get_output_embeddings()
								assert x is None
								snake_case     :					Dict     =	model.get_bias()
								assert name is None



		@slow
		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[int]:






				"""simple docstring"""







				for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case     :					str     =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
						self.assertIsNotNone(UpperCamelCase__			)







@require_tf
class   snake_case__					(			unittest.TestCase				):







		"""simple docstring"""

		@slow
		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Union[str, Any]:






				"""simple docstring"""







				snake_case     :					Optional[Any]     =	TFCTRLLMHeadModel.from_pretrained('''ctrl'''			)
				snake_case     :					Optional[Any]     =	tf.convert_to_tensor([[1_1859, 0, 1611, 8]]     ,  dtype=tf.intaa			)  # Legal the president is
				snake_case     :					Dict     =	[
				    1_1859,
				    0,
				    1611,
				    8,
				    5,
				    150,
				    2_6449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2595,
				    48,
				    2_0740,
				    24_6533,
				    24_6533,
				    19,
				    30,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				snake_case     :					int     =	model.generate(UpperCamelCase__     ,  do_sample=UpperCamelCase__			)
				self.assertListEqual(output_ids[0].numpy().tolist()     ,  UpperCamelCase__			)


