



"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class       A_			:



						"""simple docstring"""






						def __init__(					self :Optional[Any]   ,  lowercase_ :List[Any]   ,  lowercase_ :Tuple=13   ,  lowercase_ :Union[str, Any]=7   ,  lowercase_ :Optional[Any]=True   ,  lowercase_ :Optional[int]=True   ,  lowercase_ :str=False   ,  lowercase_ :Dict=True   ,  lowercase_ :Optional[int]=99   ,  lowercase_ :List[str]=32   ,  lowercase_ :List[Any]=5   ,  lowercase_ :int=4   ,  lowercase_ :str=37   ,  lowercase_ :Optional[int]="gelu"   ,  lowercase_ :Union[str, Any]=0.1   ,  lowercase_ :Optional[int]=0.1   ,  lowercase_ :Tuple=5_12   ,  lowercase_ :Optional[int]=16   ,  lowercase_ :Optional[int]=2   ,  lowercase_ :int=0.02   ,  lowercase_ :Union[str, Any]=3   ,  lowercase_ :Tuple=4   ,  lowercase_ :Any=None   ,  )				->			str:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_input_mask
												UpperCAmelCase             = use_token_type_ids
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = max_position_embeddings
												UpperCAmelCase             = type_vocab_size
												UpperCAmelCase             = type_sequence_label_size
												UpperCAmelCase             = initializer_range
												UpperCAmelCase             = num_labels
												UpperCAmelCase             = num_choices
												UpperCAmelCase             = scope






						def 	UpperCAmelCase__     (					self :Dict					)				->			Union[str, Any]:
												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
												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             = self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






						def 	UpperCAmelCase__     (					self :int					)				->			Optional[Any]:
												return LlamaConfig(
												    vocab_size=self.vocab_size   ,  hidden_size=self.hidden_size   ,  num_hidden_layers=self.num_hidden_layers   ,  num_attention_heads=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  max_position_embeddings=self.max_position_embeddings   ,  type_vocab_size=self.type_vocab_size   ,  is_decoder=lowercase_   ,  initializer_range=self.initializer_range   ,  )






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :int   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :List[str]   ,  lowercase_ :List[Any]   ,  lowercase_ :Tuple   ,  lowercase_ :Dict   ,  lowercase_ :Union[str, Any]					)				->			int:
												UpperCAmelCase             = LlamaModel(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_					)
												UpperCAmelCase             = model(lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Dict   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Optional[int]   ,  lowercase_ :List[Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Optional[int]   ,  lowercase_ :int   ,  lowercase_ :Dict   ,  lowercase_ :str   ,  )				->			Dict:
												UpperCAmelCase             = True
												UpperCAmelCase             = LlamaModel(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  )
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  )
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_					)
												self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :Optional[int]   ,  lowercase_ :Union[str, Any]   ,  lowercase_ :Any   ,  lowercase_ :List[Any]   ,  lowercase_ :List[str]   ,  lowercase_ :Dict   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Tuple   ,  lowercase_ :Union[str, Any]   ,  )				->			List[str]:
												UpperCAmelCase             = LlamaForCausalLM(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :List[Any]   ,  lowercase_ :str   ,  lowercase_ :Optional[int]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Optional[int]   ,  lowercase_ :Dict   ,  lowercase_ :Tuple   ,  lowercase_ :Optional[int]   ,  lowercase_ :Any   ,  )				->			List[str]:
												UpperCAmelCase             = True
												UpperCAmelCase             = True
												UpperCAmelCase             = LlamaForCausalLM(config=lowercase_					)
												model.to(lowercase_					)
												model.eval()

												# first forward pass
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  use_cache=lowercase_   ,  )
												UpperCAmelCase             = outputs.past_key_values

												# create hypothetical multiple next token and extent to next_input_ids
												UpperCAmelCase             = ids_tensor((self.batch_size, 3)   ,  config.vocab_size					)
												UpperCAmelCase             = ids_tensor((self.batch_size, 3)   ,  vocab_size=2					)

												# append to next input_ids and
												UpperCAmelCase             = torch.cat([input_ids, next_tokens]   ,  dim=-1					)
												UpperCAmelCase             = torch.cat([input_mask, next_mask]   ,  dim=-1					)

												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  output_hidden_states=lowercase_   ,  )['hidden_states'][0]
												UpperCAmelCase             = model(
												    lowercase_   ,  attention_mask=lowercase_   ,  encoder_hidden_states=lowercase_   ,  encoder_attention_mask=lowercase_   ,  past_key_values=lowercase_   ,  output_hidden_states=lowercase_   ,  )['hidden_states'][0]

												# select random slice
												UpperCAmelCase             = ids_tensor((1,)   ,  output_from_past.shape[-1]					).item()
												UpperCAmelCase             = output_from_no_past[:, -3:, random_slice_idx].detach()
												UpperCAmelCase             = output_from_past[:, :, random_slice_idx].detach()

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-3					)					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    (
												    UpperCAmelCase
												)      ,    
												)             = config_and_inputs
												UpperCAmelCase             = {'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						__UpperCamelCase						=							(LlamaForCausalLM,) if is_torch_available() else ()
						__UpperCamelCase						=							(
						    {
						        """feature-extraction""": LlamaModel,
						        """text-classification""": LlamaForSequenceClassification,
						        """text-generation""": LlamaForCausalLM,
						        """zero-shot""": LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			str:
												UpperCAmelCase             = LlamaModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=lowercase_   ,  hidden_size=37					)






						def 	UpperCAmelCase__     (					self :str					)				->			Optional[int]:
												self.config_tester.run_common_tests()






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Dict:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			List[str]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCAmelCase             = type
																		self.model_tester.create_and_check_model(*lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			List[str]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor([self.model_tester.batch_size]   ,  self.model_tester.type_sequence_label_size					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Union[str, Any]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = 'single_label_classification'
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor([self.model_tester.batch_size]   ,  self.model_tester.type_sequence_label_size					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Tuple:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = 3
												UpperCAmelCase             = 'multi_label_classification'
												UpperCAmelCase             = input_dict['input_ids']
												UpperCAmelCase             = input_ids.ne(1					).to(lowercase_					)
												UpperCAmelCase             = ids_tensor(
												    [self.model_tester.batch_size, config.num_labels]   ,  self.model_tester.type_sequence_label_size					).to(torch.float					)
												UpperCAmelCase             = LlamaForSequenceClassification(lowercase_					)
												model.to(lowercase_					)
												model.eval()
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  labels=lowercase_					)
												self.assertEqual(result.logits.shape   ,  (self.model_tester.batch_size, self.model_tester.num_labels)					)






						@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'					)
						def 	UpperCAmelCase__     (					self :Dict					)				->			List[str]:
												pass






						@parameterized.expand([('linear',), ('dynamic',)]					)
						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :Optional[int]					)				->			int:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase             = ids_tensor([1, 10]   ,  config.vocab_size					)
												UpperCAmelCase             = ids_tensor([1, int(config.max_position_embeddings * 1.5					)]   ,  config.vocab_size					)

												set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
												UpperCAmelCase             = LlamaModel(lowercase_					)
												original_model.to(lowercase_					)
												original_model.eval()
												UpperCAmelCase             = original_model(lowercase_					).last_hidden_state
												UpperCAmelCase             = original_model(lowercase_					).last_hidden_state

												set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
												UpperCAmelCase             = {'type': scaling_type, 'factor': 10.0}
												UpperCAmelCase             = LlamaModel(lowercase_					)
												scaled_model.to(lowercase_					)
												scaled_model.eval()
												UpperCAmelCase             = scaled_model(lowercase_					).last_hidden_state
												UpperCAmelCase             = scaled_model(lowercase_					).last_hidden_state

												# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
												# maximum sequence length, so the outputs for the short input should match.
												if scaling_type == "dynamic":
																		self.assertTrue(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)
												else:
																		self.assertFalse(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)

												# The output should be different for long inputs
												self.assertFalse(torch.allclose(lowercase_   ,  lowercase_   ,  atol=1E-5					)					)







@require_torch
class       A_			(     unittest.TestCase				):



						"""simple docstring"""






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :Any					)				->			Union[str, Any]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor([input_ids]					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Any:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'					)
						@slow
						def 	UpperCAmelCase__     (					self :List[str]					)				->			List[str]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)
												# Expected mean on dim = -1
												UpperCAmelCase             = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# slicing logits[0, 0, 0:30]
												# fmt: off
												UpperCAmelCase             = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]					)
												# fmt: on
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)






						@unittest.skip(
						    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'					)
						@slow
						def 	UpperCAmelCase__     (					self :int					)				->			Optional[int]:
												UpperCAmelCase             = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'   ,  device_map='auto'					)
												UpperCAmelCase             = model(torch.tensor(lowercase_					)					)

												UpperCAmelCase             = torch.tensor(
												    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]   ,  dtype=torch.floataa					)
												torch.testing.assert_close(out.mean(-1					)   ,  lowercase_   ,  atol=1E-2   ,  rtol=1E-2					)
												# fmt: off
												UpperCAmelCase             = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]					)
												# fmt: on
												torch.testing.assert_close(out[0, 0, :30]   ,  lowercase_   ,  atol=1E-5   ,  rtol=1E-5					)






						@unittest.skip('Model is curently gated'					)
						@slow
						def 	UpperCAmelCase__     (					self :Any					)				->			int:
												UpperCAmelCase             = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
												UpperCAmelCase             = 'Simply put, the theory of relativity states that '
												UpperCAmelCase             = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'					)
												UpperCAmelCase             = tokenizer.encode(lowercase_   ,  return_tensors='pt'					)
												UpperCAmelCase             = LlamaForCausalLM.from_pretrained(
												    'meta-llama/Llama-2-13b-chat-hf'   ,  device_map='sequential'   ,  use_safetensors=lowercase_					)

												# greedy generation outputs
												UpperCAmelCase             = model.generate(lowercase_   ,  max_new_tokens=64   ,  top_p=lowercase_   ,  temperature=1   ,  do_sample=lowercase_					)
												UpperCAmelCase             = tokenizer.decode(generated_ids[0]   ,  skip_special_tokens=lowercase_					)
												self.assertEqual(lowercase_   ,  lowercase_					)









"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=14							, _a=7							, _a=True							, _a=True							, _a=True							, _a=True							, _a=True							, _a=99							, _a=32							, _a=5							, _a=4							, _a=37							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=512							, _a=16							, _a=2							, _a=0.02							, _a=3							, _a=4							, _a=None							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_token_type_ids
				__a           =			use_input_mask
				__a           =			use_labels
				__a           =			use_mc_token_ids
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			max_position_embeddings
				__a           =			type_vocab_size
				__a           =			type_sequence_label_size
				__a           =			initializer_range
				__a           =			num_labels
				__a           =			num_choices
				__a           =			scope
				__a           =			self.vocab_size - 1

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )

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

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

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

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

				__a           =			self.get_config()

				__a           =			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      ):
				return 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							, )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLModel(config=_a      )
				model.to(_a      )
				model.eval()

				model(_a							, token_type_ids=_a							, head_mask=_a      )
				model(_a							, token_type_ids=_a      )
				__a           =			model(_a      )
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )
				self.parent.assertEqual(len(result.past_key_values      )							, config.n_layer      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLLMHeadModel(_a      )
				model.to(_a      )
				model.eval()

				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.loss.shape							, ()      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()

				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			config_and_inputs

				__a           =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict





		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			self.num_labels
				__a           =			CTRLForSequenceClassification(_a      )
				model.to(_a      )
				model.eval()
				__a           =			ids_tensor([self.batch_size]							, self.type_sequence_label_size      )
				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)      )







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(CTRLLMHeadModel,) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			Optional[Any]										=				True
		__UpperCAmelCase		:			List[Any]										=				False
		__UpperCAmelCase		:			str										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				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      ):
				__a           =			CTRLModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, n_embd=37      )

		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_a      )

		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			CTRLModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )





		@unittest.skip('''The model doesn\'t support left padding'''      )  # and it's not used enough to be worth fixing :)
		def 			__UpperCAmelCase       (  self      ):
				pass







@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLLMHeadModel.from_pretrained('''ctrl'''      )
				model.to(_a      )
				__a           =			torch.tensor(
				    [[11_859, 0, 1_611, 8]]							, dtype=torch.long							, device=_a      )  # Legal the president is
				__a           =			[
				    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

				__a           =			model.generate(_a							, do_sample=_a      )
				self.assertListEqual(output_ids[0].tolist()							, _a      )






