


"""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 			lowerCamelCase__						:







						def __init__(							self   :    Union[str, Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Tuple=13			,			_lowercase   :    Any=7			,			_lowercase   :    List[str]=True			,			_lowercase   :    Optional[Any]=True			,			_lowercase   :    Optional[int]=False			,			_lowercase   :    Optional[Any]=True			,			_lowercase   :    Optional[int]=99			,			_lowercase   :    Tuple=32			,			_lowercase   :    List[str]=5			,			_lowercase   :    Dict=4			,			_lowercase   :    int=37			,			_lowercase   :    Union[str, Any]="gelu"			,			_lowercase   :    List[str]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    List[str]=512			,			_lowercase   :    List[Any]=16			,			_lowercase   :    str=2			,			_lowercase   :    Dict=0.0_2			,			_lowercase   :    Optional[int]=3			,			_lowercase   :    List[Any]=4			,			_lowercase   :    Any=None			,			):
								A					       = parent
								A					       = batch_size
								A					       = seq_length
								A					       = is_training
								A					       = use_input_mask
								A					       = use_token_type_ids
								A					       = use_labels
								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







						def __a						(							self   :    Any		):
								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
								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()

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







						def __a						(							self   :    List[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 __a						(							self   :    str			,			_lowercase   :    List[Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    List[Any]			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Optional[int]			,			_lowercase   :    List[Any]		):
								A					       = LlamaModel(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase			,			attention_mask=_lowercase		)
								A					       = model(_lowercase		)
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)		)







						def __a						(							self   :    Dict			,			_lowercase   :    str			,			_lowercase   :    List[Any]			,			_lowercase   :    Tuple			,			_lowercase   :    List[Any]			,			_lowercase   :    List[Any]			,			_lowercase   :    List[str]			,			_lowercase   :    int			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]			,			):
								A					       = True
								A					       = LlamaModel(_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(
								    _lowercase			,			attention_mask=_lowercase			,			encoder_hidden_states=_lowercase			,			encoder_attention_mask=_lowercase			,			)
								A					       = model(
								    _lowercase			,			attention_mask=_lowercase			,			encoder_hidden_states=_lowercase			,			)
								A					       = model(_lowercase			,			attention_mask=_lowercase		)
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)		)







						def __a						(							self   :    Tuple			,			_lowercase   :    Any			,			_lowercase   :    Optional[int]			,			_lowercase   :    Any			,			_lowercase   :    Optional[Any]			,			_lowercase   :    int			,			_lowercase   :    Optional[int]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Optional[Any]			,			):
								A					       = LlamaForCausalLM(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase			,			attention_mask=_lowercase			,			labels=_lowercase		)
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)		)







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    Tuple			,			_lowercase   :    Any			,			_lowercase   :    Optional[Any]			,			_lowercase   :    List[Any]			,			_lowercase   :    Tuple			,			_lowercase   :    List[Any]			,			_lowercase   :    Any			,			_lowercase   :    Dict			,			_lowercase   :    List[Any]			,			):
								A					       = True
								A					       = True
								A					       = LlamaForCausalLM(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()

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

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

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

								A					       = model(
								    _lowercase			,			attention_mask=_lowercase			,			encoder_hidden_states=_lowercase			,			encoder_attention_mask=_lowercase			,			output_hidden_states=_lowercase			,			)['hidden_states'][0]
								A					       = 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
								A					       = ids_tensor((1,)			,			output_from_past.shape[-1]		).item()
								A					       = output_from_no_past[:, -3:, random_slice_idx].detach()
								A					       = 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 __a						(							self   :    str		):
								A					       = self.prepare_config_and_inputs()
								(
								    (
								    A
								)       , (
								    A
								)       , (
								    A
								)       , (
								    A
								)       , (
								    A
								)       , (
								    A
								)       , (
								    A
								)       , 
								)					       = config_and_inputs
								A					       = {'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_							,    UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						lowerCAmelCase       =     (LlamaForCausalLM,) if is_torch_available() else ()
						lowerCAmelCase       =     (
						    {
						        """feature-extraction""": LlamaModel,
						        """text-classification""": LlamaForSequenceClassification,
						        """text-generation""": LlamaForCausalLM,
						        """zero-shot""": LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    str		):
								A					       = LlamaModelTester(self		)
								A					       = ConfigTester(self			,			config_class=_lowercase			,			hidden_size=37		)







						def __a						(							self   :    int		):
								self.config_tester.run_common_tests()







						def __a						(							self   :    Dict		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase		)







						def __a						(							self   :    Tuple		):
								A					       = self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										A					       = type
										self.model_tester.create_and_check_model(*_lowercase		)







						def __a						(							self   :    str		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = 3
								A					       = input_dict['input_ids']
								A					       = input_ids.ne(1		).to(_lowercase		)
								A					       = ids_tensor([self.model_tester.batch_size]			,			self.model_tester.type_sequence_label_size		)
								A					       = LlamaForSequenceClassification(_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase			,			attention_mask=_lowercase			,			labels=_lowercase		)
								self.assertEqual(result.logits.shape			,			(self.model_tester.batch_size, self.model_tester.num_labels)		)







						def __a						(							self   :    List[Any]		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = 3
								A					       = 'single_label_classification'
								A					       = input_dict['input_ids']
								A					       = input_ids.ne(1		).to(_lowercase		)
								A					       = ids_tensor([self.model_tester.batch_size]			,			self.model_tester.type_sequence_label_size		)
								A					       = LlamaForSequenceClassification(_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase			,			attention_mask=_lowercase			,			labels=_lowercase		)
								self.assertEqual(result.logits.shape			,			(self.model_tester.batch_size, self.model_tester.num_labels)		)







						def __a						(							self   :    str		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = 3
								A					       = 'multi_label_classification'
								A					       = input_dict['input_ids']
								A					       = input_ids.ne(1		).to(_lowercase		)
								A					       = ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]			,			self.model_tester.type_sequence_label_size		).to(torch.float		)
								A					       = LlamaForSequenceClassification(_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = 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 __a						(							self   :    int		):
								pass







						@parameterized.expand([('linear',), ('dynamic',)]		)
						def __a						(							self   :    Optional[int]			,			_lowercase   :    Any		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = ids_tensor([1, 10]			,			config.vocab_size		)
								A					       = 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
								A					       = LlamaModel(_lowercase		)
								original_model.to(_lowercase		)
								original_model.eval()
								A					       = original_model(_lowercase		).last_hidden_state
								A					       = original_model(_lowercase		).last_hidden_state

								set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
								A					       = {'type': scaling_type, 'factor': 1_0.0}
								A					       = LlamaModel(_lowercase		)
								scaled_model.to(_lowercase		)
								scaled_model.eval()
								A					       = scaled_model(_lowercase		).last_hidden_state
								A					       = 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 			lowerCamelCase__						(   unittest.TestCase			):







						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'		)
						@slow
						def __a						(							self   :    List[str]		):
								A					       = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								A					       = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'			,			device_map='auto'		)
								A					       = model(torch.tensor([input_ids]		)		)
								# Expected mean on dim = -1
								A					       = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]		)
								torch.testing.assert_close(out.mean(-1		)			,			_lowercase			,			atol=1e-2			,			rtol=1e-2		)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								A					       = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]		)
								# 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 __a						(							self   :    List[str]		):
								A					       = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								A					       = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'			,			device_map='auto'		)
								A					       = model(torch.tensor(_lowercase		)		)
								# Expected mean on dim = -1
								A					       = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]		)
								torch.testing.assert_close(out.mean(-1		)			,			_lowercase			,			atol=1e-2			,			rtol=1e-2		)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								A					       = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]		)
								# 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 __a						(							self   :    int		):
								A					       = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								A					       = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'			,			device_map='auto'		)
								A					       = model(torch.tensor(_lowercase		)		)
								# Expected mean on dim = -1
								A					       = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]		)
								torch.testing.assert_close(out.mean(-1		)			,			_lowercase			,			atol=1e-2			,			rtol=1e-2		)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								A					       = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]		)
								# 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 __a						(							self   :    Optional[Any]		):
								A					       = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								A					       = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'			,			device_map='auto'		)
								A					       = model(torch.tensor(_lowercase		)		)

								A					       = torch.tensor(
								    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]			,			dtype=torch.floataa		)
								torch.testing.assert_close(out.mean(-1		)			,			_lowercase			,			atol=1e-2			,			rtol=1e-2		)
								# fmt: off
								A					       = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]		)
								# 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 __a						(							self   :    Any		):
								A					       = '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'
								A					       = 'Simply put, the theory of relativity states that '
								A					       = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'		)
								A					       = tokenizer.encode(_lowercase			,			return_tensors='pt'		)
								A					       = LlamaForCausalLM.from_pretrained(
								    'meta-llama/Llama-2-13b-chat-hf'			,			device_map='sequential'			,			use_safetensors=_lowercase		)

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









"""simple docstring"""


import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible



class 			lowerCamelCase__						(   unittest.TestCase			):







						def __a						(							self   :    Dict		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    int		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    # Removed: 'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								]
								self.assertTrue(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'safety_checker/pytorch_model.bin',
								    'safety_checker/model.safetensors',
								    'vae/diffusion_pytorch_model.bin',
								    'vae/diffusion_pytorch_model.safetensors',
								    'text_encoder/pytorch_model.bin',
								    # Removed: 'text_encoder/model.safetensors',
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(_lowercase		)		)







						def __a						(							self   :    Any		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = [
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Optional[Any]		):
								# pass variant but use the non-variant filenames
								A					       = [
								    'unet/diffusion_pytorch_model.bin',
								    'unet/diffusion_pytorch_model.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertFalse(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Dict		):
								A					       = [
								    'text_encoder/pytorch_model.fp16.bin',
								    'text_encoder/model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    Any		):
								# pass variant but use the non-variant filenames
								A					       = [
								    'text_encoder/pytorch_model.bin',
								    'text_encoder/model.safetensors',
								]
								A					       = 'fp16'
								self.assertTrue(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)







						def __a						(							self   :    List[str]		):
								A					       = [
								    'safety_checker/pytorch_model.fp16.bin',
								    'safety_checker/model.fp16.safetensors',
								    'vae/diffusion_pytorch_model.fp16.bin',
								    'vae/diffusion_pytorch_model.fp16.safetensors',
								    'text_encoder/pytorch_model.fp16.bin',
								    # 'text_encoder/model.fp16.safetensors',
								    'unet/diffusion_pytorch_model.fp16.bin',
								    'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								A					       = 'fp16'
								self.assertFalse(is_safetensors_compatible(_lowercase			,			variant=_lowercase		)		)





