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 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Tuple=13      ,   __lowerCamelCase       :  Optional[int]=7      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  str=99      ,   __lowerCamelCase       :  Tuple=32      ,   __lowerCamelCase       :  List[str]=5      ,   __lowerCamelCase       :  Optional[int]=4      ,   __lowerCamelCase       :  List[str]=37      ,   __lowerCamelCase       :  Optional[Any]="gelu"      ,   __lowerCamelCase       :  Tuple=0.1      ,   __lowerCamelCase       :  Union[str, Any]=0.1      ,   __lowerCamelCase       :  List[Any]=5_12      ,   __lowerCamelCase       :  int=16      ,   __lowerCamelCase       :  str=2      ,   __lowerCamelCase       :  Any=0.02      ,   __lowerCamelCase       :  int=3      ,   __lowerCamelCase       :  Optional[Any]=4      ,   __lowerCamelCase       :  Dict=None      ,   )      ->  Any:
							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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Optional[int]:
							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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[str]:
							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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   )





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str							)      ->  Optional[Any]:
							a					=    LlamaModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase							)
							a					=    model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)							)





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





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





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   )      ->  Optional[Any]:
							a					=    True
							a					=    True
							a					=    LlamaForCausalLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							# first forward pass
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   use_cache=__lowerCamelCase      ,   )
							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(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   output_hidden_states=__lowerCamelCase      ,   )["hidden_states"][0]
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   past_key_values=__lowerCamelCase      ,   output_hidden_states=__lowerCamelCase      ,   )["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(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1e-3							)							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  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 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Dict											=      (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_ :							str											=      (LlamaForCausalLM,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      (
				    {
				        """feature-extraction""": LlamaModel,
				        """text-classification""": LlamaForSequenceClassification,
				        """text-generation""": LlamaForCausalLM,
				        """zero-shot""": LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      False
				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      False





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    LlamaModelTester(self							)
							a					=    ConfigTester(self      ,   config_class=__lowerCamelCase      ,   hidden_size=37							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  str:
							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(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()
							a					=    3
							a					=    input_dict["input_ids"]
							a					=    input_ids.ne(1							).to(__lowerCamelCase							)
							a					=    ids_tensor([self.model_tester.batch_size]      ,   self.model_tester.type_sequence_label_size							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.assertEqual(result.logits.shape      ,   (self.model_tester.batch_size, self.model_tester.num_labels)							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[str]:
							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(__lowerCamelCase							)
							a					=    ids_tensor([self.model_tester.batch_size]      ,   self.model_tester.type_sequence_label_size							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.assertEqual(result.logits.shape      ,   (self.model_tester.batch_size, self.model_tester.num_labels)							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Tuple:
							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(__lowerCamelCase							)
							a					=    ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]      ,   self.model_tester.type_sequence_label_size							).to(torch.float							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							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							)      ->  Any:
							pass





				@parameterized.expand([("linear",), ("dynamic",)]							)
				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Tuple							)      ->  Union[str, 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(__lowerCamelCase							)
							original_model.to(__lowerCamelCase							)
							original_model.eval()
							a					=    original_model(__lowerCamelCase							).last_hidden_state
							a					=    original_model(__lowerCamelCase							).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": 10.0}
							a					=    LlamaModel(__lowerCamelCase							)
							scaled_model.to(__lowerCamelCase							)
							scaled_model.eval()
							a					=    scaled_model(__lowerCamelCase							).last_hidden_state
							a					=    scaled_model(__lowerCamelCase							).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(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1e-5							)							)
							else:
										self.assertFalse(torch.allclose(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1e-5							)							)

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




@require_torch
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  int:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							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_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]							)
							torch.testing.assert_close(out.mean(-1							)      ,   __lowerCamelCase      ,   atol=1e-2      ,   rtol=1e-2							)
							# slicing logits[0, 0, 0:30]
							# fmt: off
							a					=    torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]							)
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]      ,   __lowerCamelCase      ,   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       :  Dict							)      ->  List[str]:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)
							# Expected mean on dim = -1
							a					=    torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]							)
							torch.testing.assert_close(out.mean(-1							)      ,   __lowerCamelCase      ,   atol=1e-2      ,   rtol=1e-2							)
							# slicing logits[0, 0, 0:30]
							# fmt: off
							a					=    torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]							)
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]      ,   __lowerCamelCase      ,   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       :  Any							)      ->  Tuple:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)
							# Expected mean on dim = -1
							a					=    torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]							)
							torch.testing.assert_close(out.mean(-1							)      ,   __lowerCamelCase      ,   atol=1e-2      ,   rtol=1e-2							)
							# slicing logits[0, 0, 0:30]
							# fmt: off
							a					=    torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]							)
							# fmt: on
							torch.testing.assert_close(out.mean(-1							)      ,   __lowerCamelCase      ,   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       :  List[Any]							)      ->  List[Any]:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)

							a					=    torch.tensor(
							    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]      ,   dtype=torch.floataa							)
							torch.testing.assert_close(out.mean(-1							)      ,   __lowerCamelCase      ,   atol=1e-2      ,   rtol=1e-2							)
							# fmt: off
							a					=    torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]							)
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]      ,   __lowerCamelCase      ,   atol=1e-5      ,   rtol=1e-5							)





				@unittest.skip("Model is curently gated"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Optional[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(__lowerCamelCase      ,   return_tensors="pt"							)
							a					=    LlamaForCausalLM.from_pretrained(
							    "meta-llama/Llama-2-13b-chat-hf"      ,   device_map="sequential"      ,   use_safetensors=__lowerCamelCase							)

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












import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    fname.split(os.path.sep     )[-1]
			return re.search(R"^(.*)_\d+\.jpg$",						A     ).groups()[0]




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Tuple:
							a					=    file_names
							a					=    image_transform
							a					=    label_to_id





				def __len__(   self       :  Any							)      ->  Tuple:
							return len(self.file_names							)





				def __getitem__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  int:
							a					=    self.file_names[idx]
							a					=    PIL.Image.open(__lowerCamelCase							)
							a					=    raw_image.convert("RGB"							)
							if self.image_transform is not None:
										a					=    self.image_transform(__lowerCamelCase							)
							a					=    extract_label(__lowerCamelCase							)
							if self.label_to_id is not None:
										a					=    self.label_to_id[label]
							return {"image": image, "label": label}




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if args.with_tracking:
						a					=    Accelerator(
						    cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="all",						project_dir=args.project_dir     )
			else:
						a					=    Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision     )

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a					=    config["lr"]
			a					=    int(config["num_epochs"]     )
			a					=    int(config["seed"]     )
			a					=    int(config["batch_size"]     )
			a					=    config["image_size"]
			if not isinstance(A,						(list, tuple)     ):
						a					=    (image_size, image_size)

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps,						"isdigit"     ):
						if args.checkpointing_steps == "epoch":
									a					=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a					=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						a					=    None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						a					=    os.path.split(A     )[-1].split("."     )[0]
						accelerator.init_trackers(A,						A     )

			# Grab all the image filenames
			a					=    [os.path.join(args.data_dir,						A     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(".jpg"     )]

			# Build the label correspondences
			a					=    [extract_label(A     ) for fname in file_names]
			a					=    list(set(A     )     )
			id_to_label.sort()
			a					=    {lbl: i for i, lbl in enumerate(A     )}

			# Set the seed before splitting the data.
			np.random.seed(A     )
			torch.manual_seed(A     )
			torch.cuda.manual_seed_all(A     )

			# Split our filenames between train and validation
			a					=    np.random.permutation(len(A     )     )
			a					=    int(0.8 * len(A     )     )
			a					=    random_perm[:cut]
			a					=    random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			a					=    Compose([RandomResizedCrop(A,						scale=(0.5, 1.0)     ), ToTensor()]     )
			a					=    PetsDataset(
			    [file_names[i] for i in train_split],						image_transform=A,						label_to_id=A     )

			# For evaluation, we use a deterministic Resize
			a					=    Compose([Resize(A     ), ToTensor()]     )
			a					=    PetsDataset([file_names[i] for i in eval_split],						image_transform=A,						label_to_id=A     )

			# Instantiate dataloaders.
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a					=    create_model("resnet50d",						pretrained=A,						num_classes=len(A     )     )

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			a					=    model.to(accelerator.device     )

			# Freezing the base model
			for param in model.parameters():
						a					=    False
			for param in model.get_classifier().parameters():
						a					=    True

			# We normalize the batches of images to be a bit faster.
			a					=    torch.tensor(model.default_cfg["mean"]     )[None, :, None, None].to(accelerator.device     )
			a					=    torch.tensor(model.default_cfg["std"]     )[None, :, None, None].to(accelerator.device     )

			# Instantiate optimizer
			a					=    torch.optim.Adam(params=model.parameters(),						lr=lr / 25     )

			# Instantiate learning rate scheduler
			a					=    OneCycleLR(optimizer=A,						max_lr=A,						epochs=A,						steps_per_epoch=len(A     )     )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			a					,	a					,	a					,	a					,	a					=    accelerator.prepare(
			    A,						A,						A,						A,						A     )
			# We need to keep track of how many total steps we have iterated over
			a					=    0
			# We also need to keep track of the starting epoch so files are named properly
			a					=    0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									a					=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									a					=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									a					=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a					=    os.path.splitext(A     )[0]

						if "epoch" in training_difference:
									a					=    int(training_difference.replace("epoch_",						""     )     ) + 1
									a					=    None
						else:
									a					=    int(training_difference.replace("step_",						""     )     )
									a					=    resume_step // len(A     )
									resume_step -= starting_epoch * len(A     )

    # Now we train the model
			for epoch in range(A,						A     ):
						model.train()
						if args.with_tracking:
									a					=    0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									a					=    accelerator.skip_first_batches(A,						A     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a					=    train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									a					=    model(A     )
									a					=    torch.nn.functional.cross_entropy(A,						batch["label"]     )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(A     )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(A,						A     ):
												a					=    F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a					=    os.path.join(args.output_dir,						A     )
															accelerator.save_state(A     )
						model.eval()
						a					=    0
						a					=    0
						for step, batch in enumerate(A     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									with torch.no_grad():
												a					=    model(A     )
									a					=    outputs.argmax(dim=-1     )
									a					,	a					=    accelerator.gather_for_metrics((predictions, batch["label"])     )
									a					=    predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						a					=    accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""     )
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": 100 * eval_metric,
									        "train_loss": total_loss.item() / len(A     ),
									        "epoch": epoch,
									    },						step=A,						)
						if checkpointing_steps == "epoch":
									a					=    F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a					=    os.path.join(args.output_dir,						A     )
									accelerator.save_state(A     )

			if args.with_tracking:
						accelerator.end_training()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument("--data_dir",						required=A,						help="The data folder on disk."     )
			parser.add_argument("--fp16",						action="store_true",						help="If passed, will use FP16 training."     )
			parser.add_argument(
			    "--mixed_precision",						type=A,						default=A,						choices=["no", "fp16", "bf16", "fp8"],						help="Whether to use mixed precision. Choose"
			    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
			    "and an Nvidia Ampere GPU.",						)
			parser.add_argument("--cpu",						action="store_true",						help="If passed, will train on the CPU."     )
			parser.add_argument(
			    "--checkpointing_steps",						type=A,						default=A,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",						)
			parser.add_argument(
			    "--output_dir",						type=A,						default=".",						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",						)
			parser.add_argument(
			    "--resume_from_checkpoint",						type=A,						default=A,						help="If the training should continue from a checkpoint folder.",						)
			parser.add_argument(
			    "--with_tracking",						action="store_true",						help="Whether to load in all available experiment trackers from the environment and use them for logging.",						)
			parser.add_argument(
			    "--project_dir",						type=A,						default="logs",						help="Location on where to store experiment tracking logs` and relevent project information",						)
			a					=    parser.parse_args()
			a					=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(A,						A     )


if __name__ == "__main__":
		main()






