"""simple docstring"""





import unittest

from transformers import AutoTokenizer, FalconConfig, 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 (
        FalconForCausalLM,
        FalconForQuestionAnswering,
        FalconForSequenceClassification,
        FalconForTokenClassification,
        FalconModel,
    )
class  _snake_case     :






      def __init__(       self      :			Any							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict=3							,					UpperCAmelCase      :			str=7							,					UpperCAmelCase      :			Union[str, Any]=True							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			List[str]=False							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			Any=99							,					UpperCAmelCase      :			Any=32							,					UpperCAmelCase      :			str=5							,					UpperCAmelCase      :			List[Any]=4							,					UpperCAmelCase      :			Optional[int]=37							,					UpperCAmelCase      :			str="gelu"							,					UpperCAmelCase      :			Tuple=0.1							,					UpperCAmelCase      :			str=0.1							,					UpperCAmelCase      :			int=512							,					UpperCAmelCase      :			Union[str, Any]=16							,					UpperCAmelCase      :			Union[str, Any]=2							,					UpperCAmelCase      :			Any=0.0_2							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=4							,					UpperCAmelCase      :			Dict=None							,					):
             __lowerCamelCase					:		Union[str, Any]		   =     parent
             __lowerCamelCase					:		List[Any]		   =     batch_size
             __lowerCamelCase					:		str		   =     seq_length
             __lowerCamelCase					:		str		   =     is_training
             __lowerCamelCase					:		Union[str, Any]		   =     use_input_mask
             __lowerCamelCase					:		List[Any]		   =     use_token_type_ids
             __lowerCamelCase					:		Tuple		   =     use_labels
             __lowerCamelCase					:		Any		   =     vocab_size
             __lowerCamelCase					:		Union[str, Any]		   =     hidden_size
             __lowerCamelCase					:		Dict		   =     num_hidden_layers
             __lowerCamelCase					:		Optional[int]		   =     num_attention_heads
             __lowerCamelCase					:		Any		   =     intermediate_size
             __lowerCamelCase					:		str		   =     hidden_act
             __lowerCamelCase					:		Dict		   =     hidden_dropout_prob
             __lowerCamelCase					:		Tuple		   =     attention_probs_dropout_prob
             __lowerCamelCase					:		List[Any]		   =     max_position_embeddings
             __lowerCamelCase					:		Any		   =     type_vocab_size
             __lowerCamelCase					:		List[Any]		   =     type_sequence_label_size
             __lowerCamelCase					:		int		   =     initializer_range
             __lowerCamelCase					:		Tuple		   =     num_labels
             __lowerCamelCase					:		Union[str, Any]		   =     num_choices
             __lowerCamelCase					:		Any		   =     scope






      def       lowerCamelCase__     (       self      :			Any    ):
             __lowerCamelCase					:		Any		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

             __lowerCamelCase					:		Dict		   =     None
             if self.use_input_mask:
                    __lowerCamelCase					:		List[str]		   =     random_attention_mask([self.batch_size, self.seq_length]    )

             __lowerCamelCase					:		Dict		   =     None

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

             __lowerCamelCase					:		Tuple		   =     self.get_config()

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






      def       lowerCamelCase__     (       self      :			int    ):
             return FalconConfig(
                 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=UpperCAmelCase							,					initializer_range=self.initializer_range							,					pad_token_id=1							,					new_decoder_architecture=UpperCAmelCase							,					)






      def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]    ):
             __lowerCamelCase					:		Any		   =     FalconModel(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Any		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )
             __lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )
             self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )






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






      def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[str]							,					):
             __lowerCamelCase					:		Any		   =     FalconForCausalLM(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )






      def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					):
             __lowerCamelCase					:		Any		   =     True
             __lowerCamelCase					:		List[Any]		   =     True
             __lowerCamelCase					:		List[str]		   =     FalconForCausalLM(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()

             # first forward pass
             __lowerCamelCase					:		Optional[Any]		   =     model(
                 UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					use_cache=UpperCAmelCase							,					)
             __lowerCamelCase					:		int		   =     outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             __lowerCamelCase					:		List[str]		   =     ids_tensor((self.batch_size, 3)							,					config.vocab_size    )
             __lowerCamelCase					:		str		   =     ids_tensor((self.batch_size, 3)							,					vocab_size=2    )

             # append to next input_ids and
             __lowerCamelCase					:		List[str]		   =     torch.cat([input_ids, next_tokens]							,					dim=-1    )
             __lowerCamelCase					:		Union[str, Any]		   =     torch.cat([input_mask, next_mask]							,					dim=-1    )

             __lowerCamelCase					:		Optional[Any]		   =     model(
                 UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					output_hidden_states=UpperCAmelCase							,					)["hidden_states"][0]
             __lowerCamelCase					:		Optional[Any]		   =     model(
                 UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					past_key_values=UpperCAmelCase							,					output_hidden_states=UpperCAmelCase							,					)["hidden_states"][0]

             # select random slice
             __lowerCamelCase					:		Any		   =     ids_tensor((1,)							,					output_from_past.shape[-1]    ).item()
             __lowerCamelCase					:		str		   =     output_from_no_past[:, -3:, random_slice_idx].detach()
             __lowerCamelCase					:		Union[str, Any]		   =     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(UpperCAmelCase							,					UpperCAmelCase							,					atol=1E-3    )    )







      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             __lowerCamelCase					:		Dict		   =     self.prepare_config_and_inputs()
             (
                 __lowerCamelCase  
             )					:		str		   =     config_and_inputs
             __lowerCamelCase					:		Dict		   =     {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

@require_torch
class  _snake_case     (					a__				, a__				, a__				, unittest.TestCase		):
      snake_case__			  =			(
          (
              FalconModel,
              FalconForCausalLM,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      snake_case__			  =			(FalconForCausalLM,) if is_torch_available() else ()
      snake_case__			  =			(
          {
              "feature-extraction": FalconModel,
              "text-classification": FalconForSequenceClassification,
              "text-generation": FalconForCausalLM,
              "question-answering": FalconForQuestionAnswering,
              "token-classification": FalconForTokenClassification,
              "zero-shot": FalconForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__			  =			False
      snake_case__			  =			False






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		Dict		   =     FalconModelTester(self    )
             __lowerCamelCase					:		Dict		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					hidden_size=37    )






      def       lowerCamelCase__     (       self      :			List[str]    ):
             self.config_tester.run_common_tests()






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs()
             for alibi in [True, False]:
                    __lowerCamelCase					:		str		   =     alibi
                    self.model_tester.create_and_check_model(UpperCAmelCase							,					*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			str    ):
             __lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		Union[str, Any]		   =     3
             __lowerCamelCase					:		Union[str, Any]		   =     input_dict["input_ids"]
             __lowerCamelCase					:		int		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		int		   =     ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size    )
             __lowerCamelCase					:		Optional[int]		   =     FalconForSequenceClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Union[str, Any]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		List[str]		   =     3
             __lowerCamelCase					:		Optional[int]		   =     "single_label_classification"
             __lowerCamelCase					:		Any		   =     input_dict["input_ids"]
             __lowerCamelCase					:		Optional[int]		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		Any		   =     ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size    )
             __lowerCamelCase					:		Tuple		   =     FalconForSequenceClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Dict		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )






      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             __lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		Tuple		   =     input_dict["input_ids"]
             __lowerCamelCase					:		str		   =     FalconForCausalLM(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					use_cache=UpperCAmelCase    )
             __lowerCamelCase					:		Union[str, Any]		   =     input_ids.shape[0]
             __lowerCamelCase					:		int		   =     model._convert_to_rw_cache(result.past_key_values    )
             __lowerCamelCase					:		Union[str, Any]		   =     model._convert_cache_to_standard_format(UpperCAmelCase							,					UpperCAmelCase    )
             for layer in range(len(UpperCAmelCase    )    ):
                    for tensor_idx in range(2    ):
                           self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3    )
                           self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4    )
                           self.assertTrue(
                               torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]    )    )






      def       lowerCamelCase__     (       self      :			int    ):
             __lowerCamelCase					:		Optional[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		List[Any]		   =     3
             __lowerCamelCase					:		Tuple		   =     "multi_label_classification"
             __lowerCamelCase					:		List[Any]		   =     input_dict["input_ids"]
             __lowerCamelCase					:		Tuple		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		List[Any]		   =     ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]							,					self.model_tester.type_sequence_label_size    ).to(torch.float    )
             __lowerCamelCase					:		Union[str, Any]		   =     FalconForSequenceClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Any		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )







      def       lowerCamelCase__     (       self      :			List[str]    ):
             # Falcon can have different numbers of KV-heads than the number of query heads, so we need
             # to override this test to use the right head counts.
             for model_class in self.all_generative_model_classes:
                    __lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs_for_common()

                    # If it doesn't support cache, pass the test
                    if not hasattr(UpperCAmelCase							,					"use_cache"    ):
                           return

                    __lowerCamelCase					:		Any		   =     model_class(UpperCAmelCase    ).to(UpperCAmelCase    )
                    if "use_cache" not in inputs:
                           __lowerCamelCase					:		List[str]		   =     True
                    __lowerCamelCase					:		int		   =     model(**UpperCAmelCase    )

                    # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
                    if "past_key_values" not in outputs:
                           return

                    __lowerCamelCase					:		List[Any]		   =     (
                        getattr(UpperCAmelCase							,					"decoder_layers"							,					UpperCAmelCase    )
                        or getattr(UpperCAmelCase							,					"num_decoder_layers"							,					UpperCAmelCase    )
                        or config.num_hidden_layers
                    )
                    __lowerCamelCase					:		Union[str, Any]		   =     getattr(UpperCAmelCase							,					"num_kv_heads"							,					config.num_attention_heads    )
                    __lowerCamelCase					:		List[Any]		   =     getattr(UpperCAmelCase							,					"d_model"							,					config.hidden_size    )
                    __lowerCamelCase					:		Any		   =     embed_dim // num_attention_heads

                    __lowerCamelCase					:		List[Any]		   =     outputs["past_key_values"]
                    self.assertEqual(len(UpperCAmelCase    )							,					UpperCAmelCase    )

                    __lowerCamelCase					:		List[Any]		   =     inputs["input_ids"].shape
                    for i in range(UpperCAmelCase    ):
                           if config.new_decoder_architecture:
                                  __lowerCamelCase					:		int		   =     config.num_attention_heads
                           elif config.multi_query:
                                  __lowerCamelCase					:		Optional[int]		   =     1
                           self.assertEqual(len(past_kv[0]    )							,					2    )  # K V for the decoder = 2
                           self.assertEqual(
                               past_kv[i][0].shape							,					(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )
                           self.assertEqual(
                               past_kv[i][1].shape							,					(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )

@require_torch
class  _snake_case     (					unittest.TestCase		):






      @slow
      def       lowerCamelCase__     (       self      :			Any    ):
             __lowerCamelCase					:		Optional[Any]		   =     AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"    )
             __lowerCamelCase					:		List[str]		   =     FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"    )
             model.eval()
             model.to(UpperCAmelCase    )
             __lowerCamelCase					:		str		   =     tokenizer("My favorite food is"							,					return_tensors="pt"    ).to(UpperCAmelCase    )

             __lowerCamelCase					:		Optional[int]		   =     (
                 "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
             )

             __lowerCamelCase					:		Union[str, Any]		   =     model.generate(**UpperCAmelCase							,					do_sample=UpperCAmelCase							,					max_new_tokens=19    )
             __lowerCamelCase					:		Union[str, Any]		   =     tokenizer.batch_decode(UpperCAmelCase    )[0]

             self.assertEqual(UpperCAmelCase							,					UpperCAmelCase    )






      @slow
      def       lowerCamelCase__     (       self      :			List[str]    ):
             # The big models are way too big for the CI, so we use tiny random models that resemble their
             # architectures but with much smaller and fewer layers
             for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
                    __lowerCamelCase					:		List[Any]		   =     AutoTokenizer.from_pretrained(UpperCAmelCase    )
                    __lowerCamelCase					:		List[Any]		   =     FalconForCausalLM.from_pretrained(UpperCAmelCase    )
                    model.eval()
                    model.to(UpperCAmelCase    )
                    __lowerCamelCase					:		int		   =     tokenizer("My favorite food is"							,					return_tensors="pt"    ).to(UpperCAmelCase    )

                    # We just test that these run without errors - the models are randomly initialized
                    # and so the actual text outputs will be garbage
                    model.generate(**UpperCAmelCase							,					do_sample=UpperCAmelCase							,					max_new_tokens=4    )
                    model.generate(**UpperCAmelCase							,					do_sample=UpperCAmelCase							,					max_new_tokens=4    )
                    model.generate(**UpperCAmelCase							,					num_beams=2							,					max_new_tokens=4    )







      @slow
      def       lowerCamelCase__     (       self      :			List[str]    ):
             # The big models are way too big for the CI, so we use tiny random models that resemble their
             # architectures but with much smaller and fewer layers
             with torch.no_grad():
                    for repo in [
                        "Rocketknight1/falcon-rw-1b",
                        "Rocketknight1/tiny-random-falcon-7b",
                        "Rocketknight1/tiny-random-falcon-40b",
                    ]:
                           __lowerCamelCase					:		Tuple		   =     AutoTokenizer.from_pretrained(UpperCAmelCase    )
                           __lowerCamelCase					:		List[Any]		   =     FalconForCausalLM.from_pretrained(UpperCAmelCase    )
                           model.eval()
                           model.to(device=UpperCAmelCase    )
                           __lowerCamelCase					:		List[Any]		   =     tokenizer("My favorite food is"							,					return_tensors="pt"    ).to(UpperCAmelCase    )

                           # Test results are the same with and without cache
                           __lowerCamelCase					:		List[Any]		   =     model.generate(**UpperCAmelCase							,					do_sample=UpperCAmelCase							,					max_new_tokens=20							,					use_cache=UpperCAmelCase    )
                           __lowerCamelCase					:		Optional[int]		   =     model.generate(**UpperCAmelCase							,					do_sample=UpperCAmelCase							,					max_new_tokens=20							,					use_cache=UpperCAmelCase    )
                           self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0    )
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


if __name__ == "__main__":
				print(F"""{solution() = }""")