

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
			import torch

			from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=9_9			,				_a=1_3			,				_a=1_6			,				_a=7			,				_a=True			,				_a=True			,				_a=True			,				_a=False			,				_a=True			,				_a=2			,				_a=3_2			,				_a=4			,				_a=4			,				_a=3_0			,				_a=0			,				_a=1			,				_a=2			,				_a=None			,				)       ->       Any:
												_a      :       int   =						parent
												_a      :       List[Any]   =						batch_size
												_a      :       int   =						decoder_seq_length
												# For common tests
												_a      :       List[Any]   =						self.decoder_seq_length
												_a      :       Tuple   =						is_training
												_a      :       str   =						use_attention_mask
												_a      :       Tuple   =						use_labels

												_a      :       List[str]   =						vocab_size
												_a      :       Tuple   =						d_model
												_a      :       List[str]   =						d_model
												_a      :       Optional[int]   =						decoder_layers
												_a      :       Dict   =						decoder_layers
												_a      :       Dict   =						decoder_ffn_dim
												_a      :       Tuple   =						decoder_attention_heads
												_a      :       List[str]   =						decoder_attention_heads
												_a      :       Optional[int]   =						eos_token_id
												_a      :       int   =						bos_token_id
												_a      :       str   =						pad_token_id
												_a      :       Tuple   =						decoder_start_token_id
												_a      :       List[Any]   =						use_cache
												_a      :       Any   =						max_position_embeddings

												_a      :       Any   =						None
												_a      :       List[Any]   =						decoder_seq_length
												_a      :       str   =						2
												_a      :       List[str]   =						1


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						ids_tensor([self.batch_size, self.decoder_seq_length]			,				self.vocab_size			)

												_a      :       Tuple   =						None
												if self.use_attention_mask:
																	_a      :       str   =						ids_tensor([self.batch_size, self.decoder_seq_length]			,				vocab_size=2			)

												_a      :       str   =						None
												if self.use_labels:
																	_a      :       List[Any]   =						ids_tensor([self.batch_size, self.decoder_seq_length]			,				self.vocab_size			)

												_a      :       Optional[Any]   =						TrOCRConfig(
												    vocab_size=self.vocab_size			,				d_model=self.d_model			,				decoder_layers=self.decoder_layers			,				decoder_ffn_dim=self.decoder_ffn_dim			,				decoder_attention_heads=self.decoder_attention_heads			,				eos_token_id=self.eos_token_id			,				bos_token_id=self.bos_token_id			,				use_cache=self.use_cache			,				pad_token_id=self.pad_token_id			,				decoder_start_token_id=self.decoder_start_token_id			,				max_position_embeddings=self.max_position_embeddings			,				)

												return (config, input_ids, attention_mask, lm_labels)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				)       ->       List[Any]:
												_a      :       List[Any]   =						True
												_a      :       str   =						TrOCRDecoder(config=_a			).to(_a			).eval()
												_a      :       Optional[Any]   =						input_ids[:2]

												input_ids[input_ids == 0] += 1
												# first forward pass
												_a      :       List[Any]   =						model(_a			,				use_cache=_a			)
												_a      :       str   =						model(_a			)
												_a      :       List[Any]   =						model(_a			,				use_cache=_a			)

												self.parent.assertTrue(len(_a			) == len(_a			)			)
												self.parent.assertTrue(len(_a			) == len(_a			) + 1			)

												_a      :       Tuple   =						outputs['''past_key_values''']

												# create hypothetical next token and extent to next_input_ids
												_a      :       Tuple   =						ids_tensor((2, 1)			,				config.vocab_size - 1			) + 1

												# append to next input_ids and
												_a      :       Dict   =						torch.cat([input_ids, next_tokens]			,				dim=-1			)

												_a      :       Any   =						model(_a			)['''last_hidden_state''']
												_a      :       Optional[Any]   =						model(_a			,				past_key_values=_a			)['''last_hidden_state''']

												# select random slice
												_a      :       Optional[Any]   =						ids_tensor((1,)			,				output_from_past.shape[-1]			).item()
												_a      :       Optional[Any]   =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
												_a      :       Optional[Any]   =						output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												assert torch.allclose(_a			,				_a			,				atol=1e-3			)




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						self.prepare_config_and_inputs()
												_a ,   _a ,   _a ,   _a      :       Union[str, Any]   =						config_and_inputs

												_a      :       Optional[Any]   =						{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[Any]             =       (TrOCRForCausalLM,) if is_torch_available() else ()
							UpperCAmelCase__			:		List[str]             =       {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
							UpperCAmelCase__			:		Dict             =       True
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						TrOCRStandaloneDecoderModelTester(self			,				is_training=_a			)
												_a      :       Dict   =						ConfigTester(self			,				config_class=_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.config_tester.run_common_tests()


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past(*_a			)


							def 							__lowercase							(     self			)       ->       List[str]:
												return




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






def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



