


import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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 import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
						from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder






def 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       str=None      ,					__lowerCAmelCase							:       str=None      ,					__lowerCAmelCase							:       str=None      ,					__lowerCAmelCase							:       List[Any]=None      ,					__lowerCAmelCase							:       Tuple=None      ,					):
							if attention_mask is None:
														a__    =  input_ids.ne(config.pad_token_id     )
							if decoder_attention_mask is None:
														a__    =  decoder_input_ids.ne(config.pad_token_id     )
							if head_mask is None:
														a__    =  torch.ones(config.encoder_layers      ,					config.encoder_attention_heads      ,					device=__lowerCAmelCase     )
							if decoder_head_mask is None:
														a__    =  torch.ones(config.decoder_layers      ,					config.decoder_attention_heads      ,					device=__lowerCAmelCase     )
							if cross_attn_head_mask is None:
														a__    =  torch.ones(config.decoder_layers      ,					config.decoder_attention_heads      ,					device=__lowerCAmelCase     )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}







class  snake_case_     :
					def __init__(   self     :List[str]    ,__snake_case     :Optional[Any]    ,__snake_case     :Dict=13    ,__snake_case     :str=7    ,__snake_case     :Tuple=True    ,__snake_case     :Optional[int]=False    ,__snake_case     :List[Any]=99    ,__snake_case     :str=16    ,__snake_case     :Optional[Any]=2    ,__snake_case     :List[str]=4    ,__snake_case     :Optional[Any]=4    ,__snake_case     :int="relu"    ,__snake_case     :Dict=0.1    ,__snake_case     :List[str]=0.1    ,__snake_case     :Union[str, Any]=0.0    ,__snake_case     :Any=0.0    ,__snake_case     :List[str]=20    ,__snake_case     :Tuple=2    ,__snake_case     :Dict=1    ,__snake_case     :Optional[Any]=0    ,)      ->					List[Any]:
												a__    =  parent
												a__    =  batch_size
												a__    =  seq_length
												a__    =  is_training
												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__    =  encoder_layerdrop
												a__    =  decoder_layerdrop
												a__    =  max_position_embeddings
												a__    =  eos_token_id
												a__    =  pad_token_id
												a__    =  bos_token_id
					def 				lowerCamelCase__(   self     :Any   )      ->					List[str]:
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )
												a__    =  self.eos_token_id  # Eos Token
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )

												# we need to clamp the input ids here to avoid having pad token in between
												# this is because for M2M100 the position_ids are prepared such that
												# all pad tokens have pos id = 2 and rest are between 2..seq_length
												# and the seq_length here is seq_length - num_pad_tokens
												# but when using past, there is no way of knowing if the past input ids had
												# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
												# position_ids being off by num_pad_tokens in past input
												a__    =  input_ids.clamp(self.pad_token_id + 1   )
												a__    =  decoder_input_ids.clamp(self.pad_token_id + 1   )

												a__    =  self.get_config()
												a__    =  prepare_mam_aaa_inputs_dict(__snake_case    ,__snake_case    ,__snake_case   )
												return config, inputs_dict
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												return MaMaaaConfig(
												    vocab_size=self.vocab_size    ,d_model=self.hidden_size    ,encoder_layers=self.num_hidden_layers    ,decoder_layers=self.num_hidden_layers    ,encoder_attention_heads=self.num_attention_heads    ,decoder_attention_heads=self.num_attention_heads    ,encoder_ffn_dim=self.intermediate_size    ,decoder_ffn_dim=self.intermediate_size    ,dropout=self.hidden_dropout_prob    ,attention_dropout=self.attention_probs_dropout_prob    ,encoder_layerdrop=self.encoder_layerdrop    ,decoder_layerdrop=self.decoder_layerdrop    ,max_position_embeddings=self.max_position_embeddings    ,eos_token_id=self.eos_token_id    ,bos_token_id=self.bos_token_id    ,pad_token_id=self.pad_token_id    ,)
					def 				lowerCamelCase__(   self     :int   )      ->					str:
												a__   ,      a__    =  self.prepare_config_and_inputs()
												return config, inputs_dict
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  MaMaaaModel(config=__snake_case   ).get_decoder().to(__snake_case   ).eval()
												a__    =  inputs_dict['input_ids']
												a__    =  inputs_dict['attention_mask']
												a__    =  inputs_dict['head_mask']

												# first forward pass
												a__    =  model(__snake_case    ,attention_mask=__snake_case    ,head_mask=__snake_case    ,use_cache=__snake_case   )

												a__   ,      a__    =  outputs.to_tuple()

												# 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)    ,2   )

												# append to next input_ids and
												a__    =  torch.cat([input_ids, next_tokens]    ,dim=-1   )
												a__    =  torch.cat([attention_mask, next_attn_mask]    ,dim=-1   )

												a__    =  model(__snake_case    ,attention_mask=__snake_case   )['last_hidden_state']
												a__    =  model(__snake_case    ,attention_mask=__snake_case    ,past_key_values=__snake_case   )[
												    'last_hidden_state'
												]

												# 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(__snake_case    ,__snake_case    ,atol=1E-2   )   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Any    ,__snake_case     :Dict   )      ->					Optional[int]:
												a__    =  MaMaaaModel(config=__snake_case   ).to(__snake_case   ).eval()
												a__    =  model(**__snake_case   )

												a__    =  outputs.encoder_last_hidden_state
												a__    =  outputs.last_hidden_state

												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  model.get_encoder()
																			encoder.save_pretrained(__snake_case   )
																			a__    =  MaMaaaEncoder.from_pretrained(__snake_case   ).to(__snake_case   )

												a__    =  encoder(inputs_dict['input_ids']    ,attention_mask=inputs_dict['attention_mask']   )[
												    0
												]

												self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  model.get_decoder()
																			decoder.save_pretrained(__snake_case   )
																			a__    =  MaMaaaDecoder.from_pretrained(__snake_case   ).to(__snake_case   )

												a__    =  decoder(
												    input_ids=inputs_dict['decoder_input_ids']    ,attention_mask=inputs_dict['decoder_attention_mask']    ,encoder_hidden_states=__snake_case    ,encoder_attention_mask=inputs_dict['attention_mask']    ,)[0]

												self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3   )







@require_torch
class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[int]					   =    (
					    (
					        MaMaaaModel,
					        MaMaaaForConditionalGeneration,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__      :					int					   =    (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
					UpperCAmelCase__      :					str					   =    (
					    {
					        '''conversational''': MaMaaaForConditionalGeneration,
					        '''feature-extraction''': MaMaaaModel,
					        '''summarization''': MaMaaaForConditionalGeneration,
					        '''text2text-generation''': MaMaaaForConditionalGeneration,
					        '''translation''': MaMaaaForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__      :					int					   =    True
					UpperCAmelCase__      :					Union[str, Any]					   =    True
					UpperCAmelCase__      :					Union[str, Any]					   =    False
					UpperCAmelCase__      :					Union[str, Any]					   =    False
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :int    ,__snake_case     :Any    ,__snake_case     :List[Any]    ,__snake_case     :List[str]    ,__snake_case     :List[Any]   )      ->					Tuple:
												if pipeline_test_casse_name == "TranslationPipelineTests":
																			# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																			# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																			return True

												return False
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												a__    =  MaMaaaModelTester(self   )
												a__    =  ConfigTester(self    ,config_class=__snake_case   )
					def 				lowerCamelCase__(   self     :int   )      ->					Dict:
												self.config_tester.run_common_tests()
					def 				lowerCamelCase__(   self     :List[str]   )      ->					List[Any]:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			a__    =  model_class(__snake_case   )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										model.save_pretrained(__snake_case   )
																										a__   ,      a__    =  model_class.from_pretrained(__snake_case    ,output_loading_info=__snake_case   )
																			self.assertEqual(info['missing_keys']    ,[]   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[Any]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*__snake_case   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					Optional[Any]:
												a__    =  self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_encoder_decoder_model_standalone(*__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					str:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																			a__    =  model_class(__snake_case   )
																			model.to(__snake_case   )
																			model.eval()

																			a__    =  copy.deepcopy(self._prepare_for_class(__snake_case    ,__snake_case   )   )

																			if not self.is_encoder_decoder:
																										a__    =  inputs['input_ids']
																										del inputs["input_ids"]
																			else:
																										a__    =  inputs['input_ids']
																										a__    =  inputs.get('decoder_input_ids'    ,__snake_case   )
																										del inputs["input_ids"]
																										inputs.pop('decoder_input_ids'    ,__snake_case   )

																			a__    =  model.get_input_embeddings()
																			if not self.is_encoder_decoder:
																										a__    =  wte(__snake_case   )
																			else:
																										a__    =  wte(__snake_case   )
																										a__    =  wte(__snake_case   )

																			with torch.no_grad():
																										model(**__snake_case   )[0]



					def 				lowerCamelCase__(   self     :Any   )      ->					Tuple:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs()
												a__    =  input_dict['input_ids']
												a__    =  input_ids.ne(1   ).to(__snake_case   )
												a__    =  MaMaaaForConditionalGeneration(__snake_case   ).eval().to(__snake_case   )
												if torch_device == "cuda":
																			model.half()
												model.generate(__snake_case    ,attention_mask=__snake_case   )
												model.generate(num_beams=4    ,do_sample=__snake_case    ,early_stopping=__snake_case    ,num_return_sequences=3   )






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]     ):
							return torch.tensor(__lowerCAmelCase      ,					dtype=torch.long      ,					device=__lowerCAmelCase     )


snake_case							: Any      =   1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class  snake_case_     (unittest.TestCase   ):
					@cached_property
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Optional[Any]:
												return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[Any]:
												a__    =  MaMaaaModel.from_pretrained('facebook/m2m100_418M'   ).to(__snake_case   )
												a__    =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]   )
												a__    =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]   )
												a__    =  prepare_mam_aaa_inputs_dict(model.config    ,__snake_case    ,__snake_case   )
												with torch.no_grad():
																			a__    =  model(**__snake_case   )[0]
												a__    =  torch.Size((1, 11, 10_24)   )
												self.assertEqual(output.shape    ,__snake_case   )
												# change to expected output here
												a__    =  torch.tensor(
												    [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]    ,device=__snake_case   )
												self.assertTrue(torch.allclose(output[:, :3, :3]    ,__snake_case    ,atol=__snake_case   )   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[int]:
												a__    =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'   ).to(__snake_case   )

												# change to intended input
												a__    =  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]   )
												a__    =  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]   )
												a__    =  prepare_mam_aaa_inputs_dict(model.config    ,__snake_case    ,__snake_case   )
												with torch.no_grad():
																			a__    =  model(**__snake_case   )[0]
												a__    =  torch.Size((1, 11, model.config.vocab_size)   )
												self.assertEqual(output.shape    ,__snake_case   )
												# change to expected output here
												a__    =  torch.tensor(
												    [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]    ,device=__snake_case   )
												self.assertTrue(torch.allclose(output[:, :3, :3]    ,__snake_case    ,atol=__snake_case   )   )



					def 				lowerCamelCase__(   self     :Dict   )      ->					Union[str, Any]:
												a__    =  MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'   ).to(__snake_case   )
												a__    =  MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'    ,src_lang='fr'    ,tgt_lang='en'   )

												a__    =  [
												    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
												    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
												    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
												    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
												    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
												]

												# The below article tests that we don't add any hypotheses outside of the top n_beams
												a__    =  tokenizer(__snake_case    ,padding=__snake_case    ,return_tensors='pt'   )

												a__    =  model.generate(
												    input_ids=dct['input_ids'].to(__snake_case   )    ,attention_mask=dct['attention_mask'].to(__snake_case   )    ,num_beams=5    ,forced_bos_token_id=tokenizer.get_lang_id('en'   )    ,)

												a__    =  [
												    'The NSA case highlights the total absence of intelligence debate',
												    'I think there are two levels of response from the French government.',
												    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
												    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
												    ' communications in France.',
												]

												a__    =  tokenizer.batch_decode(
												    hypotheses_batch.tolist()    ,clean_up_tokenization_spaces=__snake_case    ,skip_special_tokens=__snake_case   )
												assert generated == expected_en








def 							__lowercase     (				__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       int     ):
							if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
														raise ValueError('The length of profit and weight must be same.'     )
							if max_weight <= 0:
														raise ValueError('max_weight must greater than zero.'     )
							if any(p < 0 for p in profit     ):
														raise ValueError('Profit can not be negative.'     )
							if any(w < 0 for w in weight     ):
														raise ValueError('Weight can not be negative.'     )

							# List created to store profit gained for the 1kg in case of each weight
							# respectively.  Calculate and append profit/weight for each element.
							a__    =  [p / w for p, w in zip(__lowerCAmelCase      ,					__lowerCAmelCase     )]

							# Creating a copy of the list and sorting profit/weight in ascending order
							a__    =  sorted(__lowerCAmelCase     )

							# declaring useful variables
							a__    =  len(__lowerCAmelCase     )
							a__    =  0
							a__    =  0
							a__    =  0

							# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
							while limit <= max_weight and i < length:
														# flag value for encountered greatest element in sorted_profit_by_weight
														a__    =  sorted_profit_by_weight[length - i - 1]
														a__    =  profit_by_weight.index(__lowerCAmelCase     )
														a__    =  -1

														# check if the weight encountered is less than the total weight
														# encountered before.
														if max_weight - limit >= weight[index]:
																					limit += weight[index]
																					# Adding profit gained for the given weight 1 ===
																					# weight[index]/weight[index]
																					gain += 1 * profit[index]
														else:
																					# Since the weight encountered is greater than limit, therefore take the
																					# required number of remaining kgs and calculate profit for it.
																					# weight remaining / weight[index]
																					gain += (max_weight - limit) / weight[index] * profit[index]
																					break
														i += 1
							return gain


if __name__ == "__main__":
						print(
						    '''Input profits, weights, and then max_weight (all positive ints) separated by '''
						    '''spaces.'''
						)

						snake_case							: Tuple      =   [int(x) for x in input('''Input profits separated by spaces: ''').split()]
						snake_case							: Optional[int]      =   [int(x) for x in input('''Input weights separated by spaces: ''').split()]
						snake_case							: List[str]      =   int(input('''Max weight allowed: '''))

						# Function Call
						calc_profit(profit, weight, max_weight)




