






'''simple docstring'''







import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class _lowercase			:




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Tuple=13    ,							UpperCamelCase__:       List[str]=7    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       int=True    ,							UpperCamelCase__:       Union[str, Any]=False    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       List[str]=99    ,							UpperCamelCase__:       Tuple=32    ,							UpperCamelCase__:       str=5    ,							UpperCamelCase__:       Optional[Any]=4    ,							UpperCamelCase__:       Any=37    ,							UpperCamelCase__:       Dict="gelu"    ,							UpperCamelCase__:       Optional[Any]=0.1    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Any=16    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Union[str, Any]=4    ,							UpperCamelCase__:       List[Any]=None    ,							):
														lowerCamelCase__      :			List[Any] 						= parent
														lowerCamelCase__      :			Tuple 						= batch_size
														lowerCamelCase__      :			Dict 						= seq_length
														lowerCamelCase__      :			List[Any] 						= is_training
														lowerCamelCase__      :			Any 						= use_input_mask
														lowerCamelCase__      :			List[Any] 						= use_token_type_ids
														lowerCamelCase__      :			str 						= use_labels
														lowerCamelCase__      :			Optional[int] 						= vocab_size
														lowerCamelCase__      :			str 						= hidden_size
														lowerCamelCase__      :			List[Any] 						= num_hidden_layers
														lowerCamelCase__      :			List[str] 						= num_attention_heads
														lowerCamelCase__      :			int 						= intermediate_size
														lowerCamelCase__      :			Optional[int] 						= hidden_act
														lowerCamelCase__      :			Optional[Any] 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			str 						= max_position_embeddings
														lowerCamelCase__      :			Any 						= type_vocab_size
														lowerCamelCase__      :			int 						= type_sequence_label_size
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			Optional[int] 						= num_labels
														lowerCamelCase__      :			Any 						= num_choices
														lowerCamelCase__      :			Optional[int] 						= scope




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

														lowerCamelCase__      :			Any 						= None
														if self.use_input_mask:
																					lowerCamelCase__      :			Optional[Any] 						= random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCamelCase__      :			int 						= None
														if self.use_token_type_ids:
																					lowerCamelCase__      :			Dict 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size  )

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

														lowerCamelCase__      :			str 						= self.get_config()

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




							def 	lowerCamelCase_       (							self:       Any  ):
														return OpenLlamaConfig(
														    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    ,							use_stable_embedding=UpperCamelCase__    ,							)




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Optional[Any] 						= OpenLlamaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= 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__:       Optional[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			int 						= True
														lowerCamelCase__      :			List[str] 						= OpenLlamaModel(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(
														    UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							encoder_hidden_states=UpperCamelCase__    ,							encoder_attention_mask=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= model(
														    UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							encoder_hidden_states=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Union[str, 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:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			List[Any] 						= OpenLlamaForCausalLM(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Optional[int] 						= 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:       Optional[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]    ,							):
														lowerCamelCase__      :			Tuple 						= True
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			List[str] 						= OpenLlamaForCausalLM(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()

														# first forward pass
														lowerCamelCase__      :			Union[str, Any] 						= model(
														    UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							encoder_hidden_states=UpperCamelCase__    ,							encoder_attention_mask=UpperCamelCase__    ,							use_cache=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= outputs.past_key_values

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

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

														lowerCamelCase__      :			int 						= model(
														    UpperCamelCase__    ,							attention_mask=UpperCamelCase__    ,							encoder_hidden_states=UpperCamelCase__    ,							encoder_attention_mask=UpperCamelCase__    ,							output_hidden_states=UpperCamelCase__    ,							)["""hidden_states"""][0]
														lowerCamelCase__      :			int 						= 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__      :			Optional[Any] 						= ids_tensor((1,)    ,							output_from_past.shape[-1]  ).item()
														lowerCamelCase__      :			Optional[int] 						= output_from_no_past[:, -3:, random_slice_idx].detach()
														lowerCamelCase__      :			str 						= 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:       List[str]  ):
														lowerCamelCase__      :			int 						= self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	
														)      :			Dict 						= config_and_inputs
														lowerCamelCase__      :			Optional[int] 						= {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							a          = (OpenLlamaForCausalLM,) if is_torch_available() else ()
							a          = (
							    {
							        """feature-extraction""": OpenLlamaModel,
							        """text-classification""": OpenLlamaForSequenceClassification,
							        """text-generation""": OpenLlamaForCausalLM,
							        """zero-shot""": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[str] 						= OpenLlamaModelTester(self  )
														lowerCamelCase__      :			Optional[Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														self.config_tester.run_common_tests()




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			List[Any] 						= self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					lowerCamelCase__      :			Any 						= type
																					self.model_tester.create_and_check_model(*UpperCamelCase__  )




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




							@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""  )
							def 	lowerCamelCase_       (							self:       Any  ):
														pass




							@parameterized.expand([("""linear""",), ("""dynamic""",)]  )
							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__      :			Tuple 						= ids_tensor([1, 10]    ,							config.vocab_size  )
														lowerCamelCase__      :			int 						= 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
														lowerCamelCase__      :			Union[str, Any] 						= OpenLlamaModel(UpperCamelCase__  )
														original_model.to(UpperCamelCase__  )
														original_model.eval()
														lowerCamelCase__      :			List[Any] 						= original_model(UpperCamelCase__  ).last_hidden_state
														lowerCamelCase__      :			int 						= original_model(UpperCamelCase__  ).last_hidden_state

														set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
														lowerCamelCase__      :			Optional[int] 						= {"""type""": scaling_type, """factor""": 10.0}
														lowerCamelCase__      :			Any 						= OpenLlamaModel(UpperCamelCase__  )
														scaled_model.to(UpperCamelCase__  )
														scaled_model.eval()
														lowerCamelCase__      :			Union[str, Any] 						= scaled_model(UpperCamelCase__  ).last_hidden_state
														lowerCamelCase__      :			Any 						= scaled_model(UpperCamelCase__  ).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(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														else:
																					self.assertFalse(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

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









'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			int 						= loader
														lowerCamelCase__      :			str 						= infer
														lowerCamelCase__      :			Optional[int] 						= params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					lowerCamelCase__      :			Optional[int] 						= None
														lowerCamelCase__      :			int 						= loader_batch_size

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					lowerCamelCase__      :			int 						= {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			Tuple 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			str 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			int 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			lowerCamelCase__      :			List[str] 						= None
																												elif isinstance(element[self._loader_batch_index]    ,							torch.Tensor  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			lowerCamelCase__      :			Optional[Any] 						= element[self._loader_batch_index].unsqueeze(0  )
																												elif isinstance(element[self._loader_batch_index]    ,							np.ndarray  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			lowerCamelCase__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														lowerCamelCase__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**self.params  )
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												lowerCamelCase__      :			Union[str, Any] 						= observed_batch_size
																					# Setting internal index to unwrap the batch
																					lowerCamelCase__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= next(self.subiterator  )
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					lowerCamelCase__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# Extremely similar to PipelineIterator in its unpacking mechanism
														# BUT, we have an extra required item which is the presence of `is_last`
														# That is because everything is flattened by `PipelineChunkIterator` we
														# need to keep track of how to regroup here in the original `process`
														# boundaries so that `process` and `postprocess` see the same data.

														# This iterator accumulates items (possibly while unbatching) until it
														# its a `is_last` and then just passes it on to the caller.
														lowerCamelCase__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												lowerCamelCase__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			lowerCamelCase__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




							def __len__(							self:       str  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

