





"""simple docstring"""





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase=99      ,						lowercase=13      ,						lowercase=7      ,						lowercase=9      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=32      ,						lowercase=5      ,						lowercase=4      ,						lowercase=37      ,						lowercase=8      ,						lowercase=0.1      ,						lowercase=0.002      ,						lowercase=1      ,						lowercase=0      ,						lowercase=0      ,						lowercase=None      ,						lowercase=None      ,						)		->     Optional[Any]:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       encoder_seq_length
            lowerCAmelCase  						=       decoder_seq_length
            # For common tests
            lowerCAmelCase  						=       self.decoder_seq_length
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       use_attention_mask
            lowerCAmelCase  						=       use_labels
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       d_ff
            lowerCAmelCase  						=       relative_attention_num_buckets
            lowerCAmelCase  						=       dropout_rate
            lowerCAmelCase  						=       initializer_factor
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       pad_token_id
            lowerCAmelCase  						=       decoder_start_token_id
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       decoder_layers





      def     _snake_case			(       self )		->     str:
            return TaConfig.from_pretrained("""google/umt5-base""" )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						)		->     Optional[Any]:
            if attention_mask is None:
                  lowerCAmelCase  						=       input_ids.ne(config.pad_token_id )
            if decoder_attention_mask is None:
                  lowerCAmelCase  						=       decoder_input_ids.ne(config.pad_token_id )
            if head_mask is None:
                  lowerCAmelCase  						=       torch.ones(config.num_hidden_layers      ,						config.num_attention_heads      ,						device=lowercase )
            if decoder_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            if cross_attn_head_mask is None:
                  lowerCAmelCase  						=       torch.ones(
                      config.num_decoder_layers      ,						config.num_attention_heads      ,						device=lowercase )
            return {
                "input_ids": input_ids,
                "decoder_input_ids": decoder_input_ids,
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "head_mask": head_mask,
                "decoder_head_mask": decoder_head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
            }





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       ids_tensor([self.batch_size, self.encoder_seq_length]      ,						self.vocab_size )
            lowerCAmelCase  						=       ids_tensor([self.batch_size, self.decoder_seq_length]      ,						self.vocab_size )

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for NllbMoe 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
            lowerCAmelCase  						=       input_ids.clamp(self.pad_token_id + 1 )
            lowerCAmelCase  						=       decoder_input_ids.clamp(self.pad_token_id + 1 )

            lowerCAmelCase  						=       self.get_config()
            lowerCAmelCase  						=       config.num_attention_heads
            lowerCAmelCase  						=       self.prepare_inputs_dict(lowercase      ,						lowercase      ,						lowercase )
            return config, input_dict





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.prepare_config_and_inputs()
            return config, inputs_dict





      def     _snake_case			(       self )		->     List[str]:
            return TaConfig(
                vocab_size=166      ,						d_model=self.hidden_size      ,						d_ff=self.d_ff      ,						d_kv=self.hidden_size // self.num_attention_heads      ,						num_layers=self.num_hidden_layers      ,						num_decoder_layers=self.decoder_layers      ,						num_heads=self.num_attention_heads      ,						relative_attention_num_buckets=self.relative_attention_num_buckets      ,						dropout_rate=self.dropout_rate      ,						initializer_factor=self.initializer_factor      ,						eos_token_id=self.eos_token_id      ,						bos_token_id=self.pad_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.decoder_start_token_id      ,						)





      def     _snake_case			(       self )		->     Optional[Any]:
            return TaConfig(
                vocab_size=self.vocab_size      ,						d_model=self.hidden_size      ,						d_ff=self.d_ff      ,						d_kv=self.hidden_size // self.num_attention_heads      ,						num_layers=self.num_hidden_layers      ,						num_decoder_layers=self.decoder_layers      ,						num_heads=self.num_attention_heads      ,						relative_attention_num_buckets=self.relative_attention_num_buckets      ,						dropout_rate=self.dropout_rate      ,						initializer_factor=self.initializer_factor      ,						eos_token_id=self.eos_token_id      ,						bos_token_id=self.pad_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.decoder_start_token_id      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       UMTaModel(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(
                input_ids=lowercase      ,						decoder_input_ids=lowercase      ,						attention_mask=lowercase      ,						decoder_attention_mask=lowercase      ,						)
            lowerCAmelCase  						=       model(input_ids=lowercase      ,						decoder_input_ids=lowercase )
            lowerCAmelCase  						=       result.last_hidden_state
            lowerCAmelCase  						=       result.past_key_values
            lowerCAmelCase  						=       result.encoder_last_hidden_state

            self.parent.assertEqual(encoder_output.size()      ,						(self.batch_size, self.encoder_seq_length, self.hidden_size) )
            self.parent.assertEqual(decoder_output.size()      ,						(self.batch_size, self.decoder_seq_length, self.hidden_size) )
            # There should be `num_layers` key value embeddings stored in decoder_past
            self.parent.assertEqual(len(lowercase )      ,						config.num_layers )
            # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
            self.parent.assertEqual(len(decoder_past[0] )      ,						4 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Optional[Any]:
            lowerCAmelCase  						=       UMTaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
            # first forward pass
            lowerCAmelCase  						=       model(lowercase      ,						use_cache=lowercase )
            lowerCAmelCase  						=       model(lowercase )
            lowerCAmelCase  						=       model(lowercase      ,						use_cache=lowercase )

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

            lowerCAmelCase						,		lowerCAmelCase  						=       outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            lowerCAmelCase  						=       ids_tensor((self.batch_size, 1)      ,						config.vocab_size )

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

            lowerCAmelCase  						=       model(lowercase )["""last_hidden_state"""]
            lowerCAmelCase  						=       model(lowercase      ,						past_key_values=lowercase )["""last_hidden_state"""]

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

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(lowercase      ,						lowercase      ,						atol=1e-3 ) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       UMTaModel(config=lowercase ).to(lowercase ).half().eval()
            lowerCAmelCase  						=       model(**lowercase )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(lowercase ).any().item() )







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      _SCREAMING_SNAKE_CASE					=      (UMTaForConditionalGeneration,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE					=      (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      _SCREAMING_SNAKE_CASE					=      [0.8, 0.9]





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       UMTaModelTester(self )





      @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       UMTaModel(config_and_inputs[0] ).to(lowercase )
            with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      lowercase      ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,						f'{tmpdirname}/t5_test.onnx'      ,						export_params=lowercase      ,						opset_version=9      ,						input_names=["""input_ids""", """decoder_input_ids"""]      ,						)





      @unittest.skipIf(torch_device == """cpu"""      ,						"""Cant do half precision""" )
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*lowercase )





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            lowerCAmelCase  						=       config_and_inputs[0]
            lowerCAmelCase  						=       UMTaForConditionalGeneration(lowercase ).eval()
            model.to(lowercase )

            lowerCAmelCase  						=       {
                """head_mask""": torch.zeros(config.num_layers      ,						config.num_heads      ,						device=lowercase ),
                """decoder_head_mask""": torch.zeros(config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase ),
                """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase ),
            }

            for attn_name, (name, mask) in zip(lowercase      ,						head_masking.items() ):
                  lowerCAmelCase  						=       {name: mask}
                  # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                  if name == "head_mask":
                        lowerCAmelCase  						=       torch.ones(
                            config.num_decoder_layers      ,						config.num_heads      ,						device=lowercase )

                  lowerCAmelCase  						=       model.generate(
                      config_and_inputs[1]["""input_ids"""]      ,						num_beams=1      ,						max_length=3      ,						output_attentions=lowercase      ,						return_dict_in_generate=lowercase      ,						**lowercase      ,						)
                  # We check the state of decoder_attentions and cross_attentions just from the last step
                  lowerCAmelCase  						=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                  self.assertEqual(sum([w.sum().item() for w in attn_weights] )      ,						0.0 )





      @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
      def     _snake_case			(       self )		->     Union[str, Any]:
            pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		lowercase					(					unittest.TestCase					):





      @slow
      @unittest.skip(
          """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""      ,						return_dict=lowercase ).to(lowercase )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""google/umt5-small"""      ,						use_fast=lowercase      ,						legacy=lowercase )
            lowerCAmelCase  						=       [
                """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
                """No se como puedo <extra_id_0>.""",
                """This is the reason why we <extra_id_0> them.""",
                """The <extra_id_0> walks in <extra_id_1>, seats""",
                """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
            ]
            lowerCAmelCase  						=       tokenizer(lowercase      ,						return_tensors="""pt"""      ,						padding=lowercase ).input_ids
            # fmt: off
            lowerCAmelCase  						=       torch.tensor(
                [
                    [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
                ] )
            # fmt: on
            torch.testing.assert_allclose(lowercase      ,						lowercase )

            lowerCAmelCase  						=       model.generate(input_ids.to(lowercase ) )
            lowerCAmelCase  						=       [
                """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
                """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            ]
            lowerCAmelCase  						=       tokenizer.batch_decode(lowercase )
            self.assertEqual(lowercase      ,						lowercase )









"""simple docstring"""





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

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







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







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







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







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	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(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	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(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[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__	:						Dict  				=	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__	:						str  				=	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__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						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__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**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(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							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__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	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__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# 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__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						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__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						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__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







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







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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







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







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




