
'''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  A  :





	def __init__(       self      :							Optional[int]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							int=13   ,				__magic_name__      :							List[Any]=7   ,				__magic_name__      :							Any=True   ,				__magic_name__      :							Optional[int]=True   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=99   ,				__magic_name__      :							List[Any]=32   ,				__magic_name__      :							str=5   ,				__magic_name__      :							int=4   ,				__magic_name__      :							Dict=37   ,				__magic_name__      :							Tuple="gelu"   ,				__magic_name__      :							Union[str, Any]=0.1   ,				__magic_name__      :							Optional[int]=0.1   ,				__magic_name__      :							List[str]=512   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							Any=2   ,				__magic_name__      :							Any=0.02   ,				__magic_name__      :							Tuple=3   ,				__magic_name__      :							List[str]=4   ,				__magic_name__      :							Union[str, Any]=None   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				seq_length
				lowerCAmelCase__										=				is_training
				lowerCAmelCase__										=				use_input_mask
				lowerCAmelCase__										=				use_token_type_ids
				lowerCAmelCase__										=				use_labels
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				type_vocab_size
				lowerCAmelCase__										=				type_sequence_label_size
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				num_labels
				lowerCAmelCase__										=				num_choices
				lowerCAmelCase__										=				scope





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size				)

				lowerCAmelCase__										=				None
				if self.use_input_mask:
							lowerCAmelCase__										=				random_attention_mask([self.batch_size, self.seq_length]				)

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

				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None
				if self.use_labels:
							lowerCAmelCase__										=				ids_tensor([self.batch_size]   ,				self.type_sequence_label_size				)
							lowerCAmelCase__										=				ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels				)
							lowerCAmelCase__										=				ids_tensor([self.batch_size]   ,				self.num_choices				)

				lowerCAmelCase__										=				self.get_config()

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				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=__magic_name__   ,				initializer_range=self.initializer_range   ,				use_stable_embedding=__magic_name__   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaModel(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)
				lowerCAmelCase__										=				model(__magic_name__				)
				self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Any   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				OpenLlamaModel(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				)
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				)
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)
				self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Optional[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaForCausalLM(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Any   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				OpenLlamaForCausalLM(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()

				# first forward pass
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				use_cache=__magic_name__   ,				)
				lowerCAmelCase__										=				outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				lowerCAmelCase__										=				ids_tensor((self.batch_size, 3)   ,				config.vocab_size				)
				lowerCAmelCase__										=				ids_tensor((self.batch_size, 3)   ,				vocab_size=2				)

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

				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				output_hidden_states=__magic_name__   ,				)["hidden_states"][0]
				lowerCAmelCase__										=				model(
				    __magic_name__   ,				attention_mask=__magic_name__   ,				encoder_hidden_states=__magic_name__   ,				encoder_attention_mask=__magic_name__   ,				past_key_values=__magic_name__   ,				output_hidden_states=__magic_name__   ,				)["hidden_states"][0]

				# select random slice
				lowerCAmelCase__										=				ids_tensor((1,)   ,				output_from_past.shape[-1]				).item()
				lowerCAmelCase__										=				output_from_no_past[:, -3:, random_slice_idx].detach()
				lowerCAmelCase__										=				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(__magic_name__   ,				__magic_name__   ,				atol=1E-3				)				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.prepare_config_and_inputs()
				(
				    (
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,(
				    lowerCAmelCase__
				)						,
				)										=				config_and_inputs
				lowerCAmelCase__										=				{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Dict   =  (
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	snake_case__   :Tuple   =  (OpenLlamaForCausalLM,) if is_torch_available() else ()
	snake_case__   :List[str]   =  (
	    {
	        'feature-extraction': OpenLlamaModel,
	        'text-classification': OpenLlamaForSequenceClassification,
	        'text-generation': OpenLlamaForCausalLM,
	        'zero-shot': OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case__   :List[str]   =  False
	snake_case__   :Any   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				OpenLlamaModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__   ,				hidden_size=37				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				self.config_tester.run_common_tests()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							lowerCAmelCase__										=				type
							self.model_tester.create_and_check_model(*__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				"single_label_classification"
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				"multi_label_classification"
				lowerCAmelCase__										=				input_dict["input_ids"]
				lowerCAmelCase__										=				input_ids.ne(1				).to(__magic_name__				)
				lowerCAmelCase__										=				ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]   ,				self.model_tester.type_sequence_label_size				).to(torch.float				)
				lowerCAmelCase__										=				OpenLlamaForSequenceClassification(__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				labels=__magic_name__				)
				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 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				pass







	@parameterized.expand([("linear",), ("dynamic",)]				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				ids_tensor([1, 10]   ,				config.vocab_size				)
				lowerCAmelCase__										=				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__										=				OpenLlamaModel(__magic_name__				)
				original_model.to(__magic_name__				)
				original_model.eval()
				lowerCAmelCase__										=				original_model(__magic_name__				).last_hidden_state
				lowerCAmelCase__										=				original_model(__magic_name__				).last_hidden_state

				set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
				lowerCAmelCase__										=				{"type": scaling_type, "factor": 10.0}
				lowerCAmelCase__										=				OpenLlamaModel(__magic_name__				)
				scaled_model.to(__magic_name__				)
				scaled_model.eval()
				lowerCAmelCase__										=				scaled_model(__magic_name__				).last_hidden_state
				lowerCAmelCase__										=				scaled_model(__magic_name__				).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(__magic_name__   ,				__magic_name__   ,				atol=1E-5				)				)
				else:
							self.assertFalse(torch.allclose(__magic_name__   ,				__magic_name__   ,				atol=1E-5				)				)

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







'''simple docstring'''
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCAmelCase					:    Dict           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     enum.Enum):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							1


@add_end_docstrings(snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""generated"""







				def __init__(       self						,				*A_						,				**A_     )->		Optional[int]:







											'''simple docstring'''






											super().__init__(*A_						,				**A_     )

											self.check_model_type(
											    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
											    if self.framework == 'tf'
											    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				A_=None						,				A_=None						,				A_=None						,				A_=None						,				**A_						,				)->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											if truncation is not None:
																		UpperCamelCase   			=     truncation

											UpperCamelCase   			=     generate_kwargs

											UpperCamelCase   			=     {}
											if return_tensors is not None and return_type is None:
																		UpperCamelCase   			=     ReturnType.TENSORS if return_tensors else ReturnType.TEXT
											if return_type is not None:
																		UpperCamelCase   			=     return_type

											if clean_up_tokenization_spaces is not None:
																		UpperCamelCase   			=     clean_up_tokenization_spaces

											if stop_sequence is not None:
																		UpperCamelCase   			=     self.tokenizer.encode(A_						,				add_special_tokens=A_     )
																		if len(A_     ) > 1:
																									warnings.warn(
																									    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																									    ' the stop sequence will be used as the stop sequence string in the interim.'     )
																		UpperCamelCase   			=     stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Optional[int]:







											'''simple docstring'''






											return True







				def 						UpperCAmelCase_    (       self						,				*A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.model.config.prefix if self.model.config.prefix is not None else ''
											if isinstance(args[0]						,				A_     ):
																		if self.tokenizer.pad_token_id is None:
																									raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'     )
																		UpperCamelCase   			=     ([prefix + arg for arg in args[0]],)
																		UpperCamelCase   			=     True

											elif isinstance(args[0]						,				A_     ):
																		UpperCamelCase   			=     (prefix + args[0],)
																		UpperCamelCase   			=     False
											else:
																		raise ValueError(
																		    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''     )
											UpperCamelCase   			=     self.tokenizer(*A_						,				padding=A_						,				truncation=A_						,				return_tensors=self.framework     )
											# This is produced by tokenizers but is an invalid generate kwargs
											if "token_type_ids" in inputs:
																		del inputs["token_type_ids"]
											return inputs







				def __call__(       self						,				*A_						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     super().__call__(*A_						,				**A_     )
											if (
											    isinstance(args[0]						,				A_     )
											    and all(isinstance(A_						,				A_     ) for el in args[0]     )
											    and all(len(A_     ) == 1 for res in result     )
											):
																		return [res[0] for res in result]
											return result







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=TruncationStrategy.DO_NOT_TRUNCATE						,				**A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self._parse_and_tokenize(A_						,				truncation=A_						,				**A_     )
											return inputs







				def 						UpperCAmelCase_    (       self						,				A_						,				**A_     )->		int:







											'''simple docstring'''






											if self.framework == "pt":
																		UpperCamelCase			,	UpperCamelCase   			=     model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		UpperCamelCase			,	UpperCamelCase   			=     tf.shape(model_inputs['input_ids']     ).numpy()

											UpperCamelCase   			=     generate_kwargs.get('min_length'						,				self.model.config.min_length     )
											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )
											self.check_inputs(A_						,				generate_kwargs['min_length']						,				generate_kwargs['max_length']     )
											UpperCamelCase   			=     self.model.generate(**A_						,				**A_     )
											UpperCamelCase   			=     output_ids.shape[0]
											if self.framework == "pt":
																		UpperCamelCase   			=     output_ids.reshape(A_						,				out_b // in_b						,				*output_ids.shape[1:]     )
											elif self.framework == "tf":
																		UpperCamelCase   			=     tf.reshape(A_						,				(in_b, out_b // in_b, *output_ids.shape[1:])     )
											return {"output_ids": output_ids}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=ReturnType.TEXT						,				A_=False     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     []
											for output_ids in model_outputs["output_ids"][0]:
																		if return_type == ReturnType.TENSORS:
																									UpperCamelCase   			=     {F'''{self.return_name}_token_ids''': output_ids}
																		elif return_type == ReturnType.TEXT:
																									UpperCamelCase   			=     {
																									    F'''{self.return_name}_text''': self.tokenizer.decode(
																									        A_						,				skip_special_tokens=A_						,				clean_up_tokenization_spaces=A_						,				)
																									}
																		records.append(A_     )
											return records


@add_end_docstrings(snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""summary"""







				def __call__(       self						,				*A_						,				**A_     )->		Optional[int]:







											'''simple docstring'''






											return super().__call__(*A_						,				**A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		bool:







											'''simple docstring'''






											if max_length < min_length:
																		logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''     )

											if input_length < max_length:
																		logger.warning(
																		    F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
																		    'a summarization task, where outputs shorter than the input are typically wanted, you might '
																		    F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''     )


@add_end_docstrings(snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""translation"""







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		List[Any]:







											'''simple docstring'''






											if input_length > 0.9 * max_length:
																		logger.warning(
																		    F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
																		    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'     )
											return True







				def 						UpperCAmelCase_    (       self						,				*A_						,				A_=TruncationStrategy.DO_NOT_TRUNCATE						,				A_=None						,				A_=None     )->		Dict:







											'''simple docstring'''






											if getattr(self.tokenizer						,				'_build_translation_inputs'						,				A_     ):
																		return self.tokenizer._build_translation_inputs(
																		    *A_						,				return_tensors=self.framework						,				truncation=A_						,				src_lang=A_						,				tgt_lang=A_     )
											else:
																		return super()._parse_and_tokenize(*A_						,				truncation=A_     )







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				**A_     )->		str:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     super()._sanitize_parameters(**A_     )
											if src_lang is not None:
																		UpperCamelCase   			=     src_lang
											if tgt_lang is not None:
																		UpperCamelCase   			=     tgt_lang
											if src_lang is None and tgt_lang is None:
																		# Backward compatibility, direct arguments use is preferred.
																		UpperCamelCase   			=     kwargs.get('task'						,				self.task     )
																		UpperCamelCase   			=     task.split('_'     )
																		if task and len(A_     ) == 4:
																									# translation, XX, to YY
																									UpperCamelCase   			=     items[1]
																									UpperCamelCase   			=     items[3]
											return preprocess_params, forward_params, postprocess_params







				def __call__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






											return super().__call__(*A_						,				**A_     )




