





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_lowerCAmelCase							:      Optional[int]						=	logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCAmelCase							:      int						=	'''cuda''' if torch.cuda.is_available() else '''cpu'''

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : List[Any]=100				,				_lowerCAmelCase : str=" "	) ->						List[str]:
   A_					:				Any          =			text.split(_lowerCAmelCase	)
   return [character.join(text[i : i + n]	).strip() for i in range(0				,				len(_lowerCAmelCase	)				,				_lowerCAmelCase	)]

def      __snake_case						(					_lowerCAmelCase : dict	) ->						dict:
   A_      ,						A_					:				Optional[Any]          =			[], []
   for title, text in zip(documents["title"]				,				documents["text"]	):
      if text is not None:
         for passage in split_text(_lowerCAmelCase	):
            titles.append(title if title is not None else ""	)
            texts.append(_lowerCAmelCase	)
   return {"title": titles, "text": texts}

def      __snake_case						(					_lowerCAmelCase : dict				,				_lowerCAmelCase : DPRContextEncoder				,				_lowerCAmelCase : DPRContextEncoderTokenizerFast	) ->						dict:
   A_					:				Dict          =			ctx_tokenizer(
       documents["title"]				,				documents["text"]				,				truncation=_lowerCAmelCase				,				padding="longest"				,				return_tensors="pt"	)["input_ids"]
   A_					:				Any          =			ctx_encoder(input_ids.to(device=_lowerCAmelCase	)				,				return_dict=_lowerCAmelCase	).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def      __snake_case						(					_lowerCAmelCase : "RagExampleArguments"				,				_lowerCAmelCase : "ProcessingArguments"				,				_lowerCAmelCase : "IndexHnswArguments"				,				) ->						Optional[Any]:
   ######################################
   logger.info("Step 1 - Create the dataset"	)
   ######################################

   # The dataset needed for RAG must have three columns:
   # - title (string): title of the document
   # - text (string): text of a passage of the document
   # - embeddings (array of dimension d): DPR representation of the passage

   # Let's say you have documents in tab-separated csv files with columns "title" and "text"
   assert os.path.isfile(rag_example_args.csv_path	), "Please provide a valid path to a csv file"

   # You can load a Dataset object this way
   A_					:				str          =			load_dataset(
       "csv"				,				data_files=[rag_example_args.csv_path]				,				split="train"				,				delimiter="\t"				,				column_names=["title", "text"]	)

   # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

   # Then split the documents into passages of 100 words
   A_					:				Tuple          =			dataset.map(_lowerCAmelCase				,				batched=_lowerCAmelCase				,				num_proc=processing_args.num_proc	)

   # And compute the embeddings
   A_					:				List[str]          =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=_lowerCAmelCase	)
   A_					:				Dict          =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
   A_					:				int          =			Features(
       {"text": Value("string"	), "title": Value("string"	), "embeddings": Sequence(Value("float32"	)	)}	)  # optional, save as float32 instead of float64 to save space
   A_					:				Optional[int]          =			dataset.map(
       partial(_lowerCAmelCase				,				ctx_encoder=_lowerCAmelCase				,				ctx_tokenizer=_lowerCAmelCase	)				,				batched=_lowerCAmelCase				,				batch_size=processing_args.batch_size				,				features=_lowerCAmelCase				,				)

   # And finally save your dataset
   A_					:				List[str]          =			os.path.join(rag_example_args.output_dir				,				"my_knowledge_dataset"	)
   dataset.save_to_disk(_lowerCAmelCase	)
   # from datasets import load_from_disk
   # dataset = load_from_disk(passages_path)  # to reload the dataset

   ######################################
   logger.info("Step 2 - Index the dataset"	)
   ######################################

   # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
   A_					:				Any          =			faiss.IndexHNSWFlat(index_hnsw_args.d				,				index_hnsw_args.m				,				faiss.METRIC_INNER_PRODUCT	)
   dataset.add_faiss_index("embeddings"				,				custom_index=_lowerCAmelCase	)

   # And save the index
   A_					:				Any          =			os.path.join(rag_example_args.output_dir				,				"my_knowledge_dataset_hnsw_index.faiss"	)
   dataset.get_index("embeddings"	).save(_lowerCAmelCase	)
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

   ######################################
   logger.info("Step 3 - Load RAG"	)
   ######################################

   # Easy way to load the model
   A_					:				List[Any]          =			RagRetriever.from_pretrained(
       rag_example_args.rag_model_name				,				index_name="custom"				,				indexed_dataset=_lowerCAmelCase	)
   A_					:				Dict          =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name				,				retriever=_lowerCAmelCase	)
   A_					:				Dict          =			RagTokenizer.from_pretrained(rag_example_args.rag_model_name	)

   # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
   # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

   ######################################
   logger.info("Step 4 - Have fun"	)
   ######################################

   A_					:				Any          =			rag_example_args.question or "What does Moses' rod turn into ?"
   A_					:				Optional[int]          =			tokenizer.question_encoder(_lowerCAmelCase				,				return_tensors="pt"	)["input_ids"]
   A_					:				Tuple          =			model.generate(_lowerCAmelCase	)
   A_					:				Tuple          =			tokenizer.batch_decode(_lowerCAmelCase				,				skip_special_tokens=_lowerCAmelCase	)[0]
   logger.info("Q: " + question	)
   logger.info("A: " + generated_string	)


@dataclass
class 					__magic_name__  :





     """simple docstring"""



     __UpperCamelCase					    =     field(
         default=str(Path(lowerCamelCase__							).parent / '''test_data''' / '''my_knowledge_dataset.csv'''							)    ,				metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}    ,				)
     __UpperCamelCase					    =     field(
         default=lowerCamelCase__    ,				metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}    ,				)
     __UpperCamelCase					    =     field(
         default='''facebook/rag-sequence-nq'''    ,				metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}    ,				)
     __UpperCamelCase					    =     field(
         default='''facebook/dpr-ctx_encoder-multiset-base'''    ,				metadata={
             '''help''': (
                 '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                 ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
             )
         }    ,				)
     __UpperCamelCase					    =     field(
         default=lowerCamelCase__    ,				metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}    ,				)




@dataclass
class 					__magic_name__  :





     """simple docstring"""



     __UpperCamelCase					    =     field(
         default=lowerCamelCase__    ,				metadata={
             '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
         }    ,				)
     __UpperCamelCase					    =     field(
         default=16    ,				metadata={
             '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
         }    ,				)




@dataclass
class 					__magic_name__  :





     """simple docstring"""



     __UpperCamelCase					    =     field(
         default=7_68    ,				metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}    ,				)
     __UpperCamelCase					    =     field(
         default=1_28    ,				metadata={
             '''help''': (
                 '''The number of bi-directional links created for every new element during the HNSW index construction.'''
             )
         }    ,				)


if __name__ == "__main__":
 logging.basicConfig(level=logging.WARNING)
 logger.setLevel(logging.INFO)

 _lowerCAmelCase							:      List[str]						=	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase							:      Optional[Any]						=	parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _lowerCAmelCase							:      Union[str, Any]						=	rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _lowerCAmelCase							:      List[Any]						=	'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Dict=None				,				_lowerCAmelCase : Dict=None	) ->						Tuple:
   if attention_mask is None:
      A_					:				Tuple          =			np.where(input_ids != config.pad_token_id				,				1				,				0	)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }


@require_flax
class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :List[Any]			,      snake_case       :str=13			,      snake_case       :Dict=7			,      snake_case       :List[Any]=True			,      snake_case       :Dict=False			,      snake_case       :List[Any]=99			,      snake_case       :Dict=16			,      snake_case       :Optional[Any]=2			,      snake_case       :Tuple=4			,      snake_case       :List[Any]=4			,      snake_case       :List[Any]="gelu"			,      snake_case       :List[str]=0.1			,      snake_case       :Optional[int]=0.1			,      snake_case       :Union[str, Any]=20			,      snake_case       :Dict=2			,      snake_case       :Any=1			,      snake_case       :Tuple=0			,      snake_case       :Optional[Any]=16			,      snake_case       :Union[str, Any]=16			,      snake_case       :Optional[Any]=0.02			,      ):





        '''simple docstring'''




        A_					:				str          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Dict          =			seq_length
        A_					:				Any          =			is_training
        A_					:				Optional[Any]          =			use_labels
        A_					:				Optional[Any]          =			vocab_size
        A_					:				Optional[int]          =			hidden_size
        A_					:				Dict          =			num_hidden_layers
        A_					:				Dict          =			num_attention_heads
        A_					:				Tuple          =			intermediate_size
        A_					:				Tuple          =			hidden_act
        A_					:				Union[str, Any]          =			hidden_dropout_prob
        A_					:				Tuple          =			attention_probs_dropout_prob
        A_					:				Any          =			max_position_embeddings
        A_					:				Tuple          =			eos_token_id
        A_					:				Any          =			pad_token_id
        A_					:				Tuple          =			bos_token_id
        A_					:				str          =			embed_dim
        A_					:				List[str]          =			word_embed_proj_dim
        A_					:				Dict          =			initializer_range
        A_					:				Any          =			False



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				str          =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,      self.vocab_size  )			,      3			,      self.vocab_size  )
        A_					:				Union[str, Any]          =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,      dtype=np.intaa  ))			,      -1  )

        A_					:				Any          =			OPTConfig(
            vocab_size=self.vocab_size			,      hidden_size=self.hidden_size			,      num_hidden_layers=self.num_hidden_layers			,      num_attention_heads=self.num_attention_heads			,      ffn_dim=self.intermediate_size			,      dropout=self.hidden_dropout_prob			,      attention_dropout=self.attention_probs_dropout_prob			,      max_position_embeddings=self.max_position_embeddings			,      eos_token_id=self.eos_token_id			,      bos_token_id=self.bos_token_id			,      pad_token_id=self.pad_token_id			,      embed_dim=self.embed_dim			,      is_encoder_decoder=snake_case			,      word_embed_proj_dim=self.word_embed_proj_dim			,      initializer_range=self.initializer_range			,      use_cache=snake_case			,      )
        A_					:				List[str]          =			prepare_opt_inputs_dict(snake_case			,      snake_case  )
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.prepare_config_and_inputs()
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Tuple			,      snake_case       :List[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				int          =			20
        A_					:				int          =			model_class_name(snake_case  )

        A_					:				Any          =			inputs_dict["input_ids"]
        A_					:				Union[str, Any]          =			inputs_dict["attention_mask"]

        A_					:				List[Any]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Dict          =			jnp.ones((input_ids.shape[0], max_length)			,      dtype="i4"  )

        A_					:				str          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Dict          =			model(
            input_ids[:, -1:]			,      attention_mask=snake_case			,      past_key_values=outputs_cache.past_key_values			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			model(snake_case  )

        A_					:				Dict          =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1e-3			,      msg=f"Max diff is {diff}"  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :int			,      snake_case       :Dict			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			20
        A_					:				Any          =			model_class_name(snake_case  )

        A_      ,						A_					:				List[str]          =			(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

        A_					:				Tuple          =			jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
            ]			,      axis=-1			,      )

        A_					:				Optional[int]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Any          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )

        A_					:				int          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )
        A_					:				List[Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, -1:]			,      past_key_values=outputs_cache.past_key_values			,      attention_mask=snake_case			,      position_ids=snake_case			,      )

        A_					:				str          =			model(snake_case			,      attention_mask=snake_case  )

        A_					:				List[str]          =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1e-3			,      msg=f"Max diff is {diff}"  )




@require_flax
class 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase    ,				lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     __UpperCamelCase					    =     () if is_flax_available() else ()



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			FlaxOPTModelTester(self  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        A_      ,						A_					:				List[Any]          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case			,      snake_case			,      snake_case  )



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





        '''simple docstring'''




        A_      ,						A_					:				Any          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case			,      snake_case			,      snake_case  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_					:				Dict          =			model_class_name.from_pretrained("facebook/opt-125m"  )
           A_					:				str          =			np.ones((1, 1)  ) * model.config.eos_token_id
           A_					:				List[str]          =			model(snake_case  )
           self.assertIsNotNone(snake_case  )




@require_sentencepiece
@require_flax
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				str          =			FlaxOPTModel.from_pretrained("facebook/opt-350m"  )
        A_					:				int          =			jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
        A_					:				int          =			model(input_ids=snake_case  ).last_hidden_state
        A_					:				int          =			(1, 11, 512)
        self.assertEqual(output.shape			,      snake_case  )
        A_					:				List[Any]          =			jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]  )
        self.assertTrue(jnp.allclose(output[:, :3, :3]			,      snake_case			,      atol=4e-2  )  )




@require_flax
@slow
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        super().setUp()
        A_					:				Dict          =			"facebook/opt-350m"





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Any          =			FlaxOPTForCausalLM.from_pretrained(self.path_model  )
        A_					:				List[Any]          =			GPTaTokenizer.from_pretrained(self.path_model  )

        A_					:				int          =			[
            "Today is a beautiful day and I want to",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        A_					:				Any          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case			,      add_special_tokens=snake_case  )
        A_					:				int          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        A_					:				Dict          =			jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )

        A_					:				Any          =			jax.jit(snake_case  )
        A_					:				List[str]          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )




@require_flax
@slow
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]



     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			"facebook/opt-125m"

        A_					:				Optional[int]          =			[
            "Today is a beautiful day and I want to",
            "In the city of New York, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        A_					:				str          =			[]

        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				List[str]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				List[Any]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Optional[int]          =			generated_ids[0]

           A_					:				str          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Dict          =			"facebook/opt-350m"

        A_					:				int          =			[
            "Today is a beautiful day and I want to",
            "In the city of San Francisco, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        A_					:				List[Any]          =			[]
        A_					:				Tuple          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Dict          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				Optional[int]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				Optional[int]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Tuple          =			generated_ids[0]

           A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_					:				str          =			"facebook/opt-125m"
        A_					:				Tuple          =			[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Optional[int]          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				Optional[Any]          =			tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]			,      return_tensors="jax"			,      padding=snake_case			,      )

        A_					:				Any          =			jax.jit(model.generate  )

        A_					:				Tuple          =			jit_generate(inputs["input_ids"]			,      attention_mask=inputs["attention_mask"]  ).sequences

        A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )

        self.assertIsNotNone(snake_case			,      snake_case  )





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





        '''simple docstring'''




        A_					:				Optional[int]          =			"facebook/opt-350m"

        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				int          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )

        A_					:				Tuple          =			"left"

        # use different length sentences to test batching
        A_					:				Tuple          =			[
            "Hello, my dog is a little",
            "Today, I",
        ]

        A_					:				List[Any]          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case  )
        A_					:				Any          =			inputs["input_ids"]

        A_					:				int          =			model.generate(input_ids=snake_case			,      attention_mask=inputs["attention_mask"]			,      trace=snake_case  )

        A_					:				Optional[int]          =			tokenizer(sentences[0]			,      return_tensors="jax"  ).input_ids
        A_					:				Dict          =			model.generate(input_ids=snake_case  )

        A_					:				int          =			inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        A_					:				Union[str, Any]          =			tokenizer(sentences[1]			,      return_tensors="jax"  ).input_ids
        A_					:				Optional[int]          =			model.generate(input_ids=snake_case			,      max_length=model.config.max_length - num_paddings  )

        A_					:				List[Any]          =			tokenizer.batch_decode(outputs[0]			,      skip_special_tokens=snake_case  )
        A_					:				Dict          =			tokenizer.decode(output_non_padded[0][0]			,      skip_special_tokens=snake_case  )
        A_					:				Any          =			tokenizer.decode(output_padded[0][0]			,      skip_special_tokens=snake_case  )

        A_					:				Optional[Any]          =			[
            "Hello, my dog is a little bit of a dork.\nI'm a little bit",
            "Today, I was in the middle of a conversation with a friend about the",
        ]
        self.assertListEqual(snake_case			,      snake_case  )
        self.assertListEqual(snake_case			,      [non_padded_sentence, padded_sentence]  )
