





import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_lowerCAmelCase							:      int						=	logging.get_logger(__name__)  # pylint: disable=invalid-name


_lowerCAmelCase							:      Dict						=	'''lfs-multipart-upload'''


class 					__magic_name__  (							lowerCamelCase__							):



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :ArgumentParser  ):





        '''simple docstring'''




        A_					:				List[Any]          =			parser.add_parser(
            "lfs-enable-largefiles"			,      help=(
                "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
            )			,      )
        enable_parser.add_argument("path"			,      type=snake_case			,      help="Local path to repository you want to configure."  )
        enable_parser.set_defaults(func=lambda snake_case  : LfsEnableCommand(snake_case  )  )

        A_					:				Any          =			parser.add_parser(
            snake_case			,      help=(
                "Deprecated: use `huggingface-cli` instead. "
                "Command will get called by git-lfs, do not call it directly."
            )			,      )
        upload_parser.set_defaults(func=lambda snake_case  : LfsUploadCommand(snake_case  )  )




class 					__magic_name__  :



     def __init__(							self       :Optional[Any]			,      snake_case       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			args





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





        '''simple docstring'''




        warnings.warn(
            "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."  )
        A_					:				Optional[int]          =			os.path.abspath(self.args.path  )
        if not os.path.isdir(snake_case  ):
           print("This does not look like a valid git repo."  )
           exit(1  )
        subprocess.run(
            "git config lfs.customtransfer.multipart.path transformers-cli".split()			,      check=snake_case			,      cwd=snake_case  )
        subprocess.run(
            f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()			,      check=snake_case			,      cwd=snake_case			,      )
        print("Local repo set up for largefiles"  )




def      __snake_case						(					_lowerCAmelCase : Dict	) ->						str:
   A_					:				List[Any]          =			json.dumps(_lowerCAmelCase	) + "\n"
   sys.stdout.write(_lowerCAmelCase	)
   sys.stdout.flush()

def      __snake_case						(					) ->						Optional[Dict]:
   A_					:				Optional[Any]          =			json.loads(sys.stdin.readline().strip()	)

   if "terminate" in (msg.get("type"	), msg.get("event"	)):
      # terminate message received
      return None

   if msg.get("event"	) not in ("download", "upload"):
      logger.critical("Received unexpected message"	)
      sys.exit(1	)

   return msg


class 					__magic_name__  (							lowerCamelCase__							):



     def __init__(							self       :int			,      snake_case       :str			,      snake_case       :int			,      snake_case       :int  ):





        '''simple docstring'''




        A_					:				List[str]          =			filepath
        A_					:				str          =			seek_from
        A_					:				List[Any]          =			read_limit
        A_					:				int          =			0



     def __enter__(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				Any          =			open(self.filepath			,      "rb"  )
        self.f.seek(self.seek_from  )
        return self



     def __len__(							self       :Dict  ):





        '''simple docstring'''




        A_					:				List[str]          =			os.fstat(self.f.fileno()  ).st_size
        return min(self.read_limit			,      total_length - self.seek_from  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :Optional[Any]=-1  ):





        '''simple docstring'''




        if self.n_seen >= self.read_limit:
           return b""
        A_					:				Dict          =			self.read_limit - self.n_seen
        A_					:				Any          =			self.f.read(remaining_amount if n < 0 else min(snake_case			,      snake_case  )  )
        self.n_seen += len(snake_case  )
        return data



     def __iter__(							self       :Optional[int]  ):





        '''simple docstring'''




        yield self.read(n=4 * 1_024 * 1_024  )





     def __exit__(							self       :Dict			,      *snake_case       :Tuple  ):





        '''simple docstring'''




        self.f.close()




class 					__magic_name__  :



     def __init__(							self       :Any			,      snake_case       :int  ):





        '''simple docstring'''




        A_					:				Any          =			args





     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        A_					:				Dict          =			json.loads(sys.stdin.readline().strip()  )
        if not (init_msg.get("event"  ) == "init" and init_msg.get("operation"  ) == "upload"):
           write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}  )
           sys.exit(1  )

        # The transfer process should use the information it needs from the
        # initiation structure, and also perform any one-off setup tasks it
        # needs to do. It should then respond on stdout with a simple empty
        # confirmation structure, as follows:
        write_msg({}  )

        # After the initiation exchange, git-lfs will send any number of
        # transfer requests to the stdin of the transfer process, in a serial sequence.
        while True:
           A_					:				List[Any]          =			read_msg()
           if msg is None:
              # When all transfers have been processed, git-lfs will send
              # a terminate event to the stdin of the transfer process.
              # On receiving this message the transfer process should
              # clean up and terminate. No response is expected.
              sys.exit(0  )

           A_					:				Any          =			msg["oid"]
           A_					:				Tuple          =			msg["path"]
           A_					:				Tuple          =			msg["action"]["href"]
           A_					:				List[str]          =			msg["action"]["header"]
           A_					:				Optional[Any]          =			int(header.pop("chunk_size"  )  )
           A_					:				List[str]          =			list(header.values()  )

           A_					:				Tuple          =			[]
           for i, presigned_url in enumerate(snake_case  ):
              with FileSlice(snake_case			,      seek_from=i * chunk_size			,      read_limit=snake_case  ) as data:
                 A_					:				int          =			requests.put(snake_case			,      data=snake_case  )
                 r.raise_for_status()
                 parts.append(
                     {
                         "etag": r.headers.get("etag"  ),
                         "partNumber": i + 1,
                     }  )
                 # In order to support progress reporting while data is uploading / downloading,
                 # the transfer process should post messages to stdout
                 write_msg(
                     {
                         "event": "progress",
                         "oid": oid,
                         "bytesSoFar": (i + 1) * chunk_size,
                         "bytesSinceLast": chunk_size,
                     }  )
                 # Not precise but that's ok.

           A_					:				Tuple          =			requests.post(
               snake_case			,      json={
                   "oid": oid,
                   "parts": parts,
               }			,      )
           r.raise_for_status()

           write_msg({"event": "complete", "oid": oid}  )







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]  )
