import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase__     :							List[str]            =      getLogger(__name__)


lowercase__     :							Any            =      """cuda""" if torch.cuda.is_available() else """cpu"""

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__  ,    snake_case__  ,    snake_case__ = 8  ,    snake_case__ = DEFAULT_DEVICE  ,    snake_case__=False  ,    snake_case__="summarization"  ,    snake_case__=None  ,    **snake_case__  ,    )	->      Tuple:
							lowerCAmelCase      						=						Path(snake_case__				).open('''w'''  ,    encoding='''utf-8'''				)
							lowerCAmelCase      						=						str(snake_case__				)
							lowerCAmelCase      						=						AutoModelForSeqaSeqLM.from_pretrained(snake_case__				).to(snake_case__				)
							if fpaa:
														lowerCAmelCase      						=						model.half()

							lowerCAmelCase      						=						AutoTokenizer.from_pretrained(snake_case__				)
							logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"				)  # if this is wrong, check config.model_type.

							lowerCAmelCase      						=						time.time()
							# update config with task specific params
							use_task_specific_params(snake_case__  ,    snake_case__				)
							if prefix is None:
														lowerCAmelCase      						=						prefix or getattr(model.config  ,    '''prefix'''  ,    ''''''				) or ""
							for examples_chunk in tqdm(list(chunks(snake_case__  ,    snake_case__				)				)				):
														lowerCAmelCase      						=						[prefix + text for text in examples_chunk]
														lowerCAmelCase      						=						tokenizer(snake_case__  ,    return_tensors='''pt'''  ,    truncation=snake_case__  ,    padding='''longest'''				).to(snake_case__				)
														lowerCAmelCase      						=						model.generate(
														    input_ids=batch.input_ids  ,    attention_mask=batch.attention_mask  ,    **snake_case__  ,    )
														lowerCAmelCase      						=						tokenizer.batch_decode(snake_case__  ,    skip_special_tokens=snake_case__  ,    clean_up_tokenization_spaces=snake_case__				)
														for hypothesis in dec:
																					fout.write(hypothesis + '''\n'''				)
																					fout.flush()
							fout.close()
							lowerCAmelCase      						=						int(time.time() - start_time				)  # seconds
							lowerCAmelCase      						=						len(snake_case__				)
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs  ,    4				)}

def   SCREAMING_SNAKE_CASE_	(		)	->      List[Any]:
							return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''				)

def   SCREAMING_SNAKE_CASE_	(		snake_case__=True				)	->      Any:
							lowerCAmelCase      						=						argparse.ArgumentParser()
							parser.add_argument('''model_name'''  ,    type=snake_case__  ,    help='''like facebook/bart-large-cnn,t5-base, etc.'''				)
							parser.add_argument('''input_path'''  ,    type=snake_case__  ,    help='''like cnn_dm/test.source'''				)
							parser.add_argument('''save_path'''  ,    type=snake_case__  ,    help='''where to save summaries'''				)
							parser.add_argument('''--reference_path'''  ,    type=snake_case__  ,    required=snake_case__  ,    help='''like cnn_dm/test.target'''				)
							parser.add_argument('''--score_path'''  ,    type=snake_case__  ,    required=snake_case__  ,    default='''metrics.json'''  ,    help='''where to save metrics'''				)
							parser.add_argument('''--device'''  ,    type=snake_case__  ,    required=snake_case__  ,    default=snake_case__  ,    help='''cuda, cuda:1, cpu etc.'''				)
							parser.add_argument(
							    '''--prefix'''  ,    type=snake_case__  ,    required=snake_case__  ,    default=snake_case__  ,    help='''will be added to the begininng of src examples'''				)
							parser.add_argument('''--task'''  ,    type=snake_case__  ,    default='''summarization'''  ,    help='''used for task_specific_params + metrics'''				)
							parser.add_argument('''--bs'''  ,    type=snake_case__  ,    default=8  ,    required=snake_case__  ,    help='''batch size'''				)
							parser.add_argument(
							    '''--n_obs'''  ,    type=snake_case__  ,    default=-1  ,    required=snake_case__  ,    help='''How many observations. Defaults to all.'''				)
							parser.add_argument('''--fp16'''  ,    action='''store_true'''				)
							parser.add_argument('''--dump-args'''  ,    action='''store_true'''  ,    help='''print the custom hparams with the results'''				)
							parser.add_argument(
							    '''--info'''  ,    nargs='''?'''  ,    type=snake_case__  ,    const=datetime_now()  ,    help=(
							        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
							        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
							    )  ,    )
							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
							lowerCAmelCase      						=						parser.parse_known_args()
							lowerCAmelCase      						=						parse_numeric_n_bool_cl_kwargs(snake_case__				)
							if parsed_args and verbose:
														print(f"parsed the following generate kwargs: {parsed_args}"				)
							lowerCAmelCase      						=						[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
							if args.n_obs > 0:
														lowerCAmelCase      						=						examples[: args.n_obs]
							Path(args.save_path				).parent.mkdir(exist_ok=snake_case__				)

							if args.reference_path is None and Path(args.score_path				).exists():
														warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c."				)

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError('''Can\'t mix --fp16 and --device cpu'''				)

							lowerCAmelCase      						=						generate_summaries_or_translations(
							    snake_case__  ,    args.save_path  ,    args.model_name  ,    batch_size=args.bs  ,    device=args.device  ,    fpaa=args.fpaa  ,    task=args.task  ,    prefix=args.prefix  ,    **snake_case__  ,    )

							if args.reference_path is None:
														return {}

							# Compute scores
							lowerCAmelCase      						=						calculate_bleu if "translation" in args.task else calculate_rouge
							lowerCAmelCase      						=						[x.rstrip() for x in open(args.save_path				).readlines()]
							lowerCAmelCase      						=						[x.rstrip() for x in open(args.reference_path				).readlines()][: len(snake_case__				)]
							lowerCAmelCase      						=						score_fn(snake_case__  ,    snake_case__				)
							scores.update(snake_case__				)

							if args.dump_args:
														scores.update(snake_case__				)
							if args.info:
														lowerCAmelCase      						=						args.info

							if verbose:
														print(snake_case__				)

							if args.score_path is not None:
														json.dump(snake_case__  ,    open(args.score_path  ,    '''w'''				)				)

							return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, 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
 a  :      Tuple            =    """platform"""

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				):
   if attention_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.where(input_ids != config.pad_token_id							,				1							,				0					)
   if decoder_attention_mask is None:
      lowerCAmelCase_:							Union[str, Any]       =	np.where(decoder_input_ids != config.pad_token_id							,				1							,				0					)
   if head_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      lowerCAmelCase_:							str       =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      lowerCAmelCase_:							Any       =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }

class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=7       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=99       ,						lowerCamelCase__=16       ,						lowerCamelCase__=2       ,						lowerCamelCase__=4       ,						lowerCamelCase__=4       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=32       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0.0_2       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							Any       =	seq_length
       lowerCAmelCase_:							Tuple       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							str       =	hidden_size
       lowerCAmelCase_:							Union[str, Any]       =	num_hidden_layers
       lowerCAmelCase_:							List[str]       =	num_attention_heads
       lowerCAmelCase_:							Dict       =	intermediate_size
       lowerCAmelCase_:							int       =	hidden_act
       lowerCAmelCase_:							Any       =	hidden_dropout_prob
       lowerCAmelCase_:							str       =	attention_probs_dropout_prob
       lowerCAmelCase_:							Union[str, Any]       =	max_position_embeddings
       lowerCAmelCase_:							Any       =	eos_token_id
       lowerCAmelCase_:							Union[str, Any]       =	pad_token_id
       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							Optional[int]       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,						self.vocab_size )       ,						3       ,						self.vocab_size )
       lowerCAmelCase_:							int       =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,						dtype=np.intaa ))       ,						-1 )

       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )

       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=self.hidden_size       ,						encoder_layers=self.num_hidden_layers       ,						decoder_layers=self.num_hidden_layers       ,						encoder_attention_heads=self.num_attention_heads       ,						decoder_attention_heads=self.num_attention_heads       ,						encoder_ffn_dim=self.intermediate_size       ,						decoder_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       ,						initializer_range=self.initializer_range       ,						use_cache=lowerCamelCase__       ,						)
       lowerCAmelCase_:							Optional[Any]       =	prepare_blenderbot_inputs_dict(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       return config, inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	self.prepare_config_and_inputs()
       return config, inputs_dict




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	20
       lowerCAmelCase_:							int       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       lowerCAmelCase_:							List[str]       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,						dtype="i4" )

       lowerCAmelCase_:							Optional[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)
       lowerCAmelCase_:							Optional[Any]       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Optional[Any]       =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,						dtype="i4" )
       lowerCAmelCase_:							Optional[int]       =	model.decode(
           decoder_input_ids[:, -1:]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Any       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							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}''' )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	20
       lowerCAmelCase_:							Union[str, Any]       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       lowerCAmelCase_:							int       =	jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
           ]       ,						axis=-1       ,						)

       lowerCAmelCase_:							Any       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,						)

       lowerCAmelCase_:							Tuple       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)
       lowerCAmelCase_:							str       =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,						dtype="i4" )
       lowerCAmelCase_:							Tuple       =	model.decode(
           decoder_input_ids[:, -1:]       ,						lowerCamelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						decoder_attention_mask=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    99




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]       ,						dtype=np.intaa       ,						)

       lowerCAmelCase_:							Optional[int]       =	input_ids.shape[0]
       lowerCAmelCase_:							str       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=24       ,						encoder_layers=2       ,						decoder_layers=2       ,						encoder_attention_heads=2       ,						decoder_attention_heads=2       ,						encoder_ffn_dim=32       ,						decoder_ffn_dim=32       ,						max_position_embeddings=48       ,						eos_token_id=2       ,						pad_token_id=1       ,						bos_token_id=0       ,						)
       return config, input_ids, batch_size




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	self._get_config_and_data()
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=14       ,						encoder_layers=2       ,						decoder_layers=2       ,						encoder_attention_heads=2       ,						decoder_attention_heads=2       ,						encoder_ffn_dim=8       ,						decoder_ffn_dim=8       ,						max_position_embeddings=48       ,						)
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							str       =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__       ,						decoder_input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )
       lowerCAmelCase_:							List[Any]       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       lowerCAmelCase_:							Any       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       self.assertEqual(shifted.shape       ,						input_ids.shape )
       self.assertEqual(lowerCamelCase__       ,						n_pad_before - 1 )
       self.assertTrue(np.equal(shifted[:, 0]       ,						2 ).all() )





@require_flax
class 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase ,  UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    True
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	FlaxBlenderbotSmallModelTester(self )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[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(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ )
             lowerCAmelCase_:							Union[str, Any]       =	model_class(lowerCamelCase__ )

             @jax.jit
             def encode_jitted(lowerCamelCase__       ,						lowerCamelCase__=None       ,						**lowerCamelCase__ ):
                return model.encode(input_ids=lowerCamelCase__       ,						attention_mask=lowerCamelCase__ )

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Optional[int]       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							int       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             self.assertEqual(len(lowerCamelCase__ )       ,						len(lowerCamelCase__ ) )
             for jitted_output, output in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
                self.assertEqual(jitted_output.shape       ,						output.shape )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__ ):
             lowerCAmelCase_:							Dict       =	model_class(lowerCamelCase__ )
             lowerCAmelCase_:							Optional[int]       =	model.encode(inputs_dict["input_ids"]       ,						inputs_dict["attention_mask"] )

             lowerCAmelCase_:							Any       =	{
                 "decoder_input_ids": inputs_dict["decoder_input_ids"],
                 "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                 "encoder_outputs": encoder_outputs,
             }

             @jax.jit
             def decode_jitted(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
                return model.decode(
                    decoder_input_ids=lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						encoder_outputs=lowerCamelCase__       ,						)

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Dict       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							Union[str, Any]       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             self.assertEqual(len(lowerCamelCase__ )       ,						len(lowerCamelCase__ ) )
             for jitted_output, output in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
                self.assertEqual(jitted_output.shape       ,						output.shape )




    @slow
    def      _a    (    self ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:							Any       =	model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          lowerCAmelCase_:							str       =	np.ones((1, 1) ) * model.config.eos_token_id
          lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )