



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict  ):
					__lowerCAmelCase            = MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										__lowerCAmelCase            = [1_44, 1_92, 2_40]
										__lowerCAmelCase            = [16, 32, 64, 96, 1_28, 1_60, 6_40]
					elif "mobilevit_xs" in mobilevit_name:
										__lowerCAmelCase            = [96, 1_20, 1_44]
										__lowerCAmelCase            = [16, 32, 48, 64, 80, 96, 3_84]
					elif "mobilevit_xxs" in mobilevit_name:
										__lowerCAmelCase            = [64, 80, 96]
										__lowerCAmelCase            = [16, 16, 24, 48, 64, 80, 3_20]
										__lowerCAmelCase            = 0.05
										__lowerCAmelCase            = 2.0

					if mobilevit_name.startswith("deeplabv3_"  ):
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = 16
										__lowerCAmelCase            = 21
										__lowerCAmelCase            = "pascal-voc-id2label.json"
					else:
										__lowerCAmelCase            = 10_00
										__lowerCAmelCase            = "imagenet-1k-id2label.json"

					__lowerCAmelCase            = "huggingface/label-files"
					__lowerCAmelCase            = json.load(open(hf_hub_download(__UpperCAmelCase   ,   __UpperCAmelCase   ,   repo_type="dataset"  )   ,   "r"  )  )
					__lowerCAmelCase            = {int(__UpperCAmelCase  ): v for k, v in idalabel.items()}
					__lowerCAmelCase            = idalabel
					__lowerCAmelCase            = {v: k for k, v in idalabel.items()}

					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=False  ):







					for i in range(1   ,   6  ):
										if F"""layer_{i}.""" in name:
															__lowerCAmelCase            = name.replace(F"""layer_{i}."""   ,   F"""encoder.layer.{i - 1}."""  )

					if "conv_1." in name:
										__lowerCAmelCase            = name.replace("conv_1."   ,   "conv_stem."  )
					if ".block." in name:
										__lowerCAmelCase            = name.replace(".block."   ,   "."  )

					if "exp_1x1" in name:
										__lowerCAmelCase            = name.replace("exp_1x1"   ,   "expand_1x1"  )
					if "red_1x1" in name:
										__lowerCAmelCase            = name.replace("red_1x1"   ,   "reduce_1x1"  )
					if ".local_rep.conv_3x3." in name:
										__lowerCAmelCase            = name.replace(".local_rep.conv_3x3."   ,   ".conv_kxk."  )
					if ".local_rep.conv_1x1." in name:
										__lowerCAmelCase            = name.replace(".local_rep.conv_1x1."   ,   ".conv_1x1."  )
					if ".norm." in name:
										__lowerCAmelCase            = name.replace(".norm."   ,   ".normalization."  )
					if ".conv." in name:
										__lowerCAmelCase            = name.replace(".conv."   ,   ".convolution."  )
					if ".conv_proj." in name:
										__lowerCAmelCase            = name.replace(".conv_proj."   ,   ".conv_projection."  )

					for i in range(0   ,   2  ):
										for j in range(0   ,   4  ):
															if F""".{i}.{j}.""" in name:
																				__lowerCAmelCase            = name.replace(F""".{i}.{j}."""   ,   F""".{i}.layer.{j}."""  )

					for i in range(2   ,   6  ):
										for j in range(0   ,   4  ):
															if F""".{i}.{j}.""" in name:
																				__lowerCAmelCase            = name.replace(F""".{i}.{j}."""   ,   F""".{i}."""  )
																				if "expand_1x1" in name:
																									__lowerCAmelCase            = name.replace("expand_1x1"   ,   "downsampling_layer.expand_1x1"  )
																				if "conv_3x3" in name:
																									__lowerCAmelCase            = name.replace("conv_3x3"   ,   "downsampling_layer.conv_3x3"  )
																				if "reduce_1x1" in name:
																									__lowerCAmelCase            = name.replace("reduce_1x1"   ,   "downsampling_layer.reduce_1x1"  )

					for i in range(2   ,   5  ):
										if F""".global_rep.{i}.weight""" in name:
															__lowerCAmelCase            = name.replace(F""".global_rep.{i}.weight"""   ,   ".layernorm.weight"  )
										if F""".global_rep.{i}.bias""" in name:
															__lowerCAmelCase            = name.replace(F""".global_rep.{i}.bias"""   ,   ".layernorm.bias"  )

					if ".global_rep." in name:
										__lowerCAmelCase            = name.replace(".global_rep."   ,   ".transformer."  )
					if ".pre_norm_mha.0." in name:
										__lowerCAmelCase            = name.replace(".pre_norm_mha.0."   ,   ".layernorm_before."  )
					if ".pre_norm_mha.1.out_proj." in name:
										__lowerCAmelCase            = name.replace(".pre_norm_mha.1.out_proj."   ,   ".attention.output.dense."  )
					if ".pre_norm_ffn.0." in name:
										__lowerCAmelCase            = name.replace(".pre_norm_ffn.0."   ,   ".layernorm_after."  )
					if ".pre_norm_ffn.1." in name:
										__lowerCAmelCase            = name.replace(".pre_norm_ffn.1."   ,   ".intermediate.dense."  )
					if ".pre_norm_ffn.4." in name:
										__lowerCAmelCase            = name.replace(".pre_norm_ffn.4."   ,   ".output.dense."  )
					if ".transformer." in name:
										__lowerCAmelCase            = name.replace(".transformer."   ,   ".transformer.layer."  )

					if ".aspp_layer." in name:
										__lowerCAmelCase            = name.replace(".aspp_layer."   ,   "."  )
					if ".aspp_pool." in name:
										__lowerCAmelCase            = name.replace(".aspp_pool."   ,   "."  )
					if "seg_head." in name:
										__lowerCAmelCase            = name.replace("seg_head."   ,   "segmentation_head."  )
					if "segmentation_head.classifier.classifier." in name:
										__lowerCAmelCase            = name.replace("segmentation_head.classifier.classifier."   ,   "segmentation_head.classifier."  )

					if "classifier.fc." in name:
										__lowerCAmelCase            = name.replace("classifier.fc."   ,   "classifier."  )
					elif (not base_model) and ("segmentation_head." not in name):
										__lowerCAmelCase            = "mobilevit." + name

					return name







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=False  ):







					if base_model:
										__lowerCAmelCase            = ""
					else:
										__lowerCAmelCase            = "mobilevit."

					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase            = orig_state_dict.pop(__UpperCAmelCase  )

										if key[:8] == "encoder.":
															__lowerCAmelCase            = key[8:]

										if "qkv" in key:
															__lowerCAmelCase            = key.split("."  )
															__lowerCAmelCase            = int(key_split[0][6:]  ) - 1
															__lowerCAmelCase            = int(key_split[3]  )
															__lowerCAmelCase            = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""  )
															__lowerCAmelCase            = layer.transformer.layer[transformer_num].attention.attention.all_head_size
															__lowerCAmelCase            = (
															    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
															)
															if "weight" in key:
																				__lowerCAmelCase            = val[:dim, :]
																				__lowerCAmelCase            = val[dim : dim * 2, :]
																				__lowerCAmelCase            = val[-dim:, :]
															else:
																				__lowerCAmelCase            = val[:dim]
																				__lowerCAmelCase            = val[dim : dim * 2]
																				__lowerCAmelCase            = val[-dim:]
										else:
															__lowerCAmelCase            = val

					return orig_state_dict







def 			_a   (		):
					__lowerCAmelCase            = "http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase            = Image.open(requests.get(__UpperCAmelCase   ,   stream=__UpperCAmelCase  ).raw  )
					return im







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=False  ):
					__lowerCAmelCase            = get_mobilevit_config(__UpperCAmelCase  )

					# load original state_dict
					__lowerCAmelCase            = torch.load(__UpperCAmelCase   ,   map_location="cpu"  )

					# load 🤗 model
					if mobilevit_name.startswith("deeplabv3_"  ):
										__lowerCAmelCase            = MobileViTForSemanticSegmentation(__UpperCAmelCase  ).eval()
					else:
										__lowerCAmelCase            = MobileViTForImageClassification(__UpperCAmelCase  ).eval()

					__lowerCAmelCase            = convert_state_dict(__UpperCAmelCase   ,   __UpperCAmelCase  )
					model.load_state_dict(__UpperCAmelCase  )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					__lowerCAmelCase            = MobileViTImageProcessor(crop_size=config.image_size   ,   size=config.image_size + 32  )
					__lowerCAmelCase            = image_processor(images=prepare_img()   ,   return_tensors="pt"  )
					__lowerCAmelCase            = model(**__UpperCAmelCase  )
					__lowerCAmelCase            = outputs.logits

					if mobilevit_name.startswith("deeplabv3_"  ):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															__lowerCAmelCase            = torch.tensor(
															    [
															        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
															        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
															        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
															    ]  )
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															__lowerCAmelCase            = torch.tensor(
															    [
															        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
															        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
															        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
															    ]  )
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															__lowerCAmelCase            = torch.tensor(
															    [
															        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
															        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
															        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
															    ]  )
										else:
															raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

										assert torch.allclose(logits[0, :3, :3, :3]   ,   __UpperCAmelCase   ,   atol=1E-4  )
					else:
										assert logits.shape == (1, 10_00)

										if mobilevit_name == "mobilevit_s":
															__lowerCAmelCase            = torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
										elif mobilevit_name == "mobilevit_xs":
															__lowerCAmelCase            = torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
										elif mobilevit_name == "mobilevit_xxs":
															__lowerCAmelCase            = torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
										else:
															raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

										assert torch.allclose(logits[0, :3]   ,   __UpperCAmelCase   ,   atol=1E-4  )

					Path(__UpperCAmelCase  ).mkdir(exist_ok=__UpperCAmelCase  )
					print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(__UpperCAmelCase  )
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
					image_processor.save_pretrained(__UpperCAmelCase  )

					if push_to_hub:
										__lowerCAmelCase            = {
										    "mobilevit_s": "mobilevit-small",
										    "mobilevit_xs": "mobilevit-x-small",
										    "mobilevit_xxs": "mobilevit-xx-small",
										    "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
										    "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
										    "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
										}

										print("Pushing to the hub..."  )
										__lowerCAmelCase            = model_mapping[mobilevit_name]
										image_processor.push_to_hub(__UpperCAmelCase   ,   organization="apple"  )
										model.push_to_hub(__UpperCAmelCase   ,   organization="apple"  )


if __name__ == "__main__":
					UpperCamelCase__					    =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--mobilevit_name""",
					    default="""mobilevit_s""",
					    type=str,
					    help=(
					        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
					        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
					    ),
					)
					parser.add_argument(
					    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					UpperCamelCase__					    =    parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)










from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class     a__					:
						_a							:					List[str]      =					MBartConfig
						_a							:					Union[str, Any]      =					{}
						_a							:					Tuple      =					"""gelu"""






						def __init__(     self	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=False	,    _A=9_9	,    _A=3_2	,    _A=2	,    _A=4	,    _A=3_7	,    _A=0.1	,    _A=0.1	,    _A=2_0	,    _A=2	,    _A=1	,    _A=0	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = eos_token_id
											__lowerCAmelCase            = pad_token_id
											__lowerCAmelCase            = bos_token_id






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size    )
											__lowerCAmelCase            = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )	,    1    )
											__lowerCAmelCase            = tf.concat([input_ids, eos_tensor]	,    axis=1    )

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

											__lowerCAmelCase            = self.config_cls(
											    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_ids=[2]	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    decoder_start_token_id=self.pad_token_id	,    **self.config_updates	,    )
											__lowerCAmelCase            = prepare_mbart_inputs_dict(_A	,    _A	,    _A    )
											return config, inputs_dict






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFMBartModel(config=_A    ).get_decoder()
											__lowerCAmelCase            = inputs_dict["input_ids"]

											__lowerCAmelCase            = input_ids[:1, :]
											__lowerCAmelCase            = inputs_dict["attention_mask"][:1, :]
											__lowerCAmelCase            = inputs_dict["head_mask"]
											__lowerCAmelCase            = 1

											# first forward pass
											__lowerCAmelCase            = model(_A	,    attention_mask=_A	,    head_mask=_A	,    use_cache=_A    )

											__lowerCAmelCase     ,							__lowerCAmelCase            = outputs.to_tuple()
											__lowerCAmelCase            = past_key_values[1]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Any=None   ,   SCREAMING_SNAKE_CASE_							:       int=None   ,   SCREAMING_SNAKE_CASE_							:       List[Any]=None   ,   SCREAMING_SNAKE_CASE_							:       List[Any]=None   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=None   ,   ):
					if attention_mask is None:
										__lowerCAmelCase            = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_   ,   config.pad_token_id  )   ,   tf.inta  )
					if decoder_attention_mask is None:
										__lowerCAmelCase            = tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape   ,   dtype=tf.inta  ),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,   config.pad_token_id  )   ,   tf.inta  ),
										    ]   ,   axis=-1   ,   )
					if head_mask is None:
										__lowerCAmelCase            = tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										__lowerCAmelCase            = tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										__lowerCAmelCase            = tf.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": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}
@require_tf
class     a__					(      snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[Any]      =					(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						_a							:					Any      =					(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						_a							:					Tuple      =					(
						    {
						        """conversational""": TFMBartForConditionalGeneration,
						        """feature-extraction""": TFMBartModel,
						        """summarization""": TFMBartForConditionalGeneration,
						        """text2text-generation""": TFMBartForConditionalGeneration,
						        """translation""": TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:					Optional[int]      =					True
						_a							:					Optional[int]      =					False
						_a							:					List[Any]      =					False






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																# Exception encountered when calling layer '...'
																return True

											return False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFMBartModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*_A    )






@require_sentencepiece
@require_tokenizers
@require_tf
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						]
						_a							:					Optional[Any]      =					[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						]
						_a							:					Any      =					"""facebook/mbart-large-en-ro"""






						@cached_property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return AutoTokenizer.from_pretrained(self.model_name    )






						@cached_property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
											return model






						def        __SCREAMING_SNAKE_CASE(     self	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.translate_src_text(**_A    )
											self.assertListEqual(self.expected_text	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer(self.src_text	,    **_A	,    return_tensors="tf"    )
											__lowerCAmelCase            = self.model.generate(
											    model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    num_beams=2    )
											__lowerCAmelCase            = self.tokenizer.batch_decode(_A	,    skip_special_tokens=_A    )
											return generated_words






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self._assert_generated_batch_equal_expected()




