



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__						:						Tuple          =      logging.get_logger(__name__)


def     UpperCamelCase(						__UpperCamelCase							:	Tuple				):
		if "backbone.0.body" in orig_key:
				lowerCAmelCase_    :  int             =				orig_key.replace('''backbone.0.body'''  ,'''backbone.conv_encoder.model'''				)
		if "transformer" in orig_key:
				lowerCAmelCase_    :  Optional[Any]             =				orig_key.replace('''transformer.'''  ,''''''				)
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						lowerCAmelCase_    :  List[str]             =				orig_key.replace('''norm1'''  ,'''self_attn_layer_norm'''				)
				else:
						lowerCAmelCase_    :  Any             =				orig_key.replace('''norm1'''  ,'''encoder_attn_layer_norm'''				)
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						lowerCAmelCase_    :  List[Any]             =				orig_key.replace('''norm2'''  ,'''final_layer_norm'''				)
				else:
						lowerCAmelCase_    :  Optional[Any]             =				orig_key.replace('''norm2'''  ,'''self_attn_layer_norm'''				)
		if "norm3" in orig_key:
				lowerCAmelCase_    :  List[str]             =				orig_key.replace('''norm3'''  ,'''final_layer_norm'''				)
		if "linear1" in orig_key:
				lowerCAmelCase_    :  List[Any]             =				orig_key.replace('''linear1'''  ,'''fc1'''				)
		if "linear2" in orig_key:
				lowerCAmelCase_    :  int             =				orig_key.replace('''linear2'''  ,'''fc2'''				)
		if "query_embed" in orig_key:
				lowerCAmelCase_    :  Optional[int]             =				orig_key.replace('''query_embed'''  ,'''query_position_embeddings'''				)
		if "cross_attn" in orig_key:
				lowerCAmelCase_    :  Union[str, Any]             =				orig_key.replace('''cross_attn'''  ,'''encoder_attn'''				)

		return orig_key


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
		# transformer decoder self-attention layers
		for i in range(6				):
				# read in weights + bias of input projection layer of self-attention
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
				lowerCAmelCase_    :  Tuple             =				state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""				)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase_    :  int             =				in_proj_weight[:256, :]
				lowerCAmelCase_    :  Tuple             =				in_proj_bias[:256]
				lowerCAmelCase_    :  Optional[int]             =				in_proj_weight[256:512, :]
				lowerCAmelCase_    :  List[Any]             =				in_proj_bias[256:512]
				lowerCAmelCase_    :  Union[str, Any]             =				in_proj_weight[-256:, :]
				lowerCAmelCase_    :  Optional[Any]             =				in_proj_bias[-256:]


def     UpperCamelCase(						):
		lowerCAmelCase_    :  List[Any]             =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_    :  Tuple             =				Image.open(requests.get(__UpperCamelCase  ,stream=__UpperCamelCase				).raw				)

		return im


@torch.no_grad()
def     UpperCamelCase(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Any  ,):
		lowerCAmelCase_    :  int             =				DeformableDetrConfig()
		# set config attributes
		if single_scale:
				lowerCAmelCase_    :  Union[str, Any]             =				1
		lowerCAmelCase_    :  Dict             =				dilation
		lowerCAmelCase_    :  Optional[Any]             =				with_box_refine
		lowerCAmelCase_    :  Union[str, Any]             =				two_stage
		# set labels
		lowerCAmelCase_    :  Union[str, Any]             =				91
		lowerCAmelCase_    :  Union[str, Any]             =				'''huggingface/label-files'''
		lowerCAmelCase_    :  Tuple             =				'''coco-detection-id2label.json'''
		lowerCAmelCase_    :  Union[str, Any]             =				json.load(open(cached_download(hf_hub_url(__UpperCamelCase  ,__UpperCamelCase  ,repo_type='''dataset'''				)				)  ,'''r'''				)				)
		lowerCAmelCase_    :  Tuple             =				{int(__UpperCamelCase				): v for k, v in idalabel.items()}
		lowerCAmelCase_    :  str             =				idalabel
		lowerCAmelCase_    :  List[str]             =				{v: k for k, v in idalabel.items()}

		# load image processor
		lowerCAmelCase_    :  str             =				DeformableDetrImageProcessor(format='''coco_detection'''				)

		# prepare image
		lowerCAmelCase_    :  Optional[Any]             =				prepare_img()
		lowerCAmelCase_    :  List[Any]             =				image_processor(images=__UpperCamelCase  ,return_tensors='''pt'''				)
		lowerCAmelCase_    :  Tuple             =				encoding['''pixel_values''']

		logger.info('''Converting model...'''				)

		# load original state dict
		lowerCAmelCase_    :  Dict             =				torch.load(__UpperCamelCase  ,map_location='''cpu'''				)['''model''']
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_    :  Optional[Any]             =				state_dict.pop(__UpperCamelCase				)
				lowerCAmelCase_    :  Dict             =				val
		# query, key and value matrices need special treatment
		read_in_q_k_v(__UpperCamelCase				)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowerCAmelCase_    :  int             =				'''model.'''
		for key in state_dict.copy().keys():
				if not key.startswith('''class_embed'''				) and not key.startswith('''bbox_embed'''				):
						lowerCAmelCase_    :  Optional[Any]             =				state_dict.pop(__UpperCamelCase				)
						lowerCAmelCase_    :  List[Any]             =				val
    # finally, create HuggingFace model and load state dict
		lowerCAmelCase_    :  List[str]             =				DeformableDetrForObjectDetection(__UpperCamelCase				)
		model.load_state_dict(__UpperCamelCase				)
		model.eval()

		lowerCAmelCase_    :  List[str]             =				'''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(__UpperCamelCase				)
		# verify our conversion
		lowerCAmelCase_    :  str             =				model(pixel_values.to(__UpperCamelCase				)				)

		lowerCAmelCase_    :  Dict             =				torch.tensor(
		    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]				)
		lowerCAmelCase_    :  str             =				torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]				)

		if single_scale:
				lowerCAmelCase_    :  str             =				torch.tensor(
				    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]				)
				lowerCAmelCase_    :  Optional[int]             =				torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]				)

		if single_scale and dilation:
				lowerCAmelCase_    :  str             =				torch.tensor(
				    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]				)
				lowerCAmelCase_    :  Optional[Any]             =				torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]				)

		if with_box_refine:
				lowerCAmelCase_    :  int             =				torch.tensor(
				    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]				)
				lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]				)

		if with_box_refine and two_stage:
				lowerCAmelCase_    :  Any             =				torch.tensor(
				    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]				)
				lowerCAmelCase_    :  Any             =				torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]				)

		print('''Logits:'''  ,outputs.logits[0, :3, :3]				)

		assert torch.allclose(outputs.logits[0, :3, :3]  ,expected_logits.to(__UpperCamelCase				)  ,atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,expected_boxes.to(__UpperCamelCase				)  ,atol=1e-4				)

		print('''Everything ok!'''				)

		# Save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
		Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
		model.save_pretrained(__UpperCamelCase				)
		image_processor.save_pretrained(__UpperCamelCase				)

		# Push to hub
		if push_to_hub:
				lowerCAmelCase_    :  Tuple             =				'''deformable-detr'''
				model_name += "-single-scale" if single_scale else ""
				model_name += "-dc5" if dilation else ""
				model_name += "-with-box-refine" if with_box_refine else ""
				model_name += "-two-stage" if two_stage else ""
				print('''Pushing model to hub...'''				)
				model.push_to_hub(repo_path_or_name=__UpperCamelCase  ,organization='''nielsr'''  ,commit_message='''Add model'''				)


if __name__ == "__main__":
						A__						:						List[str]          =      argparse.ArgumentParser()

						parser.add_argument(
						    '''--checkpoint_path''',
						    type=str,
						    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
						    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
						)
						parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
						parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
						parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
						parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to the folder to output PyTorch model.''',
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)
						A__						:						Optional[Any]          =      parser.parse_args()
						convert_deformable_detr_checkpoint(
						    args.checkpoint_path,
						    args.single_scale,
						    args.dilation,
						    args.with_box_refine,
						    args.two_stage,
						    args.pytorch_dump_folder_path,
						    args.push_to_hub,
						)










from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel



@require_tf
class        __snake_case		:
							_a       =				MarianConfig
							_a       =				{}
							_a       =				'''gelu'''




							def __init__( self  : Dict       ,       A_  : Optional[int]       ,       A_  : Any=1_3       ,       A_  : Any=7       ,       A_  : int=True       ,       A_  : int=False       ,       A_  : str=9_9       ,       A_  : Tuple=3_2       ,       A_  : Dict=2       ,       A_  : Union[str, Any]=4       ,       A_  : Optional[int]=3_7       ,       A_  : Any=0.1       ,       A_  : str=0.1       ,       A_  : Optional[int]=2_0       ,       A_  : Any=2       ,       A_  : Any=1       ,       A_  : Any=0       ,       ):
									lowerCAmelCase_    :  Any             =				parent
									lowerCAmelCase_    :  Dict             =				batch_size
									lowerCAmelCase_    :  Any             =				seq_length
									lowerCAmelCase_    :  Union[str, Any]             =				is_training
									lowerCAmelCase_    :  Any             =				use_labels
									lowerCAmelCase_    :  Tuple             =				vocab_size
									lowerCAmelCase_    :  Optional[int]             =				hidden_size
									lowerCAmelCase_    :  Optional[int]             =				num_hidden_layers
									lowerCAmelCase_    :  Union[str, Any]             =				num_attention_heads
									lowerCAmelCase_    :  List[str]             =				intermediate_size

									lowerCAmelCase_    :  Union[str, Any]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Union[str, Any]             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				max_position_embeddings
									lowerCAmelCase_    :  List[str]             =				eos_token_id
									lowerCAmelCase_    :  Optional[Any]             =				pad_token_id
									lowerCAmelCase_    :  Dict             =				bos_token_id




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  str             =				ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size)
									lowerCAmelCase_    :  Optional[int]             =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)       ,       1)
									lowerCAmelCase_    :  List[str]             =				tf.concat([input_ids, eos_tensor]       ,       axis=1)

									lowerCAmelCase_    :  Optional[Any]             =				ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)

									lowerCAmelCase_    :  Optional[int]             =				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_    :  int             =				prepare_marian_inputs_dict(A_       ,       A_       ,       A_)
									return config, inputs_dict







							def      UpperCAmelCase__    ( self  : int       ,       A_  : int       ,       A_  : str):
									lowerCAmelCase_    :  Any             =				TFMarianModel(config=A_).get_decoder()
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''input_ids''']

									lowerCAmelCase_    :  str             =				input_ids[:1, :]
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''attention_mask'''][:1, :]
									lowerCAmelCase_    :  Dict             =				inputs_dict['''head_mask''']
									lowerCAmelCase_    :  Optional[int]             =				1

									# first forward pass
									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_       ,       head_mask=A_       ,       use_cache=A_)

									lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowerCAmelCase_    :  int             =				ids_tensor((self.batch_size, 3)       ,       config.vocab_size)
									lowerCAmelCase_    :  str             =				tf.cast(ids_tensor((self.batch_size, 3)       ,       2)       ,       tf.inta)

									# append to next input_ids and
									lowerCAmelCase_    :  Optional[int]             =				tf.concat([input_ids, next_tokens]       ,       axis=-1)
									lowerCAmelCase_    :  int             =				tf.concat([attention_mask, next_attn_mask]       ,       axis=-1)

									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_)[0]
									lowerCAmelCase_    :  List[str]             =				model(A_       ,       attention_mask=A_       ,       past_key_values=A_)[0]

									self.parent.assertEqual(next_tokens.shape[1]       ,       output_from_past.shape[1])

									# select random slice
									lowerCAmelCase_    :  Optional[Any]             =				int(ids_tensor((1,)       ,       output_from_past.shape[-1]))
									lowerCAmelCase_    :  Tuple             =				output_from_no_past[:, -3:, random_slice_idx]
									lowerCAmelCase_    :  Optional[Any]             =				output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(A_       ,       A_       ,       rtol=1e-3)





def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]=None  ,__UpperCamelCase							:	Union[str, Any]=None  ,__UpperCamelCase							:	List[Any]=None  ,__UpperCamelCase							:	str=None  ,__UpperCamelCase							:	Optional[int]=None  ,):
		if attention_mask is None:
				lowerCAmelCase_    :  Optional[int]             =				tf.cast(tf.math.not_equal(__UpperCamelCase  ,config.pad_token_id				)  ,tf.inta				)
		if decoder_attention_mask is None:
				lowerCAmelCase_    :  List[str]             =				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_    :  Union[str, Any]             =				tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
		if decoder_head_mask is None:
				lowerCAmelCase_    :  List[str]             =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		if cross_attn_head_mask is None:
				lowerCAmelCase_    :  Union[str, Any]             =				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        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
							_a       =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							_a       =				(TFMarianMTModel,) if is_tf_available() else ()
							_a       =				(
							    {
							        '''conversational''': TFMarianMTModel,
							        '''feature-extraction''': TFMarianModel,
							        '''summarization''': TFMarianMTModel,
							        '''text2text-generation''': TFMarianMTModel,
							        '''translation''': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							_a       =				True
							_a       =				False
							_a       =				False




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Optional[Any]             =				TFMarianModelTester(self)
									lowerCAmelCase_    :  List[str]             =				ConfigTester(self       ,       config_class=A_)




							def      UpperCAmelCase__    ( self  : List[Any]):
									self.config_tester.run_common_tests()







							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  List[Any]             =				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*A_)





@require_tf
class        __snake_case		(  unittest.TestCase		):
							_a       =				1_000  # show more chars for failing integration tests




							@classmethod
							def      UpperCAmelCase__    ( cls  : Optional[Any]):
									lowerCAmelCase_    :  Union[str, Any]             =				F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls




							@cached_property
							def      UpperCAmelCase__    ( self  : List[str]):
									return AutoTokenizer.from_pretrained(self.model_name)




							@property
							def      UpperCAmelCase__    ( self  : Dict):
									return self.tokenizer.eos_token_id




							@cached_property
							def      UpperCAmelCase__    ( self  : int):
									warnings.simplefilter('''error''')
									lowerCAmelCase_    :  TFMarianMTModel             =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
									assert isinstance(A_       ,       A_)
									lowerCAmelCase_    :  int             =				model.config
									self.assertListEqual(c.bad_words_ids       ,       [[c.pad_token_id]])
									self.assertEqual(c.max_length       ,       5_1_2)
									self.assertEqual(c.decoder_start_token_id       ,       c.pad_token_id)
									return model




							def      UpperCAmelCase__    ( self  : Dict       ,       **A_  : Tuple):
									lowerCAmelCase_    :  Optional[int]             =				self.translate_src_text(**A_)
									self.assertListEqual(self.expected_text       ,       A_)







							def      UpperCAmelCase__    ( self  : Any       ,       **A_  : List[str]):
									lowerCAmelCase_    :  List[Any]             =				self.tokenizer(self.src_text       ,       **A_       ,       padding=A_       ,       return_tensors='''tf''')
									lowerCAmelCase_    :  List[str]             =				self.model.generate(
									    model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       max_length=1_2_8)
									lowerCAmelCase_    :  Any             =				self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=A_)
									return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''mt'''
							_a       =				'''en'''
							_a       =				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							_a       =				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Any):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''zh'''
							_a       =				['''My name is Wolfgang and I live in Berlin''']
							_a       =				['''我叫沃尔夫冈 我住在柏林''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : str):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''ROMANCE'''
							_a       =				[
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							_a       =				[
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									self._assert_generated_batch_equal_expected()







							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  Dict             =				TranslationPipeline(self.model       ,       self.tokenizer       ,       framework='''tf''')
									lowerCAmelCase_    :  Union[str, Any]             =				pipeline(self.src_text)
									self.assertEqual(self.expected_text       ,       [x['''translation_text'''] for x in output])





