
import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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





def    _a (						):
 lowerCamelCase		:     List[str]          =				DetaConfig(
     num_queries=900,     encoder_ffn_dim=2048,     decoder_ffn_dim=2048,     num_feature_levels=5,     assign_first_stage=lowerCamelCase,     with_box_refine=lowerCamelCase,     two_stage=lowerCamelCase,     )

 # set labels
 lowerCamelCase		:     Any          =				91
 lowerCamelCase		:     Any          =				"""huggingface/label-files"""
 lowerCamelCase		:     Optional[int]          =				"""coco-detection-id2label.json"""
 lowerCamelCase		:     Optional[Any]          =				json.load(open(cached_download(hf_hub_url(lowerCamelCase,     lowerCamelCase,     repo_type="""dataset"""							)							),     """r"""							)							)
 lowerCamelCase		:     List[Any]          =				{int(lowerCamelCase							): v for k, v in idalabel.items()}
 lowerCamelCase		:     Optional[int]          =				idalabel
 lowerCamelCase		:     Optional[int]          =				{v: k for k, v in idalabel.items()}

 return config





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     int          =				[]

 # stem
 # fmt: off
 rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")							)
 rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")							)
 rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")							)
 rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")							)
 rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")							)
 # stages
 for stage_idx in range(len(config.backbone_config.depths							)							):
  for layer_idx in range(config.backbone_config.depths[stage_idx]							):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
        )							)
   # 3 convs
   for i in range(3							):
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
        )							)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
            F'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
        )							)
    # transformer encoder
 for i in range(config.encoder_layers							):
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')							)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')							)

 # transformer decoder
 for i in range(config.decoder_layers							):
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')							)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')							)

 # fmt: on

 return rename_keys





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Dict          =				dct.pop(lowerCamelCase							)
 lowerCamelCase		:     Any          =				val





def    _a (						lowerCamelCase,     lowerCamelCase							):
 # transformer decoder self-attention layers
 lowerCamelCase		:     Any          =				config.d_model
 for i in range(config.decoder_layers							):
  # read in weights + bias of input projection layer of self-attention
  lowerCamelCase		:     Union[str, Any]          =				state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
  lowerCamelCase		:     Tuple          =				state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''							)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase		:     str          =				in_proj_weight[:hidden_size, :]
  lowerCamelCase		:     Optional[Any]          =				in_proj_bias[:hidden_size]
  lowerCamelCase		:     List[str]          =				in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  lowerCamelCase		:     int          =				in_proj_bias[hidden_size : hidden_size * 2]
  lowerCamelCase		:     Dict          =				in_proj_weight[-hidden_size:, :]
  lowerCamelCase		:     Union[str, Any]          =				in_proj_bias[-hidden_size:]





def    _a (						):
 lowerCamelCase		:     Optional[Any]          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCamelCase		:     int          =				Image.open(requests.get(lowerCamelCase,     stream=lowerCamelCase							).raw							)

 return im





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     int          =				get_deta_config()

 # load original state dict
 if model_name == "deta-resnet-50":
  lowerCamelCase		:     Optional[int]          =				"""adet_checkpoint0011.pth"""
 elif model_name == "deta-resnet-50-24-epochs":
  lowerCamelCase		:     Union[str, Any]          =				"""adet_2x_checkpoint0023.pth"""
 else:
  raise ValueError(F'''Model name {model_name} not supported'''							)
 lowerCamelCase		:     int          =				hf_hub_download(repo_id="""nielsr/deta-checkpoints""",     filename=lowerCamelCase							)
 lowerCamelCase		:     Tuple          =				torch.load(lowerCamelCase,     map_location="""cpu"""							)["""model"""]

 # rename keys
 lowerCamelCase		:     List[Any]          =				create_rename_keys(lowerCamelCase							)
 for src, dest in rename_keys:
  rename_key(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							)
 read_in_decoder_q_k_v(lowerCamelCase,     lowerCamelCase							)

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   lowerCamelCase		:     Any          =				state_dict.pop(lowerCamelCase							)
   lowerCamelCase		:     List[str]          =				val
  if "input_proj" in key:
   lowerCamelCase		:     Optional[Any]          =				state_dict.pop(lowerCamelCase							)
   lowerCamelCase		:     int          =				val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   lowerCamelCase		:     Any          =				state_dict.pop(lowerCamelCase							)
   lowerCamelCase		:     str          =				val

    # finally, create HuggingFace model and load state dict
 lowerCamelCase		:     List[str]          =				DetaForObjectDetection(lowerCamelCase							)
 model.load_state_dict(lowerCamelCase							)
 model.eval()

 lowerCamelCase		:     List[Any]          =				"""cuda""" if torch.cuda.is_available() else """cpu"""
 model.to(lowerCamelCase							)

 # load image processor
 lowerCamelCase		:     Dict          =				DetaImageProcessor(format="""coco_detection"""							)

 # verify our conversion on image
 lowerCamelCase		:     List[Any]          =				prepare_img()
 lowerCamelCase		:     List[Any]          =				processor(images=lowerCamelCase,     return_tensors="""pt"""							)
 lowerCamelCase		:     List[Any]          =				encoding["""pixel_values"""]
 lowerCamelCase		:     List[str]          =				model(pixel_values.to(lowerCamelCase							)							)

 # verify logits
 if model_name == "deta-resnet-50":
  lowerCamelCase		:     Union[str, Any]          =				torch.tensor(
      [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]							)
  lowerCamelCase		:     List[str]          =				torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]							)
 elif model_name == "deta-resnet-50-24-epochs":
  lowerCamelCase		:     Any          =				torch.tensor(
      [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]							)
  lowerCamelCase		:     int          =				torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]							)

 assert torch.allclose(outputs.logits[0, :3, :3],     expected_logits.to(lowerCamelCase							),     atol=1e-4							)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3],     expected_boxes.to(lowerCamelCase							),     atol=1e-4							)
 print("""Everything ok!"""							)

 if pytorch_dump_folder_path:
  # Save model and processor
  logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''							)
  Path(lowerCamelCase							).mkdir(exist_ok=lowerCamelCase							)
  model.save_pretrained(lowerCamelCase							)
  processor.save_pretrained(lowerCamelCase							)

 # Push to hub
 if push_to_hub:
  print("""Pushing model and processor to hub..."""							)
  model.push_to_hub(F'''jozhang97/{model_name}'''							)
  processor.push_to_hub(F'''jozhang97/{model_name}'''							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--model_name""",
          type=str,
          default="""deta-resnet-50""",
          choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
          help="""Name of the model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          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."""
      )
      _lowerCamelCase        =parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




