
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch





@require_flax
class 							A__		:
						def __init__(				self   ,  __magic_name__   ,  __magic_name__=1_4   ,  __magic_name__=7   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=9_9   ,  __magic_name__=3_2   ,  __magic_name__=5   ,  __magic_name__=4   ,  __magic_name__=3_7   ,  __magic_name__="gelu"   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=5_1_2   ,  __magic_name__=0.02   ,  __magic_name__=None   ,  ):
							lowerCamelCase		:     Union[str, Any]          =				parent
							lowerCamelCase		:     List[str]          =				batch_size
							lowerCamelCase		:     List[Any]          =				seq_length
							lowerCamelCase		:     Optional[int]          =				is_training
							lowerCamelCase		:     str          =				use_input_mask
							lowerCamelCase		:     Optional[Any]          =				use_labels
							lowerCamelCase		:     Any          =				vocab_size
							lowerCamelCase		:     Optional[Any]          =				d_model
							lowerCamelCase		:     List[str]          =				num_hidden_layers
							lowerCamelCase		:     Union[str, Any]          =				num_attention_heads
							lowerCamelCase		:     int          =				ffn_dim
							lowerCamelCase		:     Union[str, Any]          =				activation_function
							lowerCamelCase		:     int          =				activation_dropout
							lowerCamelCase		:     str          =				attention_dropout
							lowerCamelCase		:     Optional[Any]          =				max_position_embeddings
							lowerCamelCase		:     List[Any]          =				initializer_range
							lowerCamelCase		:     int          =				None
							lowerCamelCase		:     str          =				0
							lowerCamelCase		:     Dict          =				2
							lowerCamelCase		:     Optional[Any]          =				1
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				np.clip(ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size				)   ,  3   ,  self.vocab_size				)

							lowerCamelCase		:     List[Any]          =				None
							if self.use_input_mask:
								lowerCamelCase		:     Dict          =				random_attention_mask([self.batch_size, self.seq_length]				)

							lowerCamelCase		:     Dict          =				XGLMConfig(
							    vocab_size=self.vocab_size   ,  d_model=self.hidden_size   ,  num_layers=self.num_hidden_layers   ,  attention_heads=self.num_attention_heads   ,  ffn_dim=self.ffn_dim   ,  activation_function=self.activation_function   ,  activation_dropout=self.activation_dropout   ,  attention_dropout=self.attention_dropout   ,  max_position_embeddings=self.max_position_embeddings   ,  initializer_range=self.initializer_range   ,  use_cache=__magic_name__   ,  bos_token_id=self.bos_token_id   ,  eos_token_id=self.eos_token_id   ,  pad_token_id=self.pad_token_id   ,  )

							return (config, input_ids, input_mask)
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[Any]          =				self.prepare_config_and_inputs()
							lowerCamelCase		:     Optional[int]          =				config_and_inputs
							lowerCamelCase		:     Dict          =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
							return config, inputs_dict
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Tuple          =				self.prepare_config_and_inputs()

							lowerCamelCase		:     Optional[int]          =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
							lowerCamelCase		:     Optional[Any]          =				ids_tensor([self.batch_size, self.seq_length]   ,  vocab_size=2				)

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
							lowerCamelCase		:     List[Any]          =				2_0
							lowerCamelCase		:     Optional[int]          =				model_class_name(__magic_name__				)

							lowerCamelCase		:     Optional[Any]          =				model.init_cache(input_ids.shape[0]   ,  __magic_name__				)
							lowerCamelCase		:     Union[str, Any]          =				jnp.ones((input_ids.shape[0], max_decoder_length)   ,  dtype="""i4"""				)

							lowerCamelCase		:     Union[str, Any]          =				jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)
							lowerCamelCase		:     str          =				model(
							    input_ids[:, :-1]   ,  attention_mask=__magic_name__   ,  past_key_values=__magic_name__   ,  position_ids=__magic_name__   ,  )

							lowerCamelCase		:     Optional[Any]          =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype="""i4"""				)
							lowerCamelCase		:     int          =				model(
							    input_ids[:, -1:]   ,  attention_mask=__magic_name__   ,  past_key_values=outputs_cache.past_key_values   ,  position_ids=__magic_name__   ,  )

							lowerCamelCase		:     List[str]          =				model(__magic_name__				)

							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}'''				)






						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
							lowerCamelCase		:     str          =				2_0
							lowerCamelCase		:     Optional[int]          =				model_class_name(__magic_name__				)

							lowerCamelCase		:     Tuple          =				jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]   ,  axis=-1   ,  )

							lowerCamelCase		:     Optional[Any]          =				model.init_cache(input_ids.shape[0]   ,  __magic_name__				)
							lowerCamelCase		:     int          =				jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)

							lowerCamelCase		:     Optional[Any]          =				model(
							    input_ids[:, :-1]   ,  attention_mask=__magic_name__   ,  past_key_values=__magic_name__   ,  position_ids=__magic_name__   ,  )
							lowerCamelCase		:     List[Any]          =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype="""i4"""				)
							lowerCamelCase		:     Union[str, Any]          =				model(
							    input_ids[:, -1:]   ,  past_key_values=outputs_cache.past_key_values   ,  attention_mask=__magic_name__   ,  position_ids=__magic_name__   ,  )

							lowerCamelCase		:     str          =				model(__magic_name__   ,  attention_mask=__magic_name__				)
							lowerCamelCase		:     List[str]          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
							self.parent.assertTrue(diff < 1e-3   ,  msg=F'''Max diff is {diff}'''				)





@require_sentencepiece
@require_flax
class 							A__		(     __SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
						_UpperCAmelCase    :   Optional[Any]   	=      (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
						_UpperCAmelCase    :   int   	=      (FlaxXGLMForCausalLM,) if is_flax_available() else ()
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[Any]          =				FlaxXGLMModelTester(self				)
						def 						UpperCamelCase__							(				self				):
							for model_class_name in self.all_model_classes:
								lowerCamelCase		:     Union[str, Any]          =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
						def 						UpperCamelCase__							(				self				):
							for model_class_name in self.all_model_classes:
								lowerCamelCase		:     Union[str, Any]          =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
						@slow
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[Any]          =				XGLMTokenizer.from_pretrained("""XGLM"""   ,  padding_side="""left"""				)
							lowerCamelCase		:     Any          =				tokenizer(["""Hello this is a long string""", """Hey"""]   ,  return_tensors="""np"""   ,  padding=__magic_name__   ,  truncation=__magic_name__				)

							lowerCamelCase		:     Tuple          =				FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""				)
							lowerCamelCase		:     Union[str, Any]          =				1
							lowerCamelCase		:     Union[str, Any]          =				False

							lowerCamelCase		:     Dict          =				jax.jit(model.generate				)

							lowerCamelCase		:     str          =				jit_generate(inputs["""input_ids"""]   ,  attention_mask=inputs["""attention_mask"""]				).sequences

							lowerCamelCase		:     List[Any]          =				tokenizer.batch_decode(__magic_name__   ,  skip_special_tokens=__magic_name__				)

							lowerCamelCase		:     Optional[int]          =				[
							    """Hello this is a long string of questions, but I'm not sure if I'm""",
							    """Hey, I'm a newbie to the forum and I'""",
							]

							self.assertListEqual(__magic_name__   ,  __magic_name__				)
						@is_pt_flax_cross_test
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     int          =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__				):
									# prepare inputs
									lowerCamelCase		:     str          =				self._prepare_for_class(__magic_name__   ,  __magic_name__				)
									lowerCamelCase		:     Any          =				{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCamelCase		:     List[str]          =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCamelCase		:     Dict          =				getattr(__magic_name__   ,  __magic_name__				)

									lowerCamelCase		:     Optional[Any]          =				pt_inputs["""input_ids"""].shape
									lowerCamelCase		:     Union[str, Any]          =				np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)				)
									for batch_idx, start_index in enumerate(__magic_name__				):
										lowerCamelCase		:     Dict          =				0
										lowerCamelCase		:     Any          =				1
										lowerCamelCase		:     int          =				0
										lowerCamelCase		:     Dict          =				1
									lowerCamelCase		:     str          =				pt_model_class(__magic_name__				).eval()
									# Flax models don't use the `use_cache` option and cache is not returned as a default.
									# So we disable `use_cache` here for PyTorch model.
									lowerCamelCase		:     int          =				False
									lowerCamelCase		:     Optional[Any]          =				model_class(__magic_name__   ,  dtype=jnp.floataa				)
									lowerCamelCase		:     Tuple          =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,  __magic_name__				)
									lowerCamelCase		:     List[str]          =				fx_state

									with torch.no_grad():
										lowerCamelCase		:     Any          =				pt_model(**__magic_name__				).to_tuple()

									lowerCamelCase		:     Dict          =				fx_model(**__magic_name__				).to_tuple()
									self.assertEqual(len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
									for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
										self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)

									with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(__magic_name__				)
										lowerCamelCase		:     Tuple          =				model_class.from_pretrained(__magic_name__   ,  from_pt=__magic_name__				)

									lowerCamelCase		:     str          =				fx_model_loaded(**__magic_name__				).to_tuple()
									self.assertEqual(
									    len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
									for fx_output_loaded, pt_output in zip(__magic_name__   ,  __magic_name__				):
										self.assert_almost_equals(fx_output_loaded[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)
						@is_pt_flax_cross_test
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[str]          =				self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__				):
									# prepare inputs
									lowerCamelCase		:     List[str]          =				self._prepare_for_class(__magic_name__   ,  __magic_name__				)
									lowerCamelCase		:     Optional[Any]          =				{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCamelCase		:     int          =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCamelCase		:     Tuple          =				getattr(__magic_name__   ,  __magic_name__				)

									lowerCamelCase		:     Optional[Any]          =				pt_model_class(__magic_name__				).eval()
									lowerCamelCase		:     Dict          =				False
									lowerCamelCase		:     List[Any]          =				model_class(__magic_name__   ,  dtype=jnp.floataa				)

									lowerCamelCase		:     Tuple          =				load_flax_weights_in_pytorch_model(__magic_name__   ,  fx_model.params				)
									lowerCamelCase		:     Optional[Any]          =				pt_inputs["""input_ids"""].shape
									lowerCamelCase		:     Dict          =				np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)				)
									for batch_idx, start_index in enumerate(__magic_name__				):
										lowerCamelCase		:     str          =				0
										lowerCamelCase		:     Optional[int]          =				1
										lowerCamelCase		:     List[Any]          =				0
										lowerCamelCase		:     Any          =				1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
										lowerCamelCase		:     List[Any]          =				pt_model(**__magic_name__				).to_tuple()

									lowerCamelCase		:     List[str]          =				fx_model(**__magic_name__				).to_tuple()
									self.assertEqual(len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
									for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
										self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)

									with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__magic_name__				)
										lowerCamelCase		:     Optional[Any]          =				pt_model_class.from_pretrained(__magic_name__   ,  from_flax=__magic_name__				)

									with torch.no_grad():
										lowerCamelCase		:     Tuple          =				pt_model_loaded(**__magic_name__				).to_tuple()

									self.assertEqual(
									    len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
									for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
										self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)






						@slow
						def 						UpperCamelCase__							(				self				):
							for model_class_name in self.all_model_classes:
								lowerCamelCase		:     Optional[int]          =				model_class_name.from_pretrained("""facebook/xglm-564M"""				)
								lowerCamelCase		:     Union[str, Any]          =				model(np.ones((1, 1)				)				)
								self.assertIsNotNone(__magic_name__				)






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"""				)




