
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   lowercase_											=	"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def     a__			(    snake_case   ,	snake_case   ,	snake_case=None   ,	snake_case=None   ,	snake_case=None   ,	snake_case=None   ,	snake_case=None   ,	snake_case=None   ,	):





  """simple docstring"""
  if attention_mask is None:
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.where(input_ids != config.pad_token_id   ,	1   ,	0    )
  if decoder_attention_mask is None:
    __SCREAMING_SNAKE_CASE     :					str			=						np.where(decoder_input_ids != config.pad_token_id   ,	1   ,	0    )
  if head_mask is None:
    __SCREAMING_SNAKE_CASE     :					Any			=						np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    __SCREAMING_SNAKE_CASE     :					Any			=						np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    __SCREAMING_SNAKE_CASE     :					int			=						np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }





class   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Optional[Any]      ,		_A    :    Dict      ,		_A    :    Any=13      ,		_A    :    Union[str, Any]=7      ,		_A    :    Optional[Any]=True      ,		_A    :    Optional[int]=False      ,		_A    :    int=99      ,		_A    :    Any=16      ,		_A    :    Dict=2      ,		_A    :    Dict=4      ,		_A    :    Union[str, Any]=4      ,		_A    :    Any="gelu"      ,		_A    :    List[str]=0.1      ,		_A    :    Dict=0.1      ,		_A    :    Any=32      ,		_A    :    List[str]=2      ,		_A    :    Any=1      ,		_A    :    Optional[int]=0      ,		_A    :    Any=0.02      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						parent
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						batch_size
       __SCREAMING_SNAKE_CASE     :					Any			=						seq_length
       __SCREAMING_SNAKE_CASE     :					Dict			=						is_training
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						use_labels
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						vocab_size
       __SCREAMING_SNAKE_CASE     :					str			=						hidden_size
       __SCREAMING_SNAKE_CASE     :					int			=						num_hidden_layers
       __SCREAMING_SNAKE_CASE     :					List[str]			=						num_attention_heads
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						intermediate_size
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						hidden_act
       __SCREAMING_SNAKE_CASE     :					str			=						hidden_dropout_prob
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE     :					Tuple			=						max_position_embeddings
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						eos_token_id
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pad_token_id
       __SCREAMING_SNAKE_CASE     :					str			=						bos_token_id
       __SCREAMING_SNAKE_CASE     :					Any			=						initializer_range



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE     :					List[str]			=						shift_tokens_right(_A      ,		1      ,		2				)

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						MarianConfig(
           vocab_size=self.vocab_size      ,		d_model=self.hidden_size      ,		encoder_layers=self.num_hidden_layers      ,		decoder_layers=self.num_hidden_layers      ,		encoder_attention_heads=self.num_attention_heads      ,		decoder_attention_heads=self.num_attention_heads      ,		encoder_ffn_dim=self.intermediate_size      ,		decoder_ffn_dim=self.intermediate_size      ,		dropout=self.hidden_dropout_prob      ,		attention_dropout=self.attention_probs_dropout_prob      ,		max_position_embeddings=self.max_position_embeddings      ,		eos_token_id=self.eos_token_id      ,		bos_token_id=self.bos_token_id      ,		pad_token_id=self.pad_token_id      ,		initializer_range=self.initializer_range      ,		use_cache=_A      ,		)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						prepare_marian_inputs_dict(_A      ,		_A      ,		_A				)
       return config, inputs_dict



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.prepare_config_and_inputs()
       return config, inputs_dict



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    Any      ,		_A    :    Tuple      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						20
       __SCREAMING_SNAKE_CASE     :					Any			=						model_class_name(_A				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						model.encode(inputs_dict['''input_ids''']				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model.init_cache(decoder_input_ids.shape[0]      ,		_A      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,		dtype='''i4'''				)

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

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,		dtype='''i4'''				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						model.decode(
           decoder_input_ids[:, -1:]      ,		_A      ,		decoder_attention_mask=_A      ,		past_key_values=outputs_cache.past_key_values      ,		decoder_position_ids=_A      ,		)

       __SCREAMING_SNAKE_CASE     :					Dict			=						model.decode(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						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    :    int      ,		_A    :    int      ,		_A    :    Optional[int]      ,		_A    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						20
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model_class_name(_A				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model.encode(inputs_dict['''input_ids''']				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[Any]			=						(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

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

       __SCREAMING_SNAKE_CASE     :					int			=						model.init_cache(decoder_input_ids.shape[0]      ,		_A      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]      ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,		)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						model.decode(
           decoder_input_ids[:, :-1]      ,		_A      ,		decoder_attention_mask=_A      ,		past_key_values=_A      ,		decoder_position_ids=_A      ,		)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,		dtype='''i4'''				)
       __SCREAMING_SNAKE_CASE     :					str			=						model.decode(
           decoder_input_ids[:, -1:]      ,		_A      ,		past_key_values=outputs_cache.past_key_values      ,		decoder_attention_mask=_A      ,		decoder_position_ids=_A      ,		)

       __SCREAMING_SNAKE_CASE     :					Any			=						model.decode(_A      ,		_A      ,		decoder_attention_mask=_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1e-3      ,		msg=F'''Max diff is {diff}'''				)






@require_flax
class   __UpperCamelCase				(    lowerCAmelCase__				,		unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	True
     lowerCAmelCase_     =	(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     lowerCAmelCase_     =	(FlaxMarianMTModel,) if is_flax_available() else ()



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						FlaxMarianModelTester(self				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					int			=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(_A      ,		_A      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(_A      ,		_A      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           __SCREAMING_SNAKE_CASE     :					Dict			=						self._prepare_for_class(_A      ,		_A				)
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model_class(_A				)

           @jax.jit
           def encode_jitted(_A    :    Optional[Any]      ,		_A    :    Tuple=None      ,		**_A    :    Optional[int]				):
             return model.encode(input_ids=_A      ,		attention_mask=_A				)

           with self.subTest('''JIT Enabled'''				):
             __SCREAMING_SNAKE_CASE     :					Tuple			=						encode_jitted(**_A				).to_tuple()

           with self.subTest('''JIT Disabled'''				):
             with jax.disable_jit():
               __SCREAMING_SNAKE_CASE     :					List[Any]			=						encode_jitted(**_A				).to_tuple()

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



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()

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

           __SCREAMING_SNAKE_CASE     :					List[Any]			=						{
               '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
               '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
               '''encoder_outputs''': encoder_outputs,
           }

           @jax.jit
           def decode_jitted(_A    :    Union[str, Any]      ,		_A    :    Optional[Any]      ,		_A    :    str				):
             return model.decode(
                 decoder_input_ids=_A      ,		decoder_attention_mask=_A      ,		encoder_outputs=_A      ,		)

           with self.subTest('''JIT Enabled'''				):
             __SCREAMING_SNAKE_CASE     :					int			=						decode_jitted(**_A				).to_tuple()

           with self.subTest('''JIT Disabled'''				):
             with jax.disable_jit():
               __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						decode_jitted(**_A				).to_tuple()

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




     @slow
     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE     :					Dict			=						model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''				)
         # FlaxMarianForSequenceClassification expects eos token in input_ids
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						np.ones((1, 1)				) * model.config.eos_token_id
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(_A				)
         self.assertIsNotNone(_A				)






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	None
     lowerCAmelCase_     =	None



     @classmethod
     def 				UpperCAmelCase__    (							cls    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
       return cls



     @cached_property
     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       return MarianTokenizer.from_pretrained(self.model_name				)



     @property
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       return self.tokenizer.eos_token_id



     @cached_property
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					FlaxMarianMTModel			=						FlaxMarianMTModel.from_pretrained(self.model_name				)
       self.assertEqual(model.config.decoder_start_token_id      ,		model.config.pad_token_id				)
       return model



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		**_A    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.translate_src_text(**_A				)
       self.assertListEqual(self.expected_text      ,		_A				)




     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		**_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.tokenizer(self.src_text      ,		padding=_A      ,		return_tensors='''np'''      ,		**_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.model.generate(
           model_inputs.input_ids      ,		attention_mask=model_inputs.attention_mask      ,		num_beams=2      ,		max_length=128      ,		).sequences
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.tokenizer.batch_decode(_A      ,		skip_special_tokens=_A				)
       return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''en'''
     lowerCAmelCase_     =	'''fr'''
     lowerCAmelCase_     =	[
         '''I am a small frog.''',
         '''Now I can forget the 100 words of german that I know.''',
     ]
     lowerCAmelCase_     =	[
         '''Je suis une petite grenouille.''',
         '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
     ]



     @slow
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''fr'''
     lowerCAmelCase_     =	'''en'''
     lowerCAmelCase_     =	[
         '''Donnez moi le micro.''',
         '''Tom et Mary étaient assis à une table.''',  # Accents
     ]
     lowerCAmelCase_     =	[
         '''Give me the microphone.''',
         '''Tom and Mary were sitting at a table.''',
     ]



     @slow
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''mt'''
     lowerCAmelCase_     =	'''en'''
     lowerCAmelCase_     =	['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
     lowerCAmelCase_     =	['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']



     @slow
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''en'''
     lowerCAmelCase_     =	'''de'''
     lowerCAmelCase_     =	[
         '''I am a small frog.''',
         '''Now I can forget the 100 words of german that I know.''',
         '''Tom asked his teacher for advice.''',
         '''That\'s how I would do it.''',
         '''Tom really admired Mary\'s courage.''',
         '''Turn around and close your eyes.''',
     ]
     lowerCAmelCase_     =	[
         '''Ich bin ein kleiner Frosch.''',
         '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
         '''Tom bat seinen Lehrer um Rat.''',
         '''So würde ich das machen.''',
         '''Tom bewunderte Marias Mut wirklich.''',
         '''Drehen Sie sich um und schließen Sie die Augen.''',
     ]



     @slow
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''en'''
     lowerCAmelCase_     =	'''zh'''
     lowerCAmelCase_     =	['''My name is Wolfgang and I live in Berlin''']
     lowerCAmelCase_     =	['''我叫沃尔夫冈 我住在柏林''']



     @slow
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''ru'''
     lowerCAmelCase_     =	'''fr'''
     lowerCAmelCase_     =	['''Он показал мне рукопись своей новой пьесы.''']
     lowerCAmelCase_     =	['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']



     @slow
     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







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



     @slow
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       self._assert_generated_batch_equal_expected()


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_											=	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						load_tool('''summarization'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					Any			=						load_tool('''summarization'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.remote_tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.remote_tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)
