




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
      SCREAMING_SNAKE_CASE:    List[str]											=   '''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       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	)->  List[Any]:
  if attention_mask is None:
    SCREAMING_SNAKE_CASE_							=       np.where(input_ids != config.pad_token_id						,	1						,	0   )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE_							=       np.where(decoder_input_ids != config.pad_token_id						,	1						,	0   )
  if head_mask is None:
    SCREAMING_SNAKE_CASE_							=       np.ones((config.encoder_layers, config.encoder_attention_heads)   )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE_							=       np.ones((config.decoder_layers, config.decoder_attention_heads)   )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE_							=       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       lowercase_		:



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

        """simple docstring"""

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



      def   __a				( self  :      str     ):

        """simple docstring"""

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

        SCREAMING_SNAKE_CASE_							=       shift_tokens_right(snake_case__				,       1				,       2     )

        SCREAMING_SNAKE_CASE_							=       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=snake_case__				,       )
        SCREAMING_SNAKE_CASE_							=       prepare_marian_inputs_dict(snake_case__				,       snake_case__				,       snake_case__     )
        return config, inputs_dict



      def   __a				( self  :      Dict     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.prepare_config_and_inputs()
        return config, inputs_dict



      def   __a				( self  :      List[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]				,       snake_case__  :      int     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       20
        SCREAMING_SNAKE_CASE_							=       model_class_name(snake_case__     )

        SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']     )

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        SCREAMING_SNAKE_CASE_							=       model.init_cache(decoder_input_ids.shape[0]				,       snake_case__				,       snake_case__     )
        SCREAMING_SNAKE_CASE_							=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,       dtype='i4'     )

        SCREAMING_SNAKE_CASE_							=       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_							=       model.decode(
            decoder_input_ids[:, :-1]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=snake_case__				,       decoder_position_ids=snake_case__				,       )

        SCREAMING_SNAKE_CASE_							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='i4'     )
        SCREAMING_SNAKE_CASE_							=       model.decode(
            decoder_input_ids[:, -1:]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=outputs_cache.past_key_values				,       decoder_position_ids=snake_case__				,       )

        SCREAMING_SNAKE_CASE_							=       model.decode(snake_case__				,       snake_case__     )

        SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str				,       snake_case__  :      List[Any]				,       snake_case__  :      Optional[int]				,       snake_case__  :      Union[str, Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       20
        SCREAMING_SNAKE_CASE_							=       model_class_name(snake_case__     )

        SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']     )

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        SCREAMING_SNAKE_CASE_							=       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_							=       model.init_cache(decoder_input_ids.shape[0]				,       snake_case__				,       snake_case__     )
        SCREAMING_SNAKE_CASE_							=       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_							=       model.decode(
            decoder_input_ids[:, :-1]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=snake_case__				,       decoder_position_ids=snake_case__				,       )
        SCREAMING_SNAKE_CASE_							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='i4'     )
        SCREAMING_SNAKE_CASE_							=       model.decode(
            decoder_input_ids[:, -1:]				,       snake_case__				,       past_key_values=outputs_cache.past_key_values				,       decoder_attention_mask=snake_case__				,       decoder_position_ids=snake_case__				,       )

        SCREAMING_SNAKE_CASE_							=       model.decode(snake_case__				,       snake_case__				,       decoder_attention_mask=snake_case__     )

        SCREAMING_SNAKE_CASE_							=       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       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):
      lowerCAmelCase__ 			=True
      lowerCAmelCase__ 			=(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      lowerCAmelCase__ 			=(FlaxMarianMTModel,) if is_flax_available() else ()



      def   __a				( self  :      List[str]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       FlaxMarianModelTester(self     )



      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(snake_case__				,       snake_case__				,       snake_case__     )



      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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(snake_case__				,       snake_case__				,       snake_case__     )



      def   __a				( self  :      Any     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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_							=       self._prepare_for_class(snake_case__				,       snake_case__     )
            SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )

            @jax.jit
            def encode_jitted(snake_case__  :      Tuple				,       snake_case__  :      Dict=None				,       **snake_case__  :      Union[str, Any]     ):
              return model.encode(input_ids=snake_case__				,       attention_mask=snake_case__     )

            with self.subTest('JIT Enabled'     ):
              SCREAMING_SNAKE_CASE_							=       encode_jitted(**snake_case__     ).to_tuple()

            with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                SCREAMING_SNAKE_CASE_							=       encode_jitted(**snake_case__     ).to_tuple()

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



      def   __a				( self  :      int     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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_							=       model_class(snake_case__     )
            SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']				,       inputs_dict['attention_mask']     )

            SCREAMING_SNAKE_CASE_							=       {
                '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(snake_case__  :      Optional[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]     ):
              return model.decode(
                  decoder_input_ids=snake_case__				,       decoder_attention_mask=snake_case__				,       encoder_outputs=snake_case__				,       )

            with self.subTest('JIT Enabled'     ):
              SCREAMING_SNAKE_CASE_							=       decode_jitted(**snake_case__     ).to_tuple()

            with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                SCREAMING_SNAKE_CASE_							=       decode_jitted(**snake_case__     ).to_tuple()

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






      @slow
      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        for model_class_name in self.all_model_classes:
          SCREAMING_SNAKE_CASE_							=       model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'     )
          # FlaxMarianForSequenceClassification expects eos token in input_ids
          SCREAMING_SNAKE_CASE_							=       np.ones((1, 1)     ) * model.config.eos_token_id
          SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
          self.assertIsNotNone(snake_case__     )



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(unittest.TestCase   ):
      lowerCAmelCase__ 			=None
      lowerCAmelCase__ 			=None



      @classmethod
      def   __a				( cls  :      Any     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
        return cls



      @cached_property
      def   __a				( self  :      List[str]     ):

        """simple docstring"""

        return MarianTokenizer.from_pretrained(self.model_name     )



      @property
      def   __a				( self  :      List[Any]     ):

        """simple docstring"""

        return self.tokenizer.eos_token_id



      @cached_property
      def   __a				( self  :      Tuple     ):

        """simple docstring"""

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



      def   __a				( self  :      List[Any]				,       **snake_case__  :      Union[str, Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.translate_src_text(**snake_case__     )
        self.assertListEqual(self.expected_text				,       snake_case__     )






      def   __a				( self  :      Optional[int]				,       **snake_case__  :      Optional[Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.tokenizer(self.src_text				,       padding=snake_case__				,       return_tensors='np'				,       **snake_case__     )
        SCREAMING_SNAKE_CASE_							=       self.model.generate(
            model_inputs.input_ids				,       attention_mask=model_inputs.attention_mask				,       num_beams=2				,       max_length=1_28				,       ).sequences
        SCREAMING_SNAKE_CASE_							=       self.tokenizer.batch_decode(snake_case__				,       skip_special_tokens=snake_case__     )
        return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      List[Any]     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      Tuple     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      str     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      Tuple     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      Union[str, Any]     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      List[str]     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
      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   __a				( self  :      List[Any]     ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE:    List[str]											=   '''
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       lowercase_		(unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'     )
								self.tool.setup()
								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'				,       remote=snake_case__     )



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       '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   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       '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   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       '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   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       '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.'				,       )