






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
		_a				     =  '''platform'''

		import jax
		import jax.numpy as jnp

		from transformers import AutoTokenizer
		from transformers.models.mbart.modeling_flax_mbart import (
		    FlaxMBartForConditionalGeneration,
		    FlaxMBartForQuestionAnswering,
		    FlaxMBartForSequenceClassification,
		    FlaxMBartModel,
		    shift_tokens_right,
		)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		)->  Optional[Any]:





						"""simple docstring"""



						if attention_mask is None:
												_UpperCAmelCase     =      np.where(input_ids != config.pad_token_id			,		1			,		0     )
						if decoder_attention_mask is None:
												_UpperCAmelCase     =      np.where(decoder_input_ids != config.pad_token_id			,		1			,		0     )
						if head_mask is None:
												_UpperCAmelCase     =      np.ones((config.encoder_layers, config.encoder_attention_heads)     )
						if decoder_head_mask is None:
												_UpperCAmelCase     =      np.ones((config.decoder_layers, config.decoder_attention_heads)     )
						if cross_attn_head_mask is None:
												_UpperCAmelCase     =      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": decoder_attention_mask,
						}





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=13	,   UpperCAmelCase=7	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase=99	,   UpperCAmelCase=16	,   UpperCAmelCase=2	,   UpperCAmelCase=4	,   UpperCAmelCase=4	,   UpperCAmelCase="gelu"	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=32	,   UpperCAmelCase=2	,   UpperCAmelCase=1	,   UpperCAmelCase=0	,   UpperCAmelCase=2	,   UpperCAmelCase=0.02	,   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

							_UpperCAmelCase     =      shift_tokens_right(UpperCAmelCase	,   1   )

							_UpperCAmelCase     =      MBartConfig(
							    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	,   decoder_start_token_id=self.decoder_start_token_id	,   initializer_range=self.initializer_range	,   use_cache=UpperCAmelCase	,   )
							_UpperCAmelCase     =      prepare_mbart_inputs_dict(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )
							return config, inputs_dict

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      20
							_UpperCAmelCase     =      model_class_name(UpperCAmelCase   )

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

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

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

							_UpperCAmelCase     =      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]	,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,   )
							_UpperCAmelCase     =      model.decode(
							    decoder_input_ids[:, :-1]	,   UpperCAmelCase	,   decoder_attention_mask=UpperCAmelCase	,   past_key_values=UpperCAmelCase	,   decoder_position_ids=UpperCAmelCase	,   )

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

							_UpperCAmelCase     =      model.decode(UpperCAmelCase	,   UpperCAmelCase   )

							_UpperCAmelCase     =      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	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      20
							_UpperCAmelCase     =      model_class_name(UpperCAmelCase   )

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

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

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

							_UpperCAmelCase     =      model.init_cache(decoder_input_ids.shape[0]	,   UpperCAmelCase	,   UpperCAmelCase   )
							_UpperCAmelCase     =      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]	,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,   )

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

							_UpperCAmelCase     =      model.decode(UpperCAmelCase	,   UpperCAmelCase	,   decoder_attention_mask=UpperCAmelCase   )

							_UpperCAmelCase     =      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 __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				99

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]	,   dtype=np.intaa	,   )

							_UpperCAmelCase     =      input_ids.shape[0]
							_UpperCAmelCase     =      MBartConfig(
							    vocab_size=self.vocab_size	,   d_model=24	,   encoder_layers=2	,   decoder_layers=2	,   encoder_attention_heads=2	,   decoder_attention_heads=2	,   encoder_ffn_dim=32	,   decoder_ffn_dim=32	,   max_position_embeddings=48	,   eos_token_id=2	,   pad_token_id=1	,   bos_token_id=0	,   )
							return config, input_ids, batch_size

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self._get_config_and_data()
							_UpperCAmelCase     =      FlaxMBartForSequenceClassification(UpperCAmelCase   )
							_UpperCAmelCase     =      model(input_ids=UpperCAmelCase	,   decoder_input_ids=UpperCAmelCase   )
							_UpperCAmelCase     =      (batch_size, config.num_labels)
							self.assertEqual(outputs['logits'].shape	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self._get_config_and_data()
							_UpperCAmelCase     =      FlaxMBartForQuestionAnswering(UpperCAmelCase   )
							_UpperCAmelCase     =      model(input_ids=UpperCAmelCase   )

							self.assertEqual(outputs['start_logits'].shape	,   input_ids.shape   )
							self.assertEqual(outputs['end_logits'].shape	,   input_ids.shape   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self._get_config_and_data()
							_UpperCAmelCase     =      FlaxMBartForConditionalGeneration(UpperCAmelCase   )
							_UpperCAmelCase     =      lm_model(input_ids=UpperCAmelCase   )
							_UpperCAmelCase     =      (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartConfig(
							    vocab_size=self.vocab_size	,   d_model=14	,   encoder_layers=2	,   decoder_layers=2	,   encoder_attention_heads=2	,   decoder_attention_heads=2	,   encoder_ffn_dim=8	,   decoder_ffn_dim=8	,   max_position_embeddings=48	,   )
							_UpperCAmelCase     =      FlaxMBartForConditionalGeneration(UpperCAmelCase   )
							_UpperCAmelCase     =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,   dtype=np.intaa   )
							_UpperCAmelCase     =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,   dtype=np.intaa   )
							_UpperCAmelCase     =      lm_model(input_ids=UpperCAmelCase	,   decoder_input_ids=UpperCAmelCase   )
							_UpperCAmelCase     =      (*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,   dtype=np.intaa   )
							_UpperCAmelCase     =      shift_tokens_right(UpperCAmelCase	,   1   )
							_UpperCAmelCase     =      np.equal(UpperCAmelCase	,   1   ).astype(np.floataa   ).sum()
							_UpperCAmelCase     =      np.equal(UpperCAmelCase	,   1   ).astype(np.floataa   ).sum()
							self.assertEqual(shifted.shape	,   input_ids.shape   )
							self.assertEqual(UpperCAmelCase	,   n_pad_before - 1   )
							self.assertTrue(np.equal(shifted[:, 0]	,   2   ).all()   )





@require_flax
class __lowerCamelCase			(				snake_case__					,	unittest.TestCase					,	snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				True
	UpperCamelCase__								=				(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	UpperCamelCase__								=				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      FlaxMBartModelTester(self   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      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(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																			_UpperCAmelCase     =      self._prepare_for_class(UpperCAmelCase	,   UpperCAmelCase   )
																			_UpperCAmelCase     =      model_class(UpperCAmelCase   )

																			@jax.jit
																			def encode_jitted(UpperCAmelCase	,   UpperCAmelCase=None	,   **UpperCAmelCase   ):
																									return model.encode(input_ids=UpperCAmelCase	,   attention_mask=UpperCAmelCase   )

																			with self.subTest('JIT Enabled'   ):
																									_UpperCAmelCase     =      encode_jitted(**UpperCAmelCase   ).to_tuple()

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

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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																			_UpperCAmelCase     =      model_class(UpperCAmelCase   )
																			_UpperCAmelCase     =      model.encode(inputs_dict['input_ids']	,   inputs_dict['attention_mask']   )

																			_UpperCAmelCase     =      {
																			    '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(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
																									return model.decode(
																									    decoder_input_ids=UpperCAmelCase	,   decoder_attention_mask=UpperCAmelCase	,   encoder_outputs=UpperCAmelCase	,   )

																			with self.subTest('JIT Enabled'   ):
																									_UpperCAmelCase     =      decode_jitted(**UpperCAmelCase   ).to_tuple()

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

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

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for model_class_name in self.all_model_classes:
													_UpperCAmelCase     =      model_class_name.from_pretrained('facebook/mbart-large-cc25'	,   from_pt=UpperCAmelCase   )
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													_UpperCAmelCase     =      np.ones((1, 1)   ) * model.config.eos_token_id
													_UpperCAmelCase     =      model(UpperCAmelCase   )
													self.assertIsNotNone(UpperCAmelCase   )





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





	"""simple docstring"""






	UpperCamelCase__								=				[
	    " UN Chief Says There Is No Military Solution in Syria",
	]
	UpperCamelCase__								=				[
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	]
	UpperCamelCase__								=				"facebook/mbart-large-en-ro"

	@cached_property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return AutoTokenizer.from_pretrained(self.model_name   )

	@cached_property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      FlaxMBartForConditionalGeneration.from_pretrained(self.model_name	,   from_pt=UpperCAmelCase   )
							return model

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.translate_src_text(**UpperCAmelCase   )
							self.assertListEqual(self.expected_text	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tokenizer(self.src_text	,   **UpperCAmelCase	,   return_tensors='np'   )
							_UpperCAmelCase     =      self.model.generate(
							    model_inputs.input_ids	,   attention_mask=model_inputs.attention_mask	,   decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']	,   early_stopping=UpperCAmelCase	,   num_beams=2	,   ).sequences
							_UpperCAmelCase     =      self.tokenizer.batch_decode(UpperCAmelCase	,   skip_special_tokens=UpperCAmelCase   )
							return generated_words

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self._assert_generated_batch_equal_expected()








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_a				     =  '''
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 __lowerCamelCase			(				unittest.TestCase					,	snake_case__):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      load_tool('summarization'   )
							self.tool.setup()
							_UpperCAmelCase     =      load_tool('summarization'	,   remote=UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tool(UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   '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   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.remote_tool(UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   '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   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tool(text=UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   '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   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.remote_tool(text=UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   '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.'	,   )
