
'''simple docstring'''
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class   a_ :
			lowerCamelCase__	:			Tuple       =    PegasusConfig
			lowerCamelCase__	:			Optional[Any]       =    {}
			lowerCamelCase__	:			Optional[int]       =    'gelu'




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase=13					,   UpperCAmelCase=7					,   UpperCAmelCase=True					,   UpperCAmelCase=False					,   UpperCAmelCase=99					,   UpperCAmelCase=32					,   UpperCAmelCase=5					,   UpperCAmelCase=4					,   UpperCAmelCase=37					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.1					,   UpperCAmelCase=20					,   UpperCAmelCase=2					,   UpperCAmelCase=1					,   UpperCAmelCase=0					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size

				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						max_position_embeddings
				a_      				=						eos_token_id
				a_      				=						pad_token_id
				a_      				=						bos_token_id




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size	).clip(3					,   self.vocab_size	)
				a_      				=						np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)					,   1	)
				a_      				=						np.concatenate([input_ids, eos_tensor]					,   axis=1	)

				a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

				a_      				=						self.config_cls(
				    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_ids=[2]					,   bos_token_id=self.bos_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.pad_token_id					,   **self.config_updates					,   )
				a_      				=						prepare_pegasus_inputs_dict(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
				return config, inputs_dict




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						20
				a_      				=						model_class_name(UpperCAmelCase	)

				a_      				=						model.encode(inputs_dict["""input_ids"""]	)

				a_				,  a_      				=						(
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

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

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

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

				a_      				=						model.decode(UpperCAmelCase					,   UpperCAmelCase	)

				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						20
				a_      				=						model_class_name(UpperCAmelCase	)

				a_      				=						model.encode(inputs_dict["""input_ids"""]	)

				a_				,  a_      				=						(
				    inputs_dict["""decoder_input_ids"""],
				    inputs_dict["""decoder_attention_mask"""],
				)

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

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

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

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

				a_      				=						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_				(      A__  ,      A__  ,      A__  ,      A__=None  ,      A__=None  ,      ):
	if attention_mask is None:
		a_      				=						np.not_equal(A__  ,      config.pad_token_id						).astype(np.inta						)
	if decoder_attention_mask is None:
		a_      				=						np.concatenate(
		    [
		        np.ones(decoder_input_ids[:, :1].shape  ,      dtype=np.inta						),
		        np.not_equal(decoder_input_ids[:, 1:]  ,      config.pad_token_id						).astype(np.inta						),
		    ]  ,      axis=-1  ,      )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	}







@require_flax
class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Union[str, Any]       =    (
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__	:			List[Any]       =    (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			lowerCamelCase__	:			Dict       =    True
			lowerCamelCase__	:			Optional[int]       =    False
			lowerCamelCase__	:			List[str]       =    False
			lowerCamelCase__	:			Union[str, Any]       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxPegasusModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				self.config_tester.run_common_tests()




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
						a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
						a_      				=						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"""	):
							a_      				=						encode_jitted(**UpperCAmelCase	).to_tuple()

						with self.subTest("""JIT Disabled"""	):
							with jax.disable_jit():
								a_      				=						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 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

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

						a_      				=						{
						    """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"""	):
							a_      				=						decode_jitted(**UpperCAmelCase	).to_tuple()

						with self.subTest("""JIT Disabled"""	):
							with jax.disable_jit():
								a_      				=						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 					lowerCAmelCase__					(       self	):
				for model_class_name in self.all_model_classes:
					a_      				=						model_class_name.from_pretrained("""google/pegasus-large"""					,   from_pt=UpperCAmelCase	)
					a_      				=						np.ones((1, 1)	)
					a_      				=						model(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""	)
				a_      				=						PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""	)

				a_      				=						[
				    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
				    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
				]

				a_      				=						[
				    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
				    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
				]

				a_      				=						tokenizer(UpperCAmelCase					,   return_tensors="""np"""					,   truncation=UpperCAmelCase					,   max_length=5_12					,   padding=UpperCAmelCase	)
				a_      				=						model.generate(**UpperCAmelCase					,   num_beams=2	).sequences
				a_      				=						tokenizer.batch_decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase	)
				assert tgt_text == decoded


'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


lowercase__											=datasets.logging.get_logger(__name__)

lowercase__											='\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

lowercase__											='\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

lowercase__											='\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,  _KWARGS_DESCRIPTION					)
class   a_ (      datasets.Metric					):




			def 					lowerCAmelCase__					(       self	):
				return datasets.MetricInfo(
				    description=_DESCRIPTION					,   citation=_CITATION					,   homepage="""https://unbabel.github.io/COMET/html/index.html"""					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
				        {
				            """sources""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """predictions""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """references""": datasets.Value("""string"""					,   id="""sequence"""	),
				        }	)					,   codebase_urls=["""https://github.com/Unbabel/COMET"""]					,   reference_urls=[
				        """https://github.com/Unbabel/COMET""",
				        """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
				        """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
				    ]					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if self.config_name == "default":
					a_      				=						comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""	)	)
				else:
					a_      				=						comet.load_from_checkpoint(comet.download_model(self.config_name	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase=False	):
				if gpus is None:
					a_      				=						1 if torch.cuda.is_available() else 0
				a_      				=						{"""src""": sources, """mt""": predictions, """ref""": references}
				a_      				=						[dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	) for t in zip(*data.values()	)]
				a_				,  a_      				=						self.scorer.predict(UpperCAmelCase					,   gpus=UpperCAmelCase					,   progress_bar=UpperCAmelCase	)
				return {"mean_score": mean_score, "scores": scores}
