
'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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 BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)






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




			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=0.02					,   ):
				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_act
				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
				a_      				=						initializer_range




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

				a_      				=						shift_tokens_right(UpperCAmelCase					,   1					,   2	)

				a_      				=						BlenderbotConfig(
				    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=UpperCAmelCase					,   )
				a_      				=						prepare_blenderbot_inputs_dict(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
				return config, inputs_dict




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.prepare_config_and_inputs()
				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}'''	)







@require_flax
class   a_ (      unittest.TestCase					):
			lowerCamelCase__	:			Any       =    99




			def 					lowerCAmelCase__					(       self	):
				a_      				=						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					,   )

				a_      				=						input_ids.shape[0]
				a_      				=						BlenderbotConfig(
				    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 					lowerCAmelCase__					(       self	):
				a_				,  a_				,  a_      				=						self._get_config_and_data()
				a_      				=						FlaxBlenderbotForConditionalGeneration(UpperCAmelCase	)
				a_      				=						lm_model(input_ids=UpperCAmelCase	)
				a_      				=						(batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["""logits"""].shape					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						BlenderbotConfig(
				    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					,   )
				a_      				=						FlaxBlenderbotForConditionalGeneration(UpperCAmelCase	)
				a_      				=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,   dtype=np.intaa	)
				a_      				=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,   dtype=np.intaa	)
				a_      				=						lm_model(input_ids=UpperCAmelCase					,   decoder_input_ids=UpperCAmelCase	)
				a_      				=						(*summary.shape, config.vocab_size)
				self.assertEqual(outputs["""logits"""].shape					,   UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,   dtype=np.intaa	)
				a_      				=						shift_tokens_right(UpperCAmelCase					,   1					,   2	)
				a_      				=						np.equal(UpperCAmelCase					,   1	).astype(np.floataa	).sum()
				a_      				=						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   a_ (      UpperCamelCase__		,  unittest.TestCase		,  UpperCamelCase__					):
			lowerCamelCase__	:			Optional[int]       =    True
			lowerCamelCase__	:			Optional[Any]       =    (
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__	:			Optional[int]       =    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						FlaxBlenderbotModelTester(self	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						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 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						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 					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("""facebook/blenderbot-400M-distill"""	)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					a_      				=						np.ones((1, 1)	) * model.config.eos_token_id
					a_      				=						model(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)






			@unittest.skipUnless(jax_device != """cpu"""					,   """3B test too slow on CPU."""	)
			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
				a_      				=						{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

				a_      				=						FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""					,   from_pt=UpperCAmelCase	)
				a_      				=						BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""	)

				a_      				=						["""Sam"""]
				a_      				=						tokenizer(UpperCAmelCase					,   return_tensors="""jax"""	)

				a_      				=						model.generate(**UpperCAmelCase					,   **UpperCAmelCase	)
				a_      				=						"""Sam is a great name. It means \"sun\" in Gaelic."""

				a_      				=						tokenizer.batch_decode(UpperCAmelCase					,   **UpperCAmelCase	)
				assert generated_txt[0].strip() == tgt_text


'''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}
