import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def 	__lowerCamelCase							(				)      ->       List[str]:
	def filter_log(__a    : str		):
		return False if "This IS expected if you are initializing" in record.msg else True

	_lowercase   =datasets.utils.logging.get_logger("transformers.modeling_utils"		)
	logger.addFilter(__a		)
	try:
		yield
	finally:
		logger.removeFilter(__a		)


lowerCAmelCase__  	=      "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

lowerCAmelCase__  	=      "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

lowerCAmelCase__  	=      "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,		_KWARGS_DESCRIPTION					)
class 	_a  (							datasets.Metric					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		return datasets.MetricInfo(
		    description=_DESCRIPTION       ,  citation=_CITATION       ,  homepage="https://github.com/Tiiiger/bert_score"       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
		        {
		            "predictions": datasets.Value("string"       ,  id="sequence"       ),
		            "references": datasets.Sequence(datasets.Value("string"       ,  id="sequence"       )       ,  id="references"       ),
		        }       )       ,  codebase_urls=["https://github.com/Tiiiger/bert_score"]       ,  reference_urls=[
		        "https://github.com/Tiiiger/bert_score",
		        "https://arxiv.org/abs/1904.09675",
		    ]       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  ):
		_lowercase   =bert_score.utils.get_hash
		_lowercase   =bert_score.BERTScorer

		if version.parse(bert_score.__version__       ) >= version.parse("0.3.10"       ):
			_lowercase   =functools.partial(lowerCAmelCase_       ,  use_fast_tokenizer=lowerCAmelCase_       )
			_lowercase   =functools.partial(lowerCAmelCase_       ,  use_fast_tokenizer=lowerCAmelCase_       )
		elif use_fast_tokenizer:
			raise ImportWarning(
			    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
			    "You can install it with `pip install \"bert-score>=0.3.10\"`."       )

		if model_type is None:
			assert lang is not None, "either lang or model_type should be specified"
			_lowercase   =bert_score.utils.langamodel[lang.lower()]

		if num_layers is None:
			_lowercase   =bert_score.utils.modelalayers[model_type]

		_lowercase   =get_hash(
		    model=lowerCAmelCase_       ,  num_layers=lowerCAmelCase_       ,  idf=lowerCAmelCase_       ,  rescale_with_baseline=lowerCAmelCase_       ,  use_custom_baseline=baseline_path is not None       ,  )

		with filter_logging_context():
			if not hasattr(self       ,  "cached_bertscorer"       ) or self.cached_bertscorer.hash != hashcode:
				_lowercase   =scorer(
				    model_type=lowerCAmelCase_       ,  num_layers=lowerCAmelCase_       ,  batch_size=lowerCAmelCase_       ,  nthreads=lowerCAmelCase_       ,  all_layers=lowerCAmelCase_       ,  idf=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  lang=lowerCAmelCase_       ,  rescale_with_baseline=lowerCAmelCase_       ,  baseline_path=lowerCAmelCase_       ,  )

		((_lowercase)   ,  (_lowercase)   ,  (_lowercase))   =self.cached_bertscorer.score(
		    cands=lowerCAmelCase_       ,  refs=lowerCAmelCase_       ,  verbose=lowerCAmelCase_       ,  batch_size=lowerCAmelCase_       ,  )
		_lowercase   ={
		    "precision": P.tolist(),
		    "recall": R.tolist(),
		    "f1": F.tolist(),
		    "hashcode": hashcode,
		}
		return output_dict


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		# References can be strings or lists of strings
		# Let's change strings to lists of strings with one element
		if references is not None:
			_lowercase   =[[ref] if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ) else ref for ref in references]
		super().add_batch(predictions=lowerCAmelCase_       ,  references=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		# References can be strings or lists of strings
		# Let's change strings to lists of strings with one element
		if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =[reference]
		super().add(prediction=lowerCAmelCase_       ,  reference=lowerCAmelCase_       ,  **lowerCAmelCase_       )











import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
				import torch
class 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_4      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=4      ,  __lowercase=4      ,  __lowercase=[[["global", "local"], 2]]      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=7      ,  __lowercase=0.0_2      ,  )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=parent
				lowerCamelCase :			Optional[int]    		=batch_size
				lowerCamelCase :			Any    		=seq_length
				lowerCamelCase :			Optional[Any]    		=is_training
				lowerCamelCase :			Any    		=use_input_mask
				lowerCamelCase :			int    		=use_token_type_ids
				lowerCamelCase :			int    		=use_labels
				lowerCamelCase :			str    		=vocab_size
				lowerCamelCase :			Optional[int]    		=hidden_size
				lowerCamelCase :			int    		=num_hidden_layers
				lowerCamelCase :			List[Any]    		=num_attention_heads
				lowerCamelCase :			Any    		=attention_types
				lowerCamelCase :			List[str]    		=intermediate_size
				lowerCamelCase :			Dict    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			Optional[int]    		=attention_probs_dropout_prob
				lowerCamelCase :			Any    		=max_position_embeddings
				lowerCamelCase :			int    		=window_size
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Any    		=None
				lowerCamelCase :			List[Any]    		=vocab_size - 1
				lowerCamelCase :			str    		=vocab_size - 1
				lowerCamelCase :			int    		=vocab_size - 1







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

				lowerCamelCase :			Tuple    		=None
				if self.use_input_mask:
						lowerCamelCase :			Union[str, Any]    		=random_attention_mask([self.batch_size, self.seq_length] )

				lowerCamelCase :			List[Any]    		=GPTNeoConfig(
				    vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  num_layers=self.num_hidden_layers      ,  num_heads=self.num_attention_heads      ,  max_position_embeddings=self.max_position_embeddings      ,  use_cache=__lowercase      ,  bos_token_id=self.bos_token_id      ,  eos_token_id=self.eos_token_id      ,  pad_token_id=self.pad_token_id      ,  window_size=self.window_size      ,  attention_types=self.attention_types      ,  )

				return (config, input_ids, input_mask)







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=config_and_inputs
				lowerCamelCase :			List[str]    		={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			Tuple    		=2_0
				lowerCamelCase :			Union[str, Any]    		=model_class_name(__lowercase )

				lowerCamelCase :			List[Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			str    		=jnp.ones((input_ids.shape[0], max_decoder_length)      ,  dtype='''i4''' )

				lowerCamelCase :			Optional[int]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
				lowerCamelCase :			str    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			List[str]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			List[Any]    		=model(
				    input_ids[:, -1:]      ,  attention_mask=__lowercase      ,  past_key_values=outputs_cache.past_key_values      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Any    		=model(__lowercase )

				lowerCamelCase :			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       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Dict:
				lowerCamelCase :			str    		=2_0
				lowerCamelCase :			Optional[int]    		=model_class_name(__lowercase )

				lowerCamelCase :			int    		=jnp.concatenate(
				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]      ,  axis=-1      ,  )

				lowerCamelCase :			Union[str, Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			List[Any]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

				lowerCamelCase :			Optional[Any]    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )
				lowerCamelCase :			List[Any]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			Union[str, Any]    		=model(
				    input_ids[:, -1:]      ,  past_key_values=outputs_cache.past_key_values      ,  attention_mask=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Union[str, Any]    		=model(__lowercase      ,  attention_mask=__lowercase )

				lowerCamelCase :			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}" )

@require_flax
class 							snake_case_		(      _A    ,       _A    ,       unittest.TestCase):
		lowerCamelCase     :List[Any]							=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		lowerCamelCase     :Union[str, Any]							=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			str    		=FlaxGPTNeoModelTester(self )







		def       __lowercase (		self )					-> Dict:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Tuple:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[int]    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		@slow
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		=GPTaTokenizer.from_pretrained('''gpt2'''      ,  pad_token='''<|endoftext|>'''      ,  padding_side='''left''' )
				lowerCamelCase :			Any    		=tokenizer(['''Hello this is a long string''', '''Hey''']      ,  return_tensors='''np'''      ,  padding=__lowercase      ,  truncation=__lowercase )

				lowerCamelCase :			List[Any]    		=FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
				lowerCamelCase :			Union[str, Any]    		=False
				lowerCamelCase :			Optional[int]    		=model.config.eos_token_id

				lowerCamelCase :			str    		=jax.jit(model.generate )

				lowerCamelCase :			Optional[int]    		=jit_generate(
				    inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  pad_token_id=tokenizer.pad_token_id ).sequences

				lowerCamelCase :			Any    		=tokenizer.batch_decode(__lowercase      ,  skip_special_tokens=__lowercase )

				lowerCamelCase :			str    		=[
				    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
				    '''Hey, I\'m a little late to the party. I\'m going to''',
				]

				self.assertListEqual(__lowercase      ,  __lowercase )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> int:
				lowerCamelCase		, lowerCamelCase :			int    		=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__ ):
								# prepare inputs
								lowerCamelCase :			Optional[Any]    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Optional[Any]    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase :			Any    		=model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase :			Optional[int]    		=getattr(__lowercase      ,  __lowercase )

								lowerCamelCase		, lowerCamelCase :			Union[str, Any]    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			Dict    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Optional[Any]    		=1
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Union[str, Any]    		=1
								lowerCamelCase :			Optional[int]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Any    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Union[str, Any]    		=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  __lowercase )
								lowerCamelCase :			Optional[int]    		=fx_state

								with torch.no_grad():
										lowerCamelCase :			List[Any]    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			List[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(__lowercase )
										lowerCamelCase :			List[str]    		=model_class.from_pretrained(__lowercase      ,  from_pt=__lowercase )

								lowerCamelCase :			List[str]    		=fx_model_loaded(**__lowercase ).to_tuple()
								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output_loaded, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output_loaded[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> str:
				lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__ ):
								# prepare inputs
								lowerCamelCase :			Tuple    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Any    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase :			Optional[Any]    		=model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase :			List[str]    		=getattr(__lowercase      ,  __lowercase )

								lowerCamelCase :			List[str]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Union[str, Any]    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Dict    		=load_flax_weights_in_pytorch_model(__lowercase      ,  fx_model.params )
								lowerCamelCase		, lowerCamelCase :			Any    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			str    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			List[str]    		=0
										lowerCamelCase :			Union[str, Any]    		=1
										lowerCamelCase :			Optional[int]    		=0
										lowerCamelCase :			Dict    		=1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
										lowerCamelCase :			Tuple    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			Optional[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__lowercase )
										lowerCamelCase :			Union[str, Any]    		=pt_model_class.from_pretrained(__lowercase      ,  from_flax=__lowercase )

								with torch.no_grad():
										lowerCamelCase :			str    		=pt_model_loaded(**__lowercase ).to_tuple()

								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@slow
		def       __lowercase (		self )					-> Any:
				for model_class_name in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
						lowerCamelCase :			Union[str, Any]    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )

