





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 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	Tuple,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Union[str, Any]=14,				lowerCamelCase_:	Any=7,				lowerCamelCase_:	List[Any]=True,				lowerCamelCase_:	Dict=True,				lowerCamelCase_:	Dict=False,				lowerCamelCase_:	List[str]=True,				lowerCamelCase_:	Any=99,				lowerCamelCase_:	str=32,				lowerCamelCase_:	Union[str, Any]=4,				lowerCamelCase_:	int=4,				lowerCamelCase_:	Dict=[[["global", "local"], 2]],				lowerCamelCase_:	Optional[Any]=37,				lowerCamelCase_:	Tuple="gelu",				lowerCamelCase_:	Optional[int]=0.1,				lowerCamelCase_:	int=0.1,				lowerCamelCase_:	Optional[int]=512,				lowerCamelCase_:	Union[str, Any]=7,				lowerCamelCase_:	int=0.0_2,				):
			lowercase__				:			Optional[Any]            =		parent
			lowercase__				:			Optional[int]            =		batch_size
			lowercase__				:			int            =		seq_length
			lowercase__				:			Any            =		is_training
			lowercase__				:			Tuple            =		use_input_mask
			lowercase__				:			str            =		use_token_type_ids
			lowercase__				:			Tuple            =		use_labels
			lowercase__				:			List[Any]            =		vocab_size
			lowercase__				:			str            =		hidden_size
			lowercase__				:			Dict            =		num_hidden_layers
			lowercase__				:			int            =		num_attention_heads
			lowercase__				:			Optional[int]            =		attention_types
			lowercase__				:			Optional[int]            =		intermediate_size
			lowercase__				:			List[str]            =		hidden_act
			lowercase__				:			Any            =		hidden_dropout_prob
			lowercase__				:			List[Any]            =		attention_probs_dropout_prob
			lowercase__				:			List[Any]            =		max_position_embeddings
			lowercase__				:			Tuple            =		window_size
			lowercase__				:			List[Any]            =		initializer_range
			lowercase__				:			int            =		None
			lowercase__				:			Any            =		vocab_size - 1
			lowercase__				:			Any            =		vocab_size - 1
			lowercase__				:			Dict            =		vocab_size - 1

	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[str]            =		ids_tensor([self.batch_size, self.seq_length],				self.vocab_size )

			lowercase__				:			Optional[Any]            =		None
			if self.use_input_mask:
					lowercase__				:			str            =		random_attention_mask([self.batch_size, self.seq_length] )

			lowercase__				:			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=lowerCamelCase_,				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       snake_case__(	self:	Optional[int] ):
			lowercase__				:			Optional[Any]            =		self.prepare_config_and_inputs()
			lowercase__      ,							lowercase__      ,							lowercase__				:			Dict            =		config_and_inputs
			lowercase__				:			Any            =		{'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict

	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	Any,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Dict,				lowerCamelCase_:	List[str] ):
			lowercase__				:			List[str]            =		20
			lowercase__				:			List[Any]            =		model_class_name(lowerCamelCase_ )

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

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

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

			lowercase__				:			Dict            =		model(lowerCamelCase_ )

			lowercase__				:			Any            =		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       snake_case__(	self:	int,				lowerCamelCase_:	List[str],				lowerCamelCase_:	Optional[Any],				lowerCamelCase_:	Optional[Any],				lowerCamelCase_:	int ):
			lowercase__				:			Tuple            =		20
			lowercase__				:			str            =		model_class_name(lowerCamelCase_ )

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

			lowercase__				:			Optional[int]            =		model.init_cache(input_ids.shape[0],				lowerCamelCase_ )
			lowercase__				:			Dict            =		jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1 )[None, :],				(input_ids.shape[0], input_ids.shape[-1] - 1) )

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

			lowercase__				:			Optional[Any]            =		model(lowerCamelCase_,				attention_mask=lowerCamelCase_ )

			lowercase__				:			Tuple            =		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				(					_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	_A       	=							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Tuple            =		FlaxGPTNeoModelTester(self )

	def       snake_case__(	self:	Union[str, Any] ):
			for model_class_name in self.all_model_classes:
					lowercase__      ,							lowercase__      ,							lowercase__				:			Any            =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )

	def       snake_case__(	self:	Union[str, Any] ):
			for model_class_name in self.all_model_classes:
					lowercase__      ,							lowercase__      ,							lowercase__				:			Dict            =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )

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

			lowercase__				:			int            =		FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M' )
			lowercase__				:			int            =		False
			lowercase__				:			Dict            =		model.config.eos_token_id

			lowercase__				:			Optional[int]            =		jax.jit(model.generate )

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

			lowercase__				:			Dict            =		tokenizer.batch_decode(lowerCamelCase_,				skip_special_tokens=lowerCamelCase_ )

			lowercase__				:			Dict            =		[
			    '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(lowerCamelCase_,				lowerCamelCase_ )

	@is_pt_flax_cross_test
	def       snake_case__(	self:	int ):
			lowercase__      ,							lowercase__				:			str            =		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
							lowercase__				:			Optional[Any]            =		self._prepare_for_class(lowerCamelCase_,				lowerCamelCase_ )
							lowercase__				:			Union[str, Any]            =		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

							lowercase__				:			List[str]            =		convert_pytorch_state_dict_to_flax(pt_model.state_dict(),				lowerCamelCase_ )
							lowercase__				:			Optional[int]            =		fx_state

							with torch.no_grad():
									lowercase__				:			Tuple            =		pt_model(**lowerCamelCase_ ).to_tuple()

							lowercase__				:			Union[str, Any]            =		fx_model(**lowerCamelCase_ ).to_tuple()
							self.assertEqual(len(lowerCamelCase_ ),				len(lowerCamelCase_ ),				'Output lengths differ between Flax and PyTorch' )
							for fx_output, pt_output in zip(lowerCamelCase_,				lowerCamelCase_ ):
									self.assert_almost_equals(fx_output[:, -1],				pt_output[:, -1].numpy(),				4E-2 )

							with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(lowerCamelCase_ )
									lowercase__				:			Optional[Any]            =		model_class.from_pretrained(lowerCamelCase_,				from_pt=lowerCamelCase_ )

							lowercase__				:			Tuple            =		fx_model_loaded(**lowerCamelCase_ ).to_tuple()
							self.assertEqual(
							    len(lowerCamelCase_ ),				len(lowerCamelCase_ ),				'Output lengths differ between Flax and PyTorch' )
							for fx_output_loaded, pt_output in zip(lowerCamelCase_,				lowerCamelCase_ ):
									self.assert_almost_equals(fx_output_loaded[:, -1],				pt_output[:, -1].numpy(),				4E-2 )

	@is_pt_flax_cross_test
	def       snake_case__(	self:	Any ):
			lowercase__      ,							lowercase__				:			List[str]            =		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
							lowercase__				:			Dict            =		self._prepare_for_class(lowerCamelCase_,				lowerCamelCase_ )
							lowercase__				:			Tuple            =		{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

							lowercase__				:			int            =		pt_model_class(lowerCamelCase_ ).eval()
							lowercase__				:			Tuple            =		model_class(lowerCamelCase_,				dtype=jnp.floataa )

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

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

							with torch.no_grad():
									lowercase__				:			List[Any]            =		pt_model(**lowerCamelCase_ ).to_tuple()

							lowercase__				:			Union[str, Any]            =		fx_model(**lowerCamelCase_ ).to_tuple()
							self.assertEqual(len(lowerCamelCase_ ),				len(lowerCamelCase_ ),				'Output lengths differ between Flax and PyTorch' )
							for fx_output, pt_output in zip(lowerCamelCase_,				lowerCamelCase_ ):
									self.assert_almost_equals(fx_output[:, -1],				pt_output[:, -1].numpy(),				4E-2 )

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

							with torch.no_grad():
									lowercase__				:			Dict            =		pt_model_loaded(**lowerCamelCase_ ).to_tuple()

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





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









import sacrebleu as scb
from packaging import version

import datasets


__UpperCamelCase:  Optional[Any]   =   """\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__UpperCamelCase:  int   =   """\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

__UpperCamelCase:  int   =   """
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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


	'''simple docstring'''





	def       snake_case__(	self:	Optional[Any] ):
			if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
					raise ImportWarning(
					    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
					    'You can install it with `pip install "sacrebleu>=1.4.12"`.' )
			return datasets.MetricInfo(
			    description=_DESCRIPTION,				citation=_CITATION,				homepage='https://github.com/mjpost/sacreBLEU',				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/mjpost/sacreBLEU'],				reference_urls=[
			        'https://github.com/mjpost/sacreBLEU',
			        'https://en.wikipedia.org/wiki/BLEU',
			        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
			    ],				)





	def       snake_case__(	self:	Optional[Any],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	List[str],				lowerCamelCase_:	Dict="exp",				lowerCamelCase_:	Optional[int]=None,				lowerCamelCase_:	Dict=False,				lowerCamelCase_:	int=False,				lowerCamelCase_:	Tuple=None,				lowerCamelCase_:	str=False,				):
			lowercase__				:			int            =		len(references[0] )
			if any(len(lowerCamelCase_ ) != references_per_prediction for refs in references ):
					raise ValueError('Sacrebleu requires the same number of references for each prediction' )
			lowercase__				:			Optional[Any]            =		[[refs[i] for refs in references] for i in range(lowerCamelCase_ )]
			lowercase__				:			List[str]            =		scb.corpus_bleu(
			    lowerCamelCase_,				lowerCamelCase_,				smooth_method=lowerCamelCase_,				smooth_value=lowerCamelCase_,				force=lowerCamelCase_,				lowercase=lowerCamelCase_,				use_effective_order=lowerCamelCase_,				**({'tokenize': tokenize} if tokenize else {}),				)
			lowercase__				:			Union[str, Any]            =		{
			    'score': output.score,
			    'counts': output.counts,
			    'totals': output.totals,
			    'precisions': output.precisions,
			    'bp': output.bp,
			    'sys_len': output.sys_len,
			    'ref_len': output.ref_len,
			}
			return output_dict


