

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch

class    _lowercase				:

   def __init__(				self	:    Optional[Any]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]=1_4			,						lowerCamelCase__	:    str=7			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]=9_9			,						lowerCamelCase__	:    str=3_2			,						lowerCamelCase__	:    str=4			,						lowerCamelCase__	:    Dict=4			,						lowerCamelCase__	:    Optional[Any]=4			,						lowerCamelCase__	:    Optional[Any]=3_7			,						lowerCamelCase__	:    int="gelu"			,						lowerCamelCase__	:    Tuple=0.1			,						lowerCamelCase__	:    List[Any]=0.1			,						lowerCamelCase__	:    Optional[int]=5_1_2			,						lowerCamelCase__	:    Dict=0.02			,						) ->			int:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		rotary_dim
         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_							       =		initializer_range
         A_							       =		None
         A_							       =		vocab_size - 1
         A_							       =		vocab_size - 1
         A_							       =		vocab_size - 1

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[Any]:
         """simple docstring"""





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

         A_							       =		None
         if self.use_input_mask:
               A_							       =		random_attention_mask([self.batch_size, self.seq_length]       )

         A_							       =		GPTJConfig(
             vocab_size=self.vocab_size			,						n_embd=self.hidden_size			,						n_layer=self.num_hidden_layers			,						n_head=self.num_attention_heads			,						n_positions=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			,						rotary_dim=self.rotary_dim			,						)

         return (config, input_ids, input_mask)

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			str:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         A_       ,A_       ,A_							       =		config_and_inputs
         A_							       =		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    str       ) ->			Any:
         """simple docstring"""





         A_							       =		2_0
         A_							       =		model_class_name(lowercase_       )

         A_							       =		model.init_cache(input_ids.shape[0]			,						lowercase_       )
         A_							       =		jnp.ones((input_ids.shape[0], max_decoder_length)			,						dtype='''i4'''       )

         A_							       =		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]			,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )
         A_							       =		model(
             input_ids[:, :-1]			,						attention_mask=lowercase_			,						past_key_values=lowercase_			,						position_ids=lowercase_			,						)

         A_							       =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,						dtype='''i4'''       )
         A_							       =		model(
             input_ids[:, -1:]			,						attention_mask=lowercase_			,						past_key_values=outputs_cache.past_key_values			,						position_ids=lowercase_			,						)

         A_							       =		model(lowercase_       )

         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					(				self	:    Tuple			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





         A_							       =		2_0
         A_							       =		model_class_name(lowercase_       )

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

         A_							       =		model.init_cache(input_ids.shape[0]			,						lowercase_       )
         A_							       =		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]			,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )

         A_							       =		model(
             input_ids[:, :-1]			,						attention_mask=lowercase_			,						past_key_values=lowercase_			,						position_ids=lowercase_			,						)
         A_							       =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,						dtype='''i4'''       )
         A_							       =		model(
             input_ids[:, -1:]			,						past_key_values=outputs_cache.past_key_values			,						attention_mask=lowercase_			,						position_ids=lowercase_			,						)

         A_							       =		model(lowercase_			,						attention_mask=lowercase_       )

         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    _lowercase				(					lowercase__,lowercase__,unittest.TestCase			):
   _lowercase     :       Tuple  					=    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   _lowercase     :       str  					=    (FlaxGPTJForCausalLM,) if is_flax_available() else ()

   def     UpperCamelCase					(				self	:    str       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		FlaxGPTJModelTester(self       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Tuple:
         """simple docstring"""





         for model_class_name in self.all_model_classes:
               A_       ,A_       ,A_							       =		self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowercase_			,						lowercase_			,						lowercase_			,						lowercase_       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         for model_class_name in self.all_model_classes:
               A_       ,A_       ,A_							       =		self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowercase_			,						lowercase_			,						lowercase_			,						lowercase_       )

   @tooslow
   def     UpperCamelCase					(				self	:    str       ) ->			Any:
         """simple docstring"""





         A_							       =		GPTaTokenizer.from_pretrained('''gpt2'''			,						pad_token='''<|endoftext|>'''			,						padding_side='''left'''       )
         A_							       =		tokenizer(['''Hello this is a long string''', '''Hey''']			,						return_tensors='''np'''			,						padding=lowercase_			,						truncation=lowercase_       )

         A_							       =		FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''       )
         A_							       =		False
         A_							       =		model.config.eos_token_id

         A_							       =		jax.jit(model.generate       )

         A_							       =		jit_generate(
             inputs['''input_ids''']			,						attention_mask=inputs['''attention_mask''']			,						pad_token_id=tokenizer.pad_token_id       ).sequences

         A_							       =		tokenizer.batch_decode(lowercase_			,						skip_special_tokens=lowercase_       )

         A_							       =		[
             '''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     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         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__       ):
                     # prepare inputs
                     A_							       =		self._prepare_for_class(lowercase_			,						lowercase_       )
                     A_							       =		{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                     # load corresponding PyTorch class
                     A_							       =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     A_							       =		getattr(lowercase_			,						lowercase_       )

                     A_       ,A_							       =		pt_inputs['''input_ids'''].shape
                     A_							       =		np.random.randint(0			,						seq_length - 1			,						size=(batch_size,)       )
                     for batch_idx, start_index in enumerate(lowercase_       ):
                           A_							       =		0
                           A_							       =		1
                           A_							       =		0
                           A_							       =		1
                     A_							       =		pt_model_class(lowercase_       ).eval()
                     A_							       =		model_class(lowercase_			,						dtype=jnp.floataa       )

                     A_							       =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,						lowercase_       )
                     A_							       =		fx_state

                     with torch.no_grad():
                           A_							       =		pt_model(**lowercase_       ).to_tuple()

                     A_							       =		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_       )
                           A_							       =		model_class.from_pretrained(lowercase_			,						from_pt=lowercase_       )

                     A_							       =		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     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         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__       ):
                     # prepare inputs
                     A_							       =		self._prepare_for_class(lowercase_			,						lowercase_       )
                     A_							       =		{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                     # load corresponding PyTorch class
                     A_							       =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     A_							       =		getattr(lowercase_			,						lowercase_       )

                     A_							       =		pt_model_class(lowercase_       ).eval()
                     A_							       =		model_class(lowercase_			,						dtype=jnp.floataa       )

                     A_							       =		load_flax_weights_in_pytorch_model(lowercase_			,						fx_model.params       )
                     A_       ,A_							       =		pt_inputs['''input_ids'''].shape
                     A_							       =		np.random.randint(0			,						seq_length - 1			,						size=(batch_size,)       )
                     for batch_idx, start_index in enumerate(lowercase_       ):
                           A_							       =		0
                           A_							       =		1
                           A_							       =		0
                           A_							       =		1

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

                     with torch.no_grad():
                           A_							       =		pt_model(**lowercase_       ).to_tuple()

                     A_							       =		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_       )
                           A_							       =		pt_model_class.from_pretrained(lowercase_			,						from_flax=lowercase_       )

                     with torch.no_grad():
                           A_							       =		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       )





   @tooslow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Tuple:
         """simple docstring"""





         for model_class_name in self.all_model_classes:
               A_							       =		model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''       )
               A_							       =		model(np.ones((1, 1)       )       )
               self.assertIsNotNone(lowercase_       )









import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCAmelCase:						Union[str, Any]   				=       '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

_lowerCAmelCase:						Dict   				=       '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

_lowerCAmelCase:						List[Any]   				=       '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




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





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
											        {
											            'predictions': datasets.Value('string'       ,  id='sequence'),
											            'references': datasets.Value('string'       ,  id='sequence'),
											        })       ,  codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']       ,  reference_urls=[
											        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
											        'https://github.com/google-research/google-research/tree/master/rouge',
											    ]       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

											a__      =rouge_scorer.RougeScorer(rouge_types=lowercase_       ,  use_stemmer=lowercase_)
											if use_aggregator:
																		a__      =scoring.BootstrapAggregator()
											else:
																		a__      =[]

											for ref, pred in zip(lowercase_       ,  lowercase_):
																		a__      =scorer.score(lowercase_       ,  lowercase_)
																		if use_aggregator:
																									aggregator.add_scores(lowercase_)
																		else:
																									scores.append(lowercase_)

											if use_aggregator:
																		a__      =aggregator.aggregate()
											else:
																		a__      ={}
																		for key in scores[0]:
																									a__      =[score[key] for score in scores]

											return result

