



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=9_9			,    a__=1_3			,    a__=7			,    a__=9			,    a__=True			,    a__=True			,    a__=False			,    a__=3_2			,    a__=5			,    a__=4			,    a__=3_7			,    a__=8			,    a__=0.1			,    a__=0.0_0_2			,    a__=1			,    a__=0			,    a__=0			,    a__=None			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       encoder_seq_length
         A__					=       decoder_seq_length
         # For common tests
         A__					=       self.decoder_seq_length
         A__					=       is_training
         A__					=       use_attention_mask
         A__					=       use_labels
         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       d_ff
         A__					=       relative_attention_num_buckets
         A__					=       dropout_rate
         A__					=       initializer_factor
         A__					=       eos_token_id
         A__					=       pad_token_id
         A__					=       decoder_start_token_id
         A__					=       None
         A__					=       decoder_layers




     def 		snake_case_		(					self):
         return TaConfig.from_pretrained('''google/umt5-base''')




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    ):
         if attention_mask is None:
             A__					=       input_ids.ne(config.pad_token_id)
         if decoder_attention_mask is None:
             A__					=       decoder_input_ids.ne(config.pad_token_id)
         if head_mask is None:
             A__					=       torch.ones(config.num_hidden_layers			,    config.num_attention_heads			,    device=a__)
         if decoder_head_mask is None:
             A__					=       torch.ones(config.num_decoder_layers			,    config.num_attention_heads			,    device=a__)
         if cross_attn_head_mask is None:
             A__					=       torch.ones(
                 config.num_decoder_layers			,    config.num_attention_heads			,    device=a__)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }




     def 		snake_case_		(					self):
         A__					=       ids_tensor([self.batch_size, self.encoder_seq_length]			,    self.vocab_size)
         A__					=       ids_tensor([self.batch_size, self.decoder_seq_length]			,    self.vocab_size)

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         A__					=       input_ids.clamp(self.pad_token_id + 1)
         A__					=       decoder_input_ids.clamp(self.pad_token_id + 1)

         A__					=       self.get_config()
         A__					=       config.num_attention_heads
         A__					=       self.prepare_inputs_dict(a__			,    a__			,    a__)
         return config, input_dict




     def 		snake_case_		(					self):
         A__			,						A__					=       self.prepare_config_and_inputs()
         return config, inputs_dict




     def 		snake_case_		(					self):
         return TaConfig(
             vocab_size=1_6_6			,    d_model=self.hidden_size			,    d_ff=self.d_ff			,    d_kv=self.hidden_size // self.num_attention_heads			,    num_layers=self.num_hidden_layers			,    num_decoder_layers=self.decoder_layers			,    num_heads=self.num_attention_heads			,    relative_attention_num_buckets=self.relative_attention_num_buckets			,    dropout_rate=self.dropout_rate			,    initializer_factor=self.initializer_factor			,    eos_token_id=self.eos_token_id			,    bos_token_id=self.pad_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.decoder_start_token_id			,    )




     def 		snake_case_		(					self):
         return TaConfig(
             vocab_size=self.vocab_size			,    d_model=self.hidden_size			,    d_ff=self.d_ff			,    d_kv=self.hidden_size // self.num_attention_heads			,    num_layers=self.num_hidden_layers			,    num_decoder_layers=self.decoder_layers			,    num_heads=self.num_attention_heads			,    relative_attention_num_buckets=self.relative_attention_num_buckets			,    dropout_rate=self.dropout_rate			,    initializer_factor=self.initializer_factor			,    eos_token_id=self.eos_token_id			,    bos_token_id=self.pad_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.decoder_start_token_id			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         A__					=       UMTaModel(config=a__)
         model.to(a__)
         model.eval()
         A__					=       model(
             input_ids=a__			,    decoder_input_ids=a__			,    attention_mask=a__			,    decoder_attention_mask=a__			,    )
         A__					=       model(input_ids=a__			,    decoder_input_ids=a__)
         A__					=       result.last_hidden_state
         A__					=       result.past_key_values
         A__					=       result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()			,    (self.batch_size, self.encoder_seq_length, self.hidden_size))
         self.parent.assertEqual(decoder_output.size()			,    (self.batch_size, self.decoder_seq_length, self.hidden_size))
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(a__)			,    config.num_layers)
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0])			,    4)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    ):
         A__					=       UMTaModel(config=a__).get_decoder().to(a__).eval()
         # first forward pass
         A__					=       model(a__			,    use_cache=a__)
         A__					=       model(a__)
         A__					=       model(a__			,    use_cache=a__)

         self.parent.assertTrue(len(a__) == len(a__))
         self.parent.assertTrue(len(a__) == len(a__) + 1)

         A__			,						A__					=       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         A__					=       ids_tensor((self.batch_size, 1)			,    config.vocab_size)

         # append to next input_ids and
         A__					=       torch.cat([input_ids, next_tokens]			,    dim=-1)

         A__					=       model(a__)['''last_hidden_state''']
         A__					=       model(a__			,    past_key_values=a__)['''last_hidden_state''']

         # select random slice
         A__					=       ids_tensor((1,)			,    output_from_past.shape[-1]).item()
         A__					=       output_from_no_past[:, -1, random_slice_idx].detach()
         A__					=       output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(a__			,    a__			,    atol=1e-3))




     def 		snake_case_		(					self			,    a__			,    a__			,    ):
         A__					=       UMTaModel(config=a__).to(a__).half().eval()
         A__					=       model(**a__)['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(a__).any().item())







@require_torch
class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     UpperCamelCase__		     =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
     UpperCamelCase__		     =					(
         {
             '''conversational''': UMTaForConditionalGeneration,
             '''feature-extraction''': UMTaModel,
             '''summarization''': UMTaForConditionalGeneration,
             '''text2text-generation''': UMTaForConditionalGeneration,
             '''translation''': UMTaForConditionalGeneration,
             '''question-answering''': UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     UpperCamelCase__		     =					True
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					True
     UpperCamelCase__		     =					True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     UpperCamelCase__		     =					[0.8, 0.9]




     def 		snake_case_		(					self):
         A__					=       UMTaModelTester(self)




     @unittest.skip('''Test has a segmentation fault on torch 1.8.0''')
     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         A__					=       UMTaModel(config_and_inputs[0]).to(a__)
         with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 a__			,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,    F"{tmpdirname}/t5_test.onnx"			,    export_params=a__			,    opset_version=9			,    input_names=['''input_ids''', '''decoder_input_ids''']			,    )




     @unittest.skipIf(torch_device == '''cpu'''			,    '''Cant do half precision''')
     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*a__)




     def 		snake_case_		(					self):
         A__					=       ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
         A__					=       self.model_tester.prepare_config_and_inputs()
         A__					=       config_and_inputs[0]
         A__					=       UMTaForConditionalGeneration(a__).eval()
         model.to(a__)

         A__					=       {
             '''head_mask''': torch.zeros(config.num_layers			,    config.num_heads			,    device=a__),
             '''decoder_head_mask''': torch.zeros(config.num_decoder_layers			,    config.num_heads			,    device=a__),
             '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers			,    config.num_heads			,    device=a__),
         }

         for attn_name, (name, mask) in zip(a__			,    head_masking.items()):
             A__					=       {name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                 A__					=       torch.ones(
                     config.num_decoder_layers			,    config.num_heads			,    device=a__)

             A__					=       model.generate(
                 config_and_inputs[1]['''input_ids''']			,    num_beams=1			,    max_length=3			,    output_attentions=a__			,    return_dict_in_generate=a__			,    **a__			,    )
             # We check the state of decoder_attentions and cross_attentions just from the last step
             A__					=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
             self.assertEqual(sum([w.sum().item() for w in attn_weights])			,    0.0)




     @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''')
     def 		snake_case_		(					self):
         pass







@require_torch
@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @slow
     @unittest.skip(
         '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''')
     def 		snake_case_		(					self):
         A__					=       UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''			,    return_dict=a__).to(a__)
         A__					=       AutoTokenizer.from_pretrained('''google/umt5-small'''			,    use_fast=a__			,    legacy=a__)
         A__					=       [
             '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
             '''No se como puedo <extra_id_0>.''',
             '''This is the reason why we <extra_id_0> them.''',
             '''The <extra_id_0> walks in <extra_id_1>, seats''',
             '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
         ]
         A__					=       tokenizer(a__			,    return_tensors='''pt'''			,    padding=a__).input_ids
         # fmt: off
         A__					=       torch.tensor(
             [
                 [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
             ])
         # fmt: on
         torch.testing.assert_allclose(a__			,    a__)

         A__					=       model.generate(input_ids.to(a__))
         A__					=       [
             '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
             '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
             '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
         ]
         A__					=       tokenizer.batch_decode(a__)
         self.assertEqual(a__			,    a__)





import faiss  # 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 requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowercase    	=		"\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase    	=		"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase    	=		"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\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.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"



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




     def 		snake_case_		(					self):
         return datasets.MetricInfo(
             description=_DESCRIPTION			,    citation=_CITATION			,    homepage='''https://github.com/krishnap25/mauve'''			,    inputs_description=_KWARGS_DESCRIPTION			,    features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''			,    id='''sequence'''),
                     '''references''': datasets.Value('''string'''			,    id='''sequence'''),
                 })			,    codebase_urls=['''https://github.com/krishnap25/mauve''']			,    reference_urls=[
                 '''https://arxiv.org/abs/2102.01454''',
                 '''https://github.com/krishnap25/mauve''',
             ]			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__="auto"			,    a__=-1			,    a__=0.9			,    a__=5			,    a__=5_0_0			,    a__="gpt2-large"			,    a__=-1			,    a__=1_0_2_4			,    a__=2_5			,    a__=5			,    a__=True			,    a__=2_5			,    ):
         A__					=       compute_mauve(
             p_text=a__			,    q_text=a__			,    p_features=a__			,    q_features=a__			,    p_tokens=a__			,    q_tokens=a__			,    num_buckets=a__			,    pca_max_data=a__			,    kmeans_explained_var=a__			,    kmeans_num_redo=a__			,    kmeans_max_iter=a__			,    featurize_model_name=a__			,    device_id=a__			,    max_text_length=a__			,    divergence_curve_discretization_size=a__			,    mauve_scaling_factor=a__			,    verbose=a__			,    seed=a__			,    )
         return out
