





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @slow
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""       )

        _SCREAMING_SNAKE_CASE									=			tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] ,		dtype=tf.intaa ,		)  # J'aime le camembert !"

        _SCREAMING_SNAKE_CASE									=			model(__SCREAMING_SNAKE_CASE       )["""last_hidden_state"""]
        _SCREAMING_SNAKE_CASE									=			tf.TensorShape((1, 10, 768)       )
        self.assertEqual(output.shape ,		__SCREAMING_SNAKE_CASE       )
        # compare the actual values for a slice.
        _SCREAMING_SNAKE_CASE									=			tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] ,		dtype=tf.floataa ,		)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,		expected_slice.numpy() ,		atol=1e-4       )       )








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     List[Any]     ,		*__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dataset] = None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()
           __a						       =				(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           __a						       =				(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           __a						       =				gen_kwargs

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __a						       =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                   if not key.startswith(F'{metric_key_prefix}_'):
                       __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)

               metrics.update(output.metrics)
           else:
               __a						       =				output.metrics

           if self.args.should_log:
               # Only the main node log the results by default
               self.log(__SCREAMING_SNAKE_CASE)

           if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report())

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Prediction'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics

           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))
           if self.post_process_function is None or self.compute_metrics is None:
               return output

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
               if not key.startswith(F'{metric_key_prefix}_'):
                   __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions     ,		label_ids=predictions.label_ids     ,		metrics=__SCREAMING_SNAKE_CASE)





