





"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece

class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[str]									=    list(tokenizer_class.max_model_input_sizes.keys()    )
        for model_name in sa_models[:1]:
          A_    :			List[Any]									=    tokenizer_class.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )

          for special_tok in tokenizer.all_special_tokens:
            self.assertIsInstance(snake_case_   , snake_case_    )
            A_    :			Optional[int]									=    tokenizer.convert_tokens_to_ids(snake_case_    )
            self.assertIsInstance(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        A_    :			Tuple									=    pickle.dumps(snake_case_    )
        self.assertIsNotNone(snake_case_    )

        A_    :			Tuple									=    pickle.loads(snake_case_    )

        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast   , be_original.is_fast    )

        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
          self.assertIsNotNone(be_restored.encodings    )
        else:
          self.assertIsNone(be_restored.encodings    )

        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()   , be_restored.values()    ):
          if equal_op:
            self.assertTrue(equal_op(snake_case_   , snake_case_    )    )
          else:
            self.assertEqual(snake_case_   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.check_tokenizer_from_pretrained(snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertEqual(TensorType('tf'    )   , TensorType.TENSORFLOW    )
        self.assertEqual(TensorType('pt'    )   , TensorType.PYTORCH    )
        self.assertEqual(TensorType('np'    )   , TensorType.NUMPY    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import numpy as np

        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Optional[int]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        # Python no tensor
        with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

      @require_tf
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        def tf_array_equals(snake_case_   , snake_case_    ):
          return tf.reduce_all(tf.equal(snake_case_   , snake_case_    )    )

        A_    :			Dict									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			int									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

        with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

      @require_torch
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			List[Any]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('Python Tokenizer'    ):
          self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )

        with self.subTest('Rust Tokenizer'    ):
          self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    tokenizer_r(['Test', '\xad', 'test']   , is_split_into_words=snake_case_    )

        self.assertEqual(encoded.word_to_tokens(0    )   , TokenSpan(start=1   , end=2    )    )
        self.assertEqual(encoded.word_to_tokens(1    )   , snake_case_    )
        self.assertEqual(encoded.word_to_tokens(2    )   , TokenSpan(start=2   , end=3    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			str									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Any									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			List[Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='np'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			str									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			str									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Any									=    batch.convert_to_tensors(tensor_type='pt'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[int]									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			int									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='tf'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_flax
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			List[str]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			Union[str, Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Optional[Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='np'    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Tuple									=    [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
        A_    :			int									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Any									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			int									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='pt'    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        A_    :			Dict									=    [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='tf'    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        PreTrainedTokenizerFast(tokenizer_object=snake_case_    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        with tempfile.TemporaryDirectory() as tmpdirname:
          bert_tokenizer.save(os.path.join(snake_case_   , 'tokenizer.json'    )    )
          PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case_   , 'tokenizer.json'    )    )
'''simple docstring'''




import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  str:
        lowercase_       :							List[str]  					=       {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
        pl_module.logger.log_metrics(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a=True					)     ->  None:
        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
        lowercase_       :							Union[str, Any]  					=       trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}					)
        # Log results
        lowercase_       :							Optional[int]  					=       Path(pl_module.hparams.output_dir					)
        if type_path == "test":
             lowercase_       :							Union[str, Any]  					=       od / 'test_results.txt'
             lowercase_       :							Tuple  					=       od / 'test_generations.txt'
        else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             lowercase_       :							List[str]  					=       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
             lowercase_       :							int  					=       od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
             results_file.parent.mkdir(exist_ok=_a					)
             generations_file.parent.mkdir(exist_ok=_a					)
        with open(_a							,					'a+'					) as writer:
             for key in sorted(_a					):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  lowercase_       :							Any  					=       metrics[key]
                  if isinstance(_a							,					torch.Tensor					):
                       lowercase_       :							Any  					=       val.item()
                  lowercase_       :							List[str]  					=       f'''{key}: {val:.6f}\n'''
                  writer.write(_a					)

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       count_trainable_parameters(_a					)
        # mp stands for million parameters
        trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  int:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        return self._write_logs(_a							,					_a							,					'test'					)





   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              ' this function.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							patience=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							)




