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        a_				(		unittest.TestCase		):






 '''simple docstring'''






 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      list(tokenizer_class.max_model_input_sizes.keys())
        for model_name in sa_models[:1]:
               lowerCAmelCase__  			=      tokenizer_class.from_pretrained(lowercase__)
               self.assertIsNotNone(lowercase__)
               self.assertIsInstance(lowercase__    ,					lowercase__)
               self.assertIsInstance(lowercase__    ,					lowercase__)

               for special_tok in tokenizer.all_special_tokens:
                      self.assertIsInstance(lowercase__    ,					lowercase__)
                      lowerCAmelCase__  			=      tokenizer.convert_tokens_to_ids(lowercase__)
                      self.assertIsInstance(lowercase__    ,					lowercase__)




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						BatchEncoding    ,					lowercase__      :						Optional[Callable] = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      pickle.dumps(lowercase__)
        self.assertIsNotNone(lowercase__)

        lowerCAmelCase__  			=      pickle.loads(lowercase__)

        # 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(lowercase__    ,					lowercase__))
               else:
                      self.assertEqual(lowercase__    ,					lowercase__)




 @slow
 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        self.check_tokenizer_from_pretrained(lowercase__)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



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




 @require_tokenizers
 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        import numpy as np

        lowerCAmelCase__  			=      BertTokenizer.from_pretrained('bert-base-cased')
        lowerCAmelCase__  			=      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 	__snake_case		(    self      :						Union[str, Any]):







        '''simple docstring'''



        import tensorflow as tf

        def tf_array_equals(lowercase__      :						Tuple    ,					lowercase__      :						Optional[int]):
               return tf.reduce_all(tf.equal(lowercase__    ,					lowercase__))

        lowerCAmelCase__  			=      BertTokenizer.from_pretrained('bert-base-cased')
        lowerCAmelCase__  			=      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)    ,					lowercase__)

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




 @require_torch
 @require_tokenizers
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        import torch

        lowerCAmelCase__  			=      BertTokenizer.from_pretrained('bert-base-cased')
        lowerCAmelCase__  			=      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 	__snake_case		(    self      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      BertTokenizer.from_pretrained('bert-base-cased')
        lowerCAmelCase__  			=      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 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      BertTokenizerFast.from_pretrained('bert-base-cased')
        lowerCAmelCase__  			=      tokenizer_r(['Test', '\xad', 'test']    ,					is_split_into_words=lowercase__)

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




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
        lowerCAmelCase__  			=      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:
               lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='np')
        self.assertFalse(len(cs.err)    ,					msg=F"""should have no warning, but got {cs.err}""")

        lowerCAmelCase__  			=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
        lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='np'    ,					prepend_batch_axis=lowercase__)
        self.assertEqual(tensor_batch['inputs'].shape    ,					(1, 3))
        self.assertEqual(tensor_batch['labels'].shape    ,					(1,))




 @require_torch
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
        lowerCAmelCase__  			=      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:
               lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='pt')
        self.assertFalse(len(cs.err)    ,					msg=F"""should have no warning, but got {cs.err}""")

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




 @require_tf
 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
        lowerCAmelCase__  			=      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:
               lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='tf')
        self.assertFalse(len(cs.err)    ,					msg=F"""should have no warning, but got {cs.err}""")

        lowerCAmelCase__  			=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
        lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='tf'    ,					prepend_batch_axis=lowercase__)
        self.assertEqual(tensor_batch['inputs'].shape    ,					(1, 3))
        self.assertEqual(tensor_batch['labels'].shape    ,					(1,))




 @require_flax
 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]})
        lowerCAmelCase__  			=      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:
               lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='jax')
        self.assertFalse(len(cs.err)    ,					msg=F"""should have no warning, but got {cs.err}""")

        lowerCAmelCase__  			=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
        lowerCAmelCase__  			=      batch.convert_to_tensors(tensor_type='jax'    ,					prepend_batch_axis=lowercase__)
        self.assertEqual(tensor_batch['inputs'].shape    ,					(1, 3))
        self.assertEqual(tensor_batch['labels'].shape    ,					(1,))




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



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

        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__)
        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]])
        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__    ,					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 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        import torch

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

        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__)
        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]])
        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__    ,					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 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        import tensorflow as tf

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

        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__)
        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]])
        lowerCAmelCase__  			=      tokenizer.pad(lowercase__    ,					padding=lowercase__    ,					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 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      Tokenizer(WordPiece(unk_token='[UNK]'))
        PreTrainedTokenizerFast(tokenizer_object=lowercase__)




 @require_tokenizers
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      Tokenizer(WordPiece(unk_token='[UNK]'))
        with tempfile.TemporaryDirectory() as tmpdirname:
               bert_tokenizer.save(os.path.join(lowercase__    ,					'tokenizer.json'))
               PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowercase__    ,					'tokenizer.json'))

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      torch.rand((batch_size, num_channels, height, width))

        return sample




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any):







        '''simple docstring'''



        if isinstance(image[0]    ,					PIL.Image.Image):
               return np.stack([np.array(lowercase__) for i in image]    ,					axis=0)
        elif isinstance(lowercase__    ,					torch.Tensor):
               return image.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        return image




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      self.to_np(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(
                   image_processor.preprocess(lowercase__)    ,					output_type=lowercase__    ,					)
               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)
        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)

               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        lowerCAmelCase__  			=      image_processor.numpy_to_pil(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)
               for i, o in zip(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      np.array(lowercase__)
                      lowerCAmelCase__  			=      self.to_np(lowercase__) if output_type == 'pil' else (self.to_np(lowercase__) * 255).round()
                      assert (
                          np.abs(in_np - out_np).max() < 1e-6
                      ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      input_pt_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        lowerCAmelCase__  			=      input_np_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_ad).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad).max() < 1e-6




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_list).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list).max() < 1e-6
