




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  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    def    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Dict:
         lowerCAmelCase__		= list(tokenizer_class.max_model_input_sizes.keys()   )
         for model_name in sa_models[:1]:
              lowerCAmelCase__		= tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

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


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     BatchEncoding   ,	SCREAMING_SNAKE_CASE__     :     Optional[Callable] = None   )      ->       Optional[int]:
         lowerCAmelCase__		= pickle.dumps(SCREAMING_SNAKE_CASE__   )
         self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= pickle.loads(SCREAMING_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(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )   )
              else:
                   self.assertEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )


    @slow
    def    a      (       self     :     Tuple   )      ->       List[str]:
         self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Optional[Any]   )      ->       Optional[Any]:
         self.assertEqual(TensorType("tf"   )   ,	TensorType.TENSORFLOW   )
         self.assertEqual(TensorType("pt"   )   ,	TensorType.PYTORCH   )
         self.assertEqual(TensorType("np"   )   ,	TensorType.NUMPY   )


    @require_tokenizers
    def    a      (       self     :     List[Any]   )      ->       Tuple:
         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    a      (       self     :     Optional[int]   )      ->       str:
         import tensorflow as tf

         def tf_array_equals(SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Any   ):
              return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )   )

         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   )   ,	SCREAMING_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   )   ,	SCREAMING_SNAKE_CASE__   )


    @require_torch
    @require_tokenizers
    def    a      (       self     :     int   )      ->       int:
         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    a      (       self     :     Dict   )      ->       Tuple:
         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    a      (       self     :     Tuple   )      ->       Union[str, Any]:
         lowerCAmelCase__		= BertTokenizerFast.from_pretrained("bert-base-cased"   )
         lowerCAmelCase__		= tokenizer_r(["Test", "\xad", "test"]   ,	is_split_into_words=SCREAMING_SNAKE_CASE__   )

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


    def    a      (       self     :     List[Any]   )      ->       Dict:
         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=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(tensor_batch["inputs"].shape   ,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape   ,	(1,)   )


    @require_torch
    def    a      (       self     :     List[str]   )      ->       Optional[int]:
         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=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(tensor_batch["inputs"].shape   ,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape   ,	(1,)   )


    @require_tf
    def    a      (       self     :     Optional[int]   )      ->       Optional[Any]:
         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=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(tensor_batch["inputs"].shape   ,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape   ,	(1,)   )


    @require_flax
    def    a      (       self     :     Tuple   )      ->       str:
         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=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(tensor_batch["inputs"].shape   ,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape   ,	(1,)   )


    def    a      (       self     :     int   )      ->       Any:
         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(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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]]   )
         lowerCAmelCase__		= tokenizer.pad(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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    a      (       self     :     Any   )      ->       Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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]]   )
         lowerCAmelCase__		= tokenizer.pad(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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    a      (       self     :     Dict   )      ->       Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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]]   )
         lowerCAmelCase__		= tokenizer.pad(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_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    a      (       self     :     Dict   )      ->       Dict:
         lowerCAmelCase__		= Tokenizer(WordPiece(unk_token="[UNK]"   )   )
         PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE__   )


    @require_tokenizers
    def    a      (       self     :     List[str]   )      ->       Any:
         lowerCAmelCase__		= Tokenizer(WordPiece(unk_token="[UNK]"   )   )
         with tempfile.TemporaryDirectory() as tmpdirname:
              bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE__   ,	"tokenizer.json"   )   )
              PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE__   ,	"tokenizer.json"   )   )






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
