


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase      =   {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase      =   {
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

UpperCAmelCase      =   {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  VOCAB_FILES_NAMES
 __snake_case												=  PRETRAINED_VOCAB_FILES_MAP
 __snake_case												=  PRETRAINED_INIT_CONFIGURATION
 __snake_case												=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case												=  RealmTokenizer



 def __init__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase="[UNK]"		,				_UpperCAmelCase="[SEP]"		,				_UpperCAmelCase="[PAD]"		,				_UpperCAmelCase="[CLS]"		,				_UpperCAmelCase="[MASK]"		,				_UpperCAmelCase=True		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(
           _UpperCAmelCase		,				tokenizer_file=_UpperCAmelCase		,				do_lower_case=_UpperCAmelCase		,				unk_token=_UpperCAmelCase		,				sep_token=_UpperCAmelCase		,				pad_token=_UpperCAmelCase		,				cls_token=_UpperCAmelCase		,				mask_token=_UpperCAmelCase		,				tokenize_chinese_chars=_UpperCAmelCase		,				strip_accents=_UpperCAmelCase		,				**_UpperCAmelCase		,				)

       snake_case_	=				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
       if (
           normalizer_state.get('''lowercase'''		,				_UpperCAmelCase ) != do_lower_case
           or normalizer_state.get('''strip_accents'''		,				_UpperCAmelCase ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''		,				_UpperCAmelCase ) != tokenize_chinese_chars
       ):
             snake_case_	=				getattr(_UpperCAmelCase		,				normalizer_state.pop('''type''' ) )
             snake_case_	=				do_lower_case
             snake_case_	=				strip_accents
             snake_case_	=				tokenize_chinese_chars
             snake_case_	=				normalizer_class(**_UpperCAmelCase )

       snake_case_	=				do_lower_case



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				**_UpperCAmelCase ):
       snake_case_	=				PaddingStrategy.MAX_LENGTH

       snake_case_	=				text
       snake_case_	=				kwargs.pop('''text_pair'''		,				_UpperCAmelCase )
       snake_case_	=				kwargs.pop('''return_tensors'''		,				_UpperCAmelCase )

       snake_case_	=				{
           '''input_ids''': [],
           '''attention_mask''': [],
           '''token_type_ids''': [],
       }

       for idx, candidate_text in enumerate(_UpperCAmelCase ):
             if batch_text_pair is not None:
                   snake_case_	=				batch_text_pair[idx]
             else:
                   snake_case_	=				None

             snake_case_	=				super().__call__(_UpperCAmelCase		,				_UpperCAmelCase		,				return_tensors=_UpperCAmelCase		,				**_UpperCAmelCase )

             snake_case_	=				encoded_candidates.get('''input_ids''' )
             snake_case_	=				encoded_candidates.get('''attention_mask''' )
             snake_case_	=				encoded_candidates.get('''token_type_ids''' )

             if encoded_input_ids is not None:
                   output_data["input_ids"].append(_UpperCAmelCase )
             if encoded_attention_mask is not None:
                   output_data["attention_mask"].append(_UpperCAmelCase )
             if encoded_token_type_ids is not None:
                   output_data["token_type_ids"].append(_UpperCAmelCase )

       snake_case_	=				{key: item for key, item in output_data.items() if len(_UpperCAmelCase ) != 0}

       return BatchEncoding(_UpperCAmelCase		,				tensor_type=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=None ):
       snake_case_	=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				[self.sep_token_id]
       snake_case_	=				[self.cls_token_id]
       if token_ids_a is None:
             return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				self._tokenizer.model.save(_UpperCAmelCase		,				name=_UpperCAmelCase )
       return tuple(_UpperCAmelCase )



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCAmelCase      =   False







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				VQModel(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=3		,				num_vq_embeddings=self.num_embed		,				vq_embed_dim=3		,				)
       return model



 @property
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=self.text_embedder_hidden_size		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
       return CLIPTextModel(_UpperCAmelCase )



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )

       snake_case_	=				12
       snake_case_	=				12

       snake_case_	=				{
           '''attention_bias''': True,
           '''cross_attention_dim''': 32,
           '''attention_head_dim''': height * width,
           '''num_attention_heads''': 1,
           '''num_vector_embeds''': self.num_embed,
           '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
           '''norm_num_groups''': 32,
           '''sample_size''': width,
           '''activation_fn''': '''geglu-approximate''',
       }

       snake_case_	=				TransformeraDModel(**_UpperCAmelCase )
       return model



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.dummy_vqvae
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_transformer
       snake_case_	=				VQDiffusionScheduler(self.num_embed )
       snake_case_	=				LearnedClassifierFreeSamplingEmbeddings(learnable=_UpperCAmelCase )

       snake_case_	=				VQDiffusionPipeline(
           vqvae=_UpperCAmelCase		,				text_encoder=_UpperCAmelCase		,				tokenizer=_UpperCAmelCase		,				transformer=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				learned_classifier_free_sampling_embeddings=_UpperCAmelCase		,				)
       snake_case_	=				pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				'''teddy bear playing in the pool'''

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe([prompt]		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np''' )
       snake_case_	=				output.images

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe(
           [prompt]		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				return_dict=_UpperCAmelCase		,				num_inference_steps=2 )[0]

       snake_case_	=				image[0, -3:, -3:, -1]
       snake_case_	=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       snake_case_	=				np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.dummy_vqvae
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_transformer
       snake_case_	=				VQDiffusionScheduler(self.num_embed )
       snake_case_	=				LearnedClassifierFreeSamplingEmbeddings(
           learnable=_UpperCAmelCase		,				hidden_size=self.text_embedder_hidden_size		,				length=tokenizer.model_max_length )

       snake_case_	=				VQDiffusionPipeline(
           vqvae=_UpperCAmelCase		,				text_encoder=_UpperCAmelCase		,				tokenizer=_UpperCAmelCase		,				transformer=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				learned_classifier_free_sampling_embeddings=_UpperCAmelCase		,				)
       snake_case_	=				pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				'''teddy bear playing in the pool'''

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe([prompt]		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np''' )
       snake_case_	=				output.images

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe(
           [prompt]		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				return_dict=_UpperCAmelCase		,				num_inference_steps=2 )[0]

       snake_case_	=				image[0, -3:, -3:, -1]
       snake_case_	=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       snake_case_	=				np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2







@slow
@require_torch_gpu
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

       snake_case_	=				VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
       snake_case_	=				pipeline.to(_UpperCAmelCase )
       pipeline.set_progress_bar_config(disable=_UpperCAmelCase )

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipeline(
           '''teddy bear playing in the pool'''		,				num_images_per_prompt=1		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)

       snake_case_	=				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image ).max() < 2.0