





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

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

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

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

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[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      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[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      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)










import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 	A_       :
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=1							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=3							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				torch.manual_seed(0		)
				UpperCAmelCase   		=				TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''		)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				UNetaDConditionModel(
				    sample_size=3_2							,					layers_per_block=[1, 2]							,					block_out_channels=[3_2, 6_4]							,					down_block_types=[
				        '''ResnetDownsampleBlock2D''',
				        '''SimpleCrossAttnDownBlock2D''',
				    ]							,					mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''							,					up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']							,					in_channels=6							,					out_channels=6							,					cross_attention_dim=3_2							,					encoder_hid_dim=3_2							,					attention_head_dim=8							,					addition_embed_type='''text'''							,					addition_embed_type_num_heads=2							,					cross_attention_norm='''group_norm'''							,					resnet_time_scale_shift='''scale_shift'''							,					act_fn='''gelu'''							,					class_embed_type='''timestep'''							,					mid_block_scale_factor=1.4_14							,					time_embedding_act_fn='''gelu'''							,					time_embedding_dim=3_2							,					)
				unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					thresholding=_A							,					dynamic_thresholding_ratio=0.95							,					sample_max_value=1.0							,					prediction_type='''epsilon'''							,					variance_type='''learned_range'''							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				DDPMScheduler(
				    num_train_timesteps=1_0_0_0							,					beta_schedule='''squaredcos_cap_v2'''							,					beta_start=0.00_01							,					beta_end=0.02							,					)

				torch.manual_seed(0		)
				UpperCAmelCase   		=				IFWatermarker()

				return {
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "unet": unet,
				    "scheduler": scheduler,
				    "image_noising_scheduler": image_noising_scheduler,
				    "watermarker": watermarker,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''prompt''']
				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				if "image" in inputs:
							UpperCAmelCase   		=				inputs['''image''']
				else:
							UpperCAmelCase   		=				None

				if "mask_image" in inputs:
							UpperCAmelCase   		=				inputs['''mask_image''']
				else:
							UpperCAmelCase   		=				None

				if "original_image" in inputs:
							UpperCAmelCase   		=				inputs['''original_image''']
				else:
							UpperCAmelCase   		=				None

				UpperCAmelCase		,			UpperCAmelCase   		=				pipe.encode_prompt(_A		)

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				# set all optional components to None
				for optional_component in pipe._optional_components:
							setattr(_A							,					_A							,					_A		)

				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				for optional_component in pipe._optional_components:
							self.assertTrue(
							    getattr(_A							,					_A		) is None							,					F"""`{optional_component}` did not stay set to None after loading."""							,					)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)

				UpperCAmelCase   		=				inputs['''generator''']
				UpperCAmelCase   		=				inputs['''num_inference_steps''']
				UpperCAmelCase   		=				inputs['''output_type''']

				# inputs with prompt converted to embeddings
				UpperCAmelCase   		=				{
				    '''prompt_embeds''': prompt_embeds,
				    '''negative_prompt_embeds''': negative_prompt_embeds,
				    '''generator''': generator,
				    '''num_inference_steps''': num_inference_steps,
				    '''output_type''': output_type,
				}

				if image is not None:
							UpperCAmelCase   		=				image

				if mask_image is not None:
							UpperCAmelCase   		=				mask_image

				if original_image is not None:
							UpperCAmelCase   		=				original_image

				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_dummy_components()
				UpperCAmelCase   		=				self.pipeline_class(**_A		)
				pipe.to(_A		)
				pipe.set_progress_bar_config(disable=_A		)

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe(**_A		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(_A		)
							UpperCAmelCase   		=				self.pipeline_class.from_pretrained(_A		)
							pipe_loaded.to(_A		)
							pipe_loaded.set_progress_bar_config(disable=_A		)

				pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

				UpperCAmelCase   		=				self.get_dummy_inputs(_A		)
				UpperCAmelCase   		=				pipe_loaded(**_A		)[0]

				UpperCAmelCase   		=				np.abs(to_np(_A		) - to_np(_A		)		).max()
				self.assertLess(_A							,					1E-4		)



