

'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


__snake_case			:     List[str]										=      logging.get_logger(__name__)

__snake_case			:     Optional[Any]										=      {'tokenizer_file': 'tokenizer.json'}

__snake_case			:     Union[str, Any]										=      {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	VOCAB_FILES_NAMES
			__snake_case        =	PRETRAINED_VOCAB_FILES_MAP
			__snake_case        =	['input_ids', 'attention_mask']
			__snake_case        =	None


			def __init__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Union[str, Any]=None		,      lowerCAmelCase_	:						Union[str, Any]=None		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						Optional[int]="<unk>"		,      lowerCAmelCase_	:						Union[str, Any]="<s>"		,      lowerCAmelCase_	:						List[str]="</s>"		,      lowerCAmelCase_	:						Tuple="<pad>"		,      lowerCAmelCase_	:						Optional[int]=False		,      lowerCAmelCase_	:						List[Any]=False		,      **lowerCAmelCase_	:						str		,      )      ->		Tuple:





									'''simple docstring'''
									super().__init__(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      tokenizer_file=lowerCAmelCase_		,      unk_token=lowerCAmelCase_		,      bos_token=lowerCAmelCase_		,      eos_token=lowerCAmelCase_		,      pad_token=lowerCAmelCase_		,      add_prefix_space=lowerCAmelCase_		,      clean_up_tokenization_spaces=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									A__					:       Optional[Any]     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
									if pre_tok_state.get("""add_prefix_space"""		,      lowerCAmelCase_			) != add_prefix_space:
															A__					:       str     =getattr(lowerCAmelCase_		,      pre_tok_state.pop("""type"""			)			)
															A__					:       str     =add_prefix_space
															A__					:       Optional[Any]     =pre_tok_class(**lowerCAmelCase_			)

									A__					:       Any     =add_prefix_space


			def lowercase__						(				self	:						int		,      *lowerCAmelCase_	:						Tuple		,      **lowerCAmelCase_	:						List[str]			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Dict     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)
									if not (self.add_prefix_space or not is_split_into_words):
															raise Exception(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
															    """ pretokenized inputs."""			)

									return super()._batch_encode_plus(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      *lowerCAmelCase_	:						Optional[int]		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Union[str, Any]     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)

									if not (self.add_prefix_space or not is_split_into_words):
															raise Exception(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
															    """ pretokenized inputs."""			)

									return super()._encode_plus(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Optional[str] = None			)      ->		Tuple[str]:





									'''simple docstring'''
									A__					:       Dict     =self._tokenizer.model.save(lowerCAmelCase_		,      name=lowerCAmelCase_			)
									return tuple(lowerCAmelCase_			)



			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						"Conversation"			)      ->		List[int]:





									'''simple docstring'''
									A__					:       int     =[]
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_			) + [self.eos_token_id]			)

									if len(lowerCAmelCase_			) > self.model_max_length:
															A__					:       List[str]     =input_ids[-self.model_max_length :]
									return input_ids




'''simple docstring'''



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 lowerCamelCase			:



			'''simple docstring'''






			def lowercase__						(				self	:						Any			)      ->		Tuple:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       str     =TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									A__					:       int     =AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									A__					:       Union[str, Any]     =UNetaDConditionModel(
									    sample_size=32		,      layers_per_block=1		,      block_out_channels=[32, 64]		,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ]		,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,      in_channels=3		,      out_channels=6		,      cross_attention_dim=32		,      encoder_hid_dim=32		,      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			)
									A__					:       Dict     =DDPMScheduler(
									    num_train_timesteps=10_00		,      beta_schedule="""squaredcos_cap_v2"""		,      beta_start=0.0001		,      beta_end=0.02		,      thresholding=lowerCAmelCase_		,      dynamic_thresholding_ratio=0.95		,      sample_max_value=1.0		,      prediction_type="""epsilon"""		,      variance_type="""learned_range"""		,      )

									torch.manual_seed(0			)
									A__					:       Union[str, Any]     =IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}


			def lowercase__						(				self	:						Union[str, Any]			)      ->		int:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       List[Any]     =TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									A__					:       str     =AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""			)

									torch.manual_seed(0			)
									A__					:       Dict     =UNetaDConditionModel(
									    sample_size=32		,      layers_per_block=[1, 2]		,      block_out_channels=[32, 64]		,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ]		,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,      in_channels=6		,      out_channels=6		,      cross_attention_dim=32		,      encoder_hid_dim=32		,      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=1.414		,      time_embedding_act_fn="""gelu"""		,      time_embedding_dim=32		,      )
									unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									torch.manual_seed(0			)
									A__					:       Optional[int]     =DDPMScheduler(
									    num_train_timesteps=10_00		,      beta_schedule="""squaredcos_cap_v2"""		,      beta_start=0.0001		,      beta_end=0.02		,      thresholding=lowerCAmelCase_		,      dynamic_thresholding_ratio=0.95		,      sample_max_value=1.0		,      prediction_type="""epsilon"""		,      variance_type="""learned_range"""		,      )

									torch.manual_seed(0			)
									A__					:       int     =DDPMScheduler(
									    num_train_timesteps=10_00		,      beta_schedule="""squaredcos_cap_v2"""		,      beta_start=0.0001		,      beta_end=0.02		,      )

									torch.manual_seed(0			)
									A__					:       List[str]     =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	:						str			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Tuple     =self.get_dummy_components()
									A__					:       str     =self.pipeline_class(**lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Optional[int]     =self.get_dummy_inputs(lowerCAmelCase_			)

									A__					:       str     =inputs["""prompt"""]
									A__					:       Optional[int]     =inputs["""generator"""]
									A__					:       Optional[Any]     =inputs["""num_inference_steps"""]
									A__					:       Union[str, Any]     =inputs["""output_type"""]

									if "image" in inputs:
															A__					:       Union[str, Any]     =inputs["""image"""]
									else:
															A__					:       List[Any]     =None

									if "mask_image" in inputs:
															A__					:       Union[str, Any]     =inputs["""mask_image"""]
									else:
															A__					:       Tuple     =None

									if "original_image" in inputs:
															A__					:       Optional[Any]     =inputs["""original_image"""]
									else:
															A__					:       Tuple     =None

									A__   ,						A__					:       Optional[Any]     =pipe.encode_prompt(lowerCAmelCase_			)

									# inputs with prompt converted to embeddings
									A__					:       Optional[int]     ={
									    """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:
															A__					:       int     =image

									if mask_image is not None:
															A__					:       Tuple     =mask_image

									if original_image is not None:
															A__					:       Optional[int]     =original_image

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

									A__					:       Any     =pipe(**lowerCAmelCase_			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(lowerCAmelCase_			)
															A__					:       int     =self.pipeline_class.from_pretrained(lowerCAmelCase_			)
															pipe_loaded.to(lowerCAmelCase_			)
															pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_			)

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

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

									A__					:       Dict     =self.get_dummy_inputs(lowerCAmelCase_			)

									A__					:       int     =inputs["""generator"""]
									A__					:       str     =inputs["""num_inference_steps"""]
									A__					:       Optional[Any]     =inputs["""output_type"""]

									# inputs with prompt converted to embeddings
									A__					:       List[Any]     ={
									    """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:
															A__					:       int     =image

									if mask_image is not None:
															A__					:       int     =mask_image

									if original_image is not None:
															A__					:       Optional[int]     =original_image

									A__					:       List[str]     =pipe_loaded(**lowerCAmelCase_			)[0]

									A__					:       Union[str, Any]     =np.abs(to_np(lowerCAmelCase_			) - to_np(lowerCAmelCase_			)			).max()
									self.assertLess(lowerCAmelCase_		,      1e-4			)



			def lowercase__						(				self	:						List[str]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.get_dummy_components()
									A__					:       int     =self.pipeline_class(**lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       int     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       List[Any]     =pipe(**lowerCAmelCase_			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(lowerCAmelCase_			)
															A__					:       List[Any]     =self.pipeline_class.from_pretrained(lowerCAmelCase_			)
															pipe_loaded.to(lowerCAmelCase_			)
															pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_			)

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

									A__					:       int     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Tuple     =pipe_loaded(**lowerCAmelCase_			)[0]

									A__					:       Tuple     =np.abs(to_np(lowerCAmelCase_			) - to_np(lowerCAmelCase_			)			).max()
									self.assertLess(lowerCAmelCase_		,      1e-4			)

