






"""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       snake_case_       :



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

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

					torch.manual_seed(0						)
					A__							 =       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__							 =       DDPMScheduler(
					    num_train_timesteps=1000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.0001  ,					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						)
					A__							 =       IFWatermarker()

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

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

					torch.manual_seed(0						)
					A__							 =       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__							 =       DDPMScheduler(
					    num_train_timesteps=1000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.0001  ,					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						)
					A__							 =       DDPMScheduler(
					    num_train_timesteps=1000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.0001  ,					beta_end=0.02  ,					)

					torch.manual_seed(0						)
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inputs(__a						)

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

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

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

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

					A__ ,			A__							 =       pipe.encode_prompt(__a						)

					# inputs with prompt converted to embeddings
					A__							 =       {
					    '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__							 =       image

					if mask_image is not None:
									A__							 =       mask_image

					if original_image is not None:
									A__							 =       original_image

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

					A__							 =       pipe(**__a						)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(__a						)
									A__							 =       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.'''  ,					)

					A__							 =       self.get_dummy_inputs(__a						)

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

					# inputs with prompt converted to embeddings
					A__							 =       {
					    '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__							 =       image

					if mask_image is not None:
									A__							 =       mask_image

					if original_image is not None:
									A__							 =       original_image

					A__							 =       pipe_loaded(**__a						)[0]

					A__							 =       np.abs(to_np(__a						) - to_np(__a						)						).max()
					self.assertLess(__a  ,					1E-4						)






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe(**__a						)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(__a						)
									A__							 =       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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe_loaded(**__a						)[0]

					A__							 =       np.abs(to_np(__a						) - to_np(__a						)						).max()
					self.assertLess(__a  ,					1E-4						)










"""simple docstring"""






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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(
					    __a  ,					__a  ,					tokenizer_file=__a  ,					unk_token=__a  ,					bos_token=__a  ,					eos_token=__a  ,					add_prefix_space=__a  ,					**__a  ,					)

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

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


