

"""simple docstring"""


import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  LDMTextToImagePipeline
				SCREAMING_SNAKE_CASE_   :      Dict         =  TEXT_TO_IMAGE_PARAMS - {
				    """negative_prompt""",
				    """negative_prompt_embeds""",
				    """cross_attention_kwargs""",
				    """prompt_embeds""",
				}
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  PipelineTesterMixin.required_optional_params - {
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				SCREAMING_SNAKE_CASE_   :      Any         =  TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_   :      int         =  False

				def 							lowercase_     (	self							:				int)->   str:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:       Any          =			UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)     ,  layers_per_block=2     ,  sample_size=3_2     ,  in_channels=4     ,  out_channels=4     ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,  cross_attention_dim=3_2     ,  )
						__lowerCAmelCase:       List[Any]          =			DDIMScheduler(
						    beta_start=0.00085     ,  beta_end=0.012     ,  beta_schedule="scaled_linear"     ,  clip_sample=UpperCamelCase__     ,  set_alpha_to_one=UpperCamelCase__     ,  )
						torch.manual_seed(0)
						__lowerCAmelCase:       Optional[int]          =			AutoencoderKL(
						    block_out_channels=(3_2, 6_4)     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")     ,  up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")     ,  latent_channels=4     ,  )
						torch.manual_seed(0)
						__lowerCAmelCase:       Dict          =			CLIPTextConfig(
						    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=3_2     ,  intermediate_size=3_7     ,  layer_norm_eps=1e-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1_0_0_0     ,  )
						__lowerCAmelCase:       Any          =			CLIPTextModel(UpperCamelCase__)
						__lowerCAmelCase:       str          =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__lowerCAmelCase:       Optional[int]          =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Optional[int]=0)->   Optional[Any]:



						'''simple docstring'''


						if str(UpperCamelCase__).startswith("mps"):
								__lowerCAmelCase:       int          =			torch.manual_seed(UpperCamelCase__)
						else:
								__lowerCAmelCase:       Optional[int]          =			torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
						__lowerCAmelCase:       int          =			{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			"cpu"  # ensure determinism for the device-dependent torch.Generator

						__lowerCAmelCase:       List[str]          =			self.get_dummy_components()
						__lowerCAmelCase:       List[str]          =			LDMTextToImagePipeline(**UpperCamelCase__)
						pipe.to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			self.get_dummy_inputs(UpperCamelCase__)
						__lowerCAmelCase:       Any          =			pipe(**UpperCamelCase__).images
						__lowerCAmelCase:       List[str]          =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_6, 1_6, 3)
						__lowerCAmelCase:       Optional[int]          =			np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3





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

				def 							lowercase_     (	self							:				Optional[int])->   int:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Optional[int]=torch.floataa     ,  UpperCamelCase__							:				Optional[Any]=0)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:       Union[str, Any]          =			np.random.RandomState(UpperCamelCase__).standard_normal((1, 4, 3_2, 3_2))
						__lowerCAmelCase:       Optional[int]          =			torch.from_numpy(UpperCamelCase__).to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




				def 							lowercase_     (	self							:				List[str])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			self.get_inputs(UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			pipe(**UpperCamelCase__).images
						__lowerCAmelCase:       int          =			image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						__lowerCAmelCase:       Union[str, Any]          =			np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__lowerCAmelCase:       List[Any]          =			np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3





@nightly
@require_torch_gpu
class     snake_case	(				unittest.TestCase  ):

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



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				int=torch.floataa     ,  UpperCamelCase__							:				Optional[int]=0)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			torch.manual_seed(UpperCamelCase__)
						__lowerCAmelCase:       str          =			np.random.RandomState(UpperCamelCase__).standard_normal((1, 4, 3_2, 3_2))
						__lowerCAmelCase:       List[Any]          =			torch.from_numpy(UpperCamelCase__).to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 5_0,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs




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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase__)
						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			self.get_inputs(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			pipe(**UpperCamelCase__).images[0]

						__lowerCAmelCase:       Dict          =			load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__lowerCAmelCase:       Union[str, Any]          =			np.abs(expected_image - image).max()
						assert max_diff < 1e-3





"""simple docstring"""


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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

__A						   =			logging.get_logger(__name__)


__A						   =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A						   =			{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__A						   =			{"facebook/blenderbot-3B": 128}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

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



						'''simple docstring'''


						super().__init__(
						    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  errors=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Any          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       int          =			add_prefix_space
								__lowerCAmelCase:       str          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       List[Any]          =			json.loads(tokenizer_component_instance.__getstate__())

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
										__lowerCAmelCase:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Dict          =			add_prefix_space
										__lowerCAmelCase:       Optional[Any]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

								if changes_to_apply:
										__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       str          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							lowercase_     (	self							:				Optional[Any])->   str:



						'''simple docstring'''


						if self._mask_token is None:
								if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
								return None
						return str(self._mask_token)

				@mask_token.setter
				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

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

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

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

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

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[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 + sep + token_ids_a + sep) * [0]

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[]
						for is_user, text in conversation.iter_texts():
								if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(" " + text)
								else:
										# Generated responses should contain them already.
										inputs.append(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       int          =			input_ids[-self.model_max_length :]
								logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.")
						return input_ids


