
'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :int   =  StableUnCLIPPipeline
	snake_case__   :int   =  TEXT_TO_IMAGE_PARAMS
	snake_case__   :str   =  TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case__   :Optional[int]   =  TEXT_TO_IMAGE_IMAGE_PARAMS
	snake_case__   :str   =  TEXT_TO_IMAGE_IMAGE_PARAMS

	# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
	snake_case__   :Tuple   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				32
				lowerCAmelCase__										=				embedder_hidden_size

				# prior components

				torch.manual_seed(0				)
				lowerCAmelCase__										=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				CLIPTextModelWithProjection(
				    CLIPTextConfig(
				        bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__magic_name__   ,				projection_dim=__magic_name__   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				PriorTransformer(
				    num_attention_heads=2   ,				attention_head_dim=12   ,				embedding_dim=__magic_name__   ,				num_layers=1   ,				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				DDPMScheduler(
				    variance_type="fixed_small_log"   ,				prediction_type="sample"   ,				num_train_timesteps=1000   ,				clip_sample=__magic_name__   ,				clip_sample_range=5.0   ,				beta_schedule="squaredcos_cap_v2"   ,				)

				# regular denoising components

				torch.manual_seed(0				)
				lowerCAmelCase__										=				StableUnCLIPImageNormalizer(embedding_dim=__magic_name__				)
				lowerCAmelCase__										=				DDPMScheduler(beta_schedule="squaredcos_cap_v2"				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				CLIPTextModel(
				    CLIPTextConfig(
				        bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__magic_name__   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				UNetaDConditionModel(
				    sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")   ,				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type="projection"   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=__magic_name__   ,				layers_per_block=1   ,				upcast_attention=__magic_name__   ,				use_linear_projection=__magic_name__   ,				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				DDIMScheduler(
				    beta_schedule="scaled_linear"   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				prediction_type="v_prediction"   ,				set_alpha_to_one=__magic_name__   ,				steps_offset=1   ,				)

				torch.manual_seed(0				)
				lowerCAmelCase__										=				AutoencoderKL()

				lowerCAmelCase__										=				{
				    # prior components
				    "prior_tokenizer": prior_tokenizer,
				    "prior_text_encoder": prior_text_encoder,
				    "prior": prior,
				    "prior_scheduler": prior_scheduler,
				    # image noising components
				    "image_normalizer": image_normalizer,
				    "image_noising_scheduler": image_noising_scheduler,
				    # regular denoising components
				    "tokenizer": tokenizer,
				    "text_encoder": text_encoder,
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				}

				return components





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							List[Any]=0				):


				"""simple docstring"""

				if str(__magic_name__				).startswith("mps"				):
							lowerCAmelCase__										=				torch.manual_seed(__magic_name__				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
				lowerCAmelCase__										=				{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "prior_num_inference_steps": 2,
				    "output_type": "numpy",
				}
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				torch_device == "cpu"

				self._test_attention_slicing_forward_pass(test_max_difference=__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				torch_device in ["cpu", "mps"]

				self._test_inference_batch_single_identical(test_max_difference=__magic_name__				)






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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"				)

				lowerCAmelCase__										=				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,				torch_dtype=torch.floataa				)
				pipe.to(__magic_name__				)
				pipe.set_progress_bar_config(disable=__magic_name__				)
				# stable unclip will oom when integration tests are run on a V100,
				# so turn on memory savings
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				lowerCAmelCase__										=				torch.Generator(device="cpu"				).manual_seed(0				)
				lowerCAmelCase__										=				pipe("anime turle"   ,				generator=__magic_name__   ,				output_type="np"				)

				lowerCAmelCase__										=				output.images[0]

				assert image.shape == (768, 768, 3)

				assert_mean_pixel_difference(__magic_name__   ,				__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				lowerCAmelCase__										=				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,				torch_dtype=torch.floataa				)
				lowerCAmelCase__										=				pipe.to(__magic_name__				)
				pipe.set_progress_bar_config(disable=__magic_name__				)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				lowerCAmelCase__										=				pipe(
				    "anime turtle"   ,				prior_num_inference_steps=2   ,				num_inference_steps=2   ,				output_type="np"   ,				)

				lowerCAmelCase__										=				torch.cuda.max_memory_allocated()
				# make sure that less than 7 GB is allocated
				assert mem_bytes < 7 * 10**9













"""simple docstring"""



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase	:	str				=      logging.get_logger(__name__)


_lowerCAmelCase	:	int				=      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	List[Any]				=      {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase	:	List[str]				=      {
    """allenai/led-base-16384""": 16_384,
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =LEDTokenizer
   SCREAMING_SNAKE_CASE_    =['''input_ids''', '''attention_mask''']
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	str=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Dict=None							,			snake_case__      :	List[str]="replace"							,			snake_case__      :	Optional[int]="<s>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Union[str, Any]="</s>"							,			snake_case__      :	Dict="<s>"							,			snake_case__      :	Tuple="<unk>"							,			snake_case__      :	Any="<pad>"							,			snake_case__      :	Dict="<mask>"							,			snake_case__      :	int=False							,			snake_case__      :	Optional[int]=True							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			snake_case__							,			tokenizer_file=snake_case__							,			errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			unk_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			trim_offsets=snake_case__							,			**snake_case__							,			)

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

        UpperCAmelCase__						:    Dict							=							add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        UpperCAmelCase__						:    List[str]							=							"post_processor"
        UpperCAmelCase__						:    List[Any]							=							getattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
        if tokenizer_component_instance:
             UpperCAmelCase__						:    int							=							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:
                  UpperCAmelCase__						:    Optional[Any]							=							tuple(state["sep"]							)
             if "cls" in state:
                  UpperCAmelCase__						:    Any							=							tuple(state["cls"]							)

             UpperCAmelCase__						:    Any							=							False

             if state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
                  UpperCAmelCase__						:    Union[str, Any]							=							add_prefix_space
                  UpperCAmelCase__						:    List[Any]							=							True

             if state.get("trim_offsets"							,			snake_case__							) != trim_offsets:
                  UpperCAmelCase__						:    Optional[int]							=							trim_offsets
                  UpperCAmelCase__						:    List[Any]							=							True

             if changes_to_apply:
                  UpperCAmelCase__						:    List[str]							=							getattr(snake_case__							,			state.pop("type"							)							)
                  UpperCAmelCase__						:    Optional[int]							=							component_class(**snake_case__							)
                  setattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def       __a							(		self      :	Any							):


        '''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       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__							) if isinstance(snake_case__							,			snake_case__							) else value
        UpperCAmelCase__						:    Dict							=							value
   def       __a							(		self      :	str							,			*snake_case__      :	Any							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							kwargs.get("is_split_into_words"							,			snake_case__							)

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

        return super()._batch_encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Union[str, Any]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.get("is_split_into_words"							,			snake_case__							)

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

        return super()._encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	str=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
   def       __a							(		self      :	str							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							[self.sep_token_id]
        UpperCAmelCase__						:    Optional[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       __a							(		self      :	Any							,			snake_case__      :	Union[Dict[str, EncodedInput], BatchEncoding]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							super()._pad(
            encoded_inputs=snake_case__							,			max_length=snake_case__							,			padding_strategy=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_attention_mask=snake_case__							,			)

        # Load from model defaults
        if return_attention_mask is None:
             UpperCAmelCase__						:    Optional[int]							=							"attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase__						:    List[Any]							=							encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase__						:    Any							=							len(encoded_inputs["global_attention_mask"]							) != len(snake_case__							)

             if needs_to_be_padded:
                  UpperCAmelCase__						:    List[str]							=							len(snake_case__							) - len(encoded_inputs["global_attention_mask"]							)

                  if self.padding_side == "right":
                       # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                       UpperCAmelCase__						:    Dict							=							(
                           encoded_inputs["global_attention_mask"] + [-1] * difference
                       )
                  elif self.padding_side == "left":
                       UpperCAmelCase__						:    Dict							=							[-1] * difference + encoded_inputs[
                           "global_attention_mask"
                       ]
                  else:
                       raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

        return encoded_inputs


