
'''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

__lowerCAmelCase												=	logging.get_logger(__name__)


__lowerCAmelCase												=	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCAmelCase												=	{
    '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'
    },
}

__lowerCAmelCase												=	{'facebook/blenderbot-3B': 128}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						["input_ids", "attention_mask"]
							lowerCAmelCase_ 						=						BlenderbotTokenizer

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase="replace"  ,  UpperCAmelCase="<s>"  ,  UpperCAmelCase="</s>"  ,  UpperCAmelCase="</s>"  ,  UpperCAmelCase="<s>"  ,  UpperCAmelCase="<unk>"  ,  UpperCAmelCase="<pad>"  ,  UpperCAmelCase="<mask>"  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  **UpperCAmelCase  ,  )					->       int:
														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  ,  )

														_snake_case       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""  ,  UpperCAmelCase  ) != add_prefix_space:
																					_snake_case       =  getattr(UpperCAmelCase  ,  pre_tok_state.pop("""type"""  )  )
																					_snake_case       =  add_prefix_space
																					_snake_case       =  pre_tok_class(**UpperCAmelCase  )

														_snake_case       =  add_prefix_space

														_snake_case       =  """post_processor"""
														_snake_case       =  getattr(self.backend_tokenizer  ,  UpperCAmelCase  ,  UpperCAmelCase  )
														if tokenizer_component_instance:
																					_snake_case       =  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:
																												_snake_case       =  tuple(state["""sep"""]  )
																					if "cls" in state:
																												_snake_case       =  tuple(state["""cls"""]  )

																					_snake_case       =  False

																					if state.get("""add_prefix_space"""  ,  UpperCAmelCase  ) != add_prefix_space:
																												_snake_case       =  add_prefix_space
																												_snake_case       =  True

																					if state.get("""trim_offsets"""  ,  UpperCAmelCase  ) != trim_offsets:
																												_snake_case       =  trim_offsets
																												_snake_case       =  True

																					if changes_to_apply:
																												_snake_case       =  getattr(UpperCAmelCase  ,  state.pop("""type"""  )  )
																												_snake_case       =  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  )					->       str:
														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  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else value
														_snake_case       =  value

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       BatchEncoding:
														_snake_case       =  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  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       BatchEncoding:
														_snake_case       =  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  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[str]:
														_snake_case       =  self._tokenizer.model.save(UpperCAmelCase  ,  name=UpperCAmelCase  )
														return tuple(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [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  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Dict:
														return token_ids_a + [self.eos_token_id]





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       List[int]:
														_snake_case       =  []
														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  )

														_snake_case       =  """  """.join(UpperCAmelCase  )
														_snake_case       =  self.encode(UpperCAmelCase  )
														if len(UpperCAmelCase  ) > self.model_max_length:
																					_snake_case       =  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

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

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

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1