




"""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 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





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


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   torch.manual_seed(0		)
   _snake_case							:		Tuple								=  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=a_,      activation_fn="""gelu-approximate""",      num_embeds_ada_norm=1_000,      norm_type="""ada_norm_zero""",      norm_elementwise_affine=a_,      )
   _snake_case							:		List[Any]								=  AutoencoderKL()
   _snake_case							:		Any								=  DDIMScheduler()
   _snake_case							:		Optional[Any]								=  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
   return components


 def  UpperCamelCase_   (				self:		Any,      a_:		Dict,      a_:		List[Any]=0		):




   '''simple docstring'''



   if str(a_		).startswith("""mps"""		):
     _snake_case							:		str								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		Dict								=  torch.Generator(device=a_		).manual_seed(a_		)
   _snake_case							:		Union[str, Any]								=  {
       """class_labels""": [1],
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }
   return inputs


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  """cpu"""

   _snake_case							:		str								=  self.get_dummy_components()
   _snake_case							:		Union[str, Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		int								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Any								=  pipe(**a_		).images
   _snake_case							:		Tuple								=  image[0, -3:, -3:, -1]

   self.assertEqual(image.shape,      (1, 16, 16, 3)		)
   _snake_case							:		List[str]								=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]		)
   _snake_case							:		Optional[Any]								=  np.abs(image_slice.flatten() - expected_slice		).max()
   self.assertLessEqual(a_,      1E-3		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   self._test_inference_batch_single_identical(relax_max_difference=a_,      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  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)







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







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  torch.manual_seed(0		)

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

   _snake_case							:		Optional[Any]								=  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
   _snake_case							:		List[str]								=  pipe.get_label_ids(a_		)

   _snake_case							:		List[str]								=  pipe(a_,      generator=a_,      num_inference_steps=40,      output_type="""np"""		).images

   for word, image in zip(a_,      a_		):
     _snake_case							:		Dict								=  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  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Dict								=  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""		)
   _snake_case							:		List[Any]								=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
   pipe.to("""cuda"""		)

   _snake_case							:		Dict								=  ["""vase""", """umbrella"""]
   _snake_case							:		List[str]								=  pipe.get_label_ids(a_		)

   _snake_case							:		Tuple								=  torch.manual_seed(0		)
   _snake_case							:		Dict								=  pipe(a_,      generator=a_,      num_inference_steps=25,      output_type="""np"""		).images

   for word, image in zip(a_,      a_		):
     _snake_case							:		int								=  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








"""simple docstring"""


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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A_						=				logging.get_logger(__name__)

A_						=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_						=				{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

A_						=				{
    '''Salesforce/codegen-350M-mono''': 20_48,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]
 lowercase__ 					=					CodeGenTokenizer


 def __init__(				self:		Union[str, Any],      a_:		List[Any]=None,      a_:		str=None,      a_:		str=None,      a_:		Dict="<|endoftext|>",      a_:		Tuple="<|endoftext|>",      a_:		str="<|endoftext|>",      a_:		List[Any]=False,      **a_:		List[str],      ):




   '''simple docstring'''



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

   if kwargs.pop("""add_bos_token""",      a_		):
     _snake_case							:		str								=  kwargs.pop("""name_or_path""",      """"""		)
     raise ValueError(
         """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
         """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
         f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
         f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
         """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
         """ so that the fast tokenizer works correctly."""		)

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

   _snake_case							:		List[Any]								=  add_prefix_space


 def  UpperCamelCase_   (				self:		Any,      *a_:		Any,      **a_:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  kwargs.get("""is_split_into_words""",      a_		)
   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(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      *a_:		Any,      **a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  kwargs.get("""is_split_into_words""",      a_		)

   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(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],      a_:		bool = False,      a_:		bool = None,      a_:		Optional[List[str]] = None,      **a_:		List[str],      ):




   '''simple docstring'''



   _snake_case							:		Any								=  super().decode(
       token_ids=a_,      skip_special_tokens=a_,      clean_up_tokenization_spaces=a_,      **a_,      )

   if truncate_before_pattern is not None and len(a_		) > 0:
     _snake_case							:		List[str]								=  self.truncate(a_,      a_		)

   return decoded_text


 def  UpperCamelCase_   (				self:		Dict,      a_:		Tuple,      a_:		Optional[Any]		):




   '''simple docstring'''



   def find_re(a_:		Dict,      a_:		str,      a_:		Union[str, Any]		):
     _snake_case							:		Any								=  pattern.search(a_,      a_		)
     return m.start() if m else -1

   _snake_case							:		Tuple								=  [re.compile(a_,      re.MULTILINE		) for pattern in truncate_before_pattern]

   _snake_case							:		List[Any]								=  list(re.finditer("""^print""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		int								=  completion[: prints[1].start()]

   _snake_case							:		List[str]								=  list(re.finditer("""^def""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		List[Any]								=  completion[: defs[1].start()]

   _snake_case							:		int								=  0

   _snake_case							:		List[Any]								=  [
       pos for pos in [find_re(a_,      a_,      a_		) for terminal in terminals] if pos != -1
   ]

   if len(a_		) > 0:
     return completion[: min(a_		)]
   else:
     return completion


