
"""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		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     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				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	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     ,					)
      __UpperCamelCase      =	AutoencoderKL()
      __UpperCamelCase      =	DDIMScheduler()
      __UpperCamelCase      =	{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      if str(__UpperCAmelCase				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      __UpperCamelCase      =	{
          'class_labels': [1],
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'cpu'

      __UpperCamelCase      =	self.get_dummy_components()
      __UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

      self.assertEqual(image.shape     ,					(1, 16, 16, 3)				)
      __UpperCamelCase      =	np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]				)
      __UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
      self.assertLessEqual(__UpperCAmelCase     ,					1E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)


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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch.manual_seed(0				)

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

      __UpperCamelCase      =	['vase', 'umbrella', 'white shark', 'white wolf']
      __UpperCamelCase      =	pipe.get_label_ids(__UpperCAmelCase				)

      __UpperCamelCase      =	pipe(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=40     ,					output_type='np'				).images

      for word, image in zip(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	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				):

      '''simple docstring'''




      __UpperCamelCase      =	DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'				)
      __UpperCamelCase      =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
      pipe.to('cuda'				)

      __UpperCamelCase      =	['vase', 'umbrella']
      __UpperCamelCase      =	pipe.get_label_ids(__UpperCAmelCase				)

      __UpperCamelCase      =	torch.manual_seed(0				)
      __UpperCamelCase      =	pipe(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=25     ,					output_type='np'				).images

      for word, image in zip(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	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


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[str]       =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    List[Any]       =			{
    "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"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						CodeGenTokenizer





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

      '''simple docstring'''




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

      if kwargs.pop('add_bos_token'     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	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.'				)

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

      __UpperCamelCase      =	add_prefix_space





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	super().decode(
          token_ids=__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase     ,					clean_up_tokenization_spaces=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if truncate_before_pattern is not None and len(__UpperCAmelCase				) > 0:
       __UpperCamelCase      =	self.truncate(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return decoded_text







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      def find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	pattern.search(__UpperCAmelCase     ,					__UpperCAmelCase				)
       return m.start() if m else -1

      __UpperCamelCase      =	[re.compile(__UpperCAmelCase     ,					re.MULTILINE				) for pattern in truncate_before_pattern]

      __UpperCamelCase      =	list(re.finditer('^print'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: prints[1].start()]

      __UpperCamelCase      =	list(re.finditer('^def'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: defs[1].start()]

      __UpperCamelCase      =	0

      __UpperCamelCase      =	[
          pos for pos in [find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				) for terminal in terminals] if pos != -1
      ]

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



