





from dataclasses import dataclass, field
from typing import Optional







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Model name or path of model to be trained."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""./"""		,metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot-clean-train"""		,metadata={"""help""": """Name or path of training dataset."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot-clean-valid"""		,metadata={"""help""": """Name or path of validation dataset."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=2		,metadata={"""help""": """Batch size for training."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=2		,metadata={"""help""": """Batch size for evaluation."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(default=0.1		,metadata={"""help""": """Value of weight decay."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=10000		,metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(default=2E-4		,metadata={"""help""": """Learning rate fo training."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default="""cosine"""		,metadata={"""help""": """Learning rate."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=750		,metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=16		,metadata={"""help""": """Number of gradient accumulation steps."""}  )
   _UpperCamelCase :						Optional[bool]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=50000		,metadata={"""help""": """Maximum number of training steps."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=-1		,metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=1024		,metadata={"""help""": """Sequence lengths used for training."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=1		,metadata={"""help""": """Training seed."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=1024		,metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}		,)
   _UpperCamelCase :						Optional[str]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
   _UpperCamelCase :						Optional[bool]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """If True the data is pretokenized."""}  )







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Model name or path of model to be evaluated."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot-clean-valid"""		,metadata={"""help""": """Name or path of validation dataset."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=2		,metadata={"""help""": """Batch size used for evaluation."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=-1		,metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=1024		,metadata={"""help""": """Length of sequences to be evaluated."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=1		,metadata={"""help""": """Random seed used for evaluation."""}  )







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Model name or path of model to be evaluated."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Number of workers used for code evaluation."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}		,)
   _UpperCamelCase :						Optional[bool]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """Sample from the language model's output distribution."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(default=0.2		,metadata={"""help""": """Sampling temperature used for generation."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=256		,metadata={"""help""": """Maximum number of newly generated tokens."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=0		,metadata={"""help""": """Top-k parameter used for generation."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(default=0.95		,metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=10		,metadata={"""help""": """Number of generations to run in parallel."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=200		,metadata={"""help""": """Number of completions to generate for each sample."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=1		,metadata={"""help""": """Random seed used for evaluation."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""eval_results.json"""		,metadata={"""help""": """Random seed used for evaluation."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""0"""		,metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=-1		,metadata={
           """help""": (
               """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
               """ number corresponds to which GPU device id to run on."""
           )
       }		,)







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[int]     		=				field(
       default=_UpperCamelCase		,metadata={
           """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
       }		,)
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""transformersbook/codeparrot"""		,metadata={"""help""": """Folder or name of dataset to process."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot-clean"""		,metadata={"""help""": """Folder to save processed processed dataset."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=100000		,metadata={"""help""": """Number of files to save per JSON output file."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default="""content"""		,metadata={"""help""": """Column containing text data to process."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=1000		,metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=100		,metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=0.25		,metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=1.5		,metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=0.7		,metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Name or path to the tokenizer."""}		,)
   _UpperCamelCase :						Optional[bool]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
   _UpperCamelCase :						Optional[float]     		=				field(
       default=0.85		,metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""gpt2"""		,metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""transformersbook/codeparrot-train"""		,metadata={"""help""": """Dataset to train tokenizer on."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default="""content"""		,metadata={"""help""": """Column containing text data to process."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=200000		,metadata={"""help""": """Number of examples to train tokenizer on."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(
       default=32768		,metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default="""codeparrot"""		,metadata={"""help""": """Name of new tokenizer."""}  )
   _UpperCamelCase :						Optional[bool]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Push saved tokenizer to the hub."""}  )







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Name or path to the tokenizer."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot-clean-train"""		,metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""tokenized-codeparrot-train"""		,metadata={"""help""": """Repo name of the pretokenized data."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Number of workers used for code evaluation."""}  )







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""gpt2-large"""		,metadata={"""help""": """Configuration to use for model initialization."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""codeparrot/codeparrot"""		,metadata={"""help""": """Tokenizer attached to model."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default="""codeparrot"""		,metadata={"""help""": """Name of the created model."""}  )
   _UpperCamelCase :						Optional[bool]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Push saved tokenizer to the hub."""}  )








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   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				StableUnCLIPPipeline
   _UpperCamelCase :						Optional[int]     		=				TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase :						List[Any]     		=				TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_IMAGE_PARAMS

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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  32
      a__	:						int      		=  embedder_hidden_size

      # prior components

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

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=snake_case					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						int      		=  PriorTransformer(
          num_attention_heads=2					,			attention_head_dim=12					,			embedding_dim=snake_case					,			num_layers=1					,			)

      torch.manual_seed(0      )
      a__	:						str      		=  DDPMScheduler(
          variance_type="fixed_small_log"					,			prediction_type="sample"					,			num_train_timesteps=1_000					,			clip_sample=snake_case					,			clip_sample_range=5.0					,			beta_schedule="squaredcos_cap_v2"					,			)

      # regular denoising components

      torch.manual_seed(0      )
      a__	:						Any      		=  StableUnCLIPImageNormalizer(embedding_dim=snake_case      )
      a__	:						int      		=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Union[str, Any]      		=  CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			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=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						Any      		=  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=snake_case					,			layers_per_block=1					,			upcast_attention=snake_case					,			use_linear_projection=snake_case					,			)

      torch.manual_seed(0      )
      a__	:						Tuple      		=  DDIMScheduler(
          beta_schedule="scaled_linear"					,			beta_start=0.00_085					,			beta_end=0.012					,			prediction_type="v_prediction"					,			set_alpha_to_one=snake_case					,			steps_offset=1					,			)

      torch.manual_seed(0      )
      a__	:						Optional[int]      		=  AutoencoderKL()

      a__	:						Any      		=  {
          # 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 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Dict:
      """simple docstring"""





      if str(snake_case      ).startswith("mps"      ):
         a__	:						Union[str, Any]      		=  torch.manual_seed(snake_case      )
      else:
         a__	:						List[str]      		=  torch.Generator(device=snake_case      ).manual_seed(snake_case      )
      a__	:						Any      		=  {
          "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 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=snake_case      )







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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





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

      a__	:						int      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      # 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()

      a__	:						Dict      		=  torch.Generator(device="cpu"      ).manual_seed(0      )
      a__	:						Dict      		=  pipe("anime turle"					,			generator=snake_case					,			output_type="np"      )

      a__	:						List[str]      		=  output.images[0]

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

      assert_mean_pixel_difference(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





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

      a__	:						str      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      a__	:						Union[str, Any]      		=  pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Union[str, Any]      		=  pipe(
          "anime turtle"					,			prior_num_inference_steps=2					,			num_inference_steps=2					,			output_type="np"					,			)

      a__	:						List[Any]      		=  torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9

