



'''simple docstring'''






import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowerCamelCase__	  =		logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase__	  =		'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = \"a shark\"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")\n        ```\n'

@dataclass
class 	_lowerCAmelCase     (    lowercase_       ):






		'''simple docstring'''
		snake_case_							= 42




class 	_lowerCAmelCase     (    lowercase_       ):






		'''simple docstring'''
		def __init__(						self							:       Any      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       int      ,		UpperCamelCase_							:       Tuple      ,		)      ->			Tuple:







									'''simple docstring'''





									super().__init__()

									self.register_modules(
									    prior=UpperCAmelCase_      ,		text_encoder=UpperCAmelCase_      ,		tokenizer=UpperCAmelCase_      ,		scheduler=UpperCAmelCase_      ,		renderer=UpperCAmelCase_      ,		)
		def 						__lowercase      (						self							:       Tuple      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       int      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       List[str]      ,		UpperCamelCase_							:       Union[str, Any]      ,		UpperCamelCase_							:       Union[str, Any]		)      ->			Tuple:







									'''simple docstring'''





									if latents is None:
																_lowercase							:		int				=   randn_tensor(UpperCAmelCase_      ,		generator=UpperCAmelCase_      ,		device=UpperCAmelCase_      ,		dtype=UpperCAmelCase_		)
									else:
																if latents.shape != shape:
																							raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
																_lowercase							:		str				=   latents.to(UpperCAmelCase_		)

									_lowercase							:		Optional[int]				=   latents * scheduler.init_noise_sigma
									return latents
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       Union[str, Any]=0		)      ->			Tuple:







									'''simple docstring'''





									if is_accelerate_available():
																from accelerate import cpu_offload
									else:
																raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

									_lowercase							:		Tuple				=   torch.device(F"cuda:{gpu_id}"		)

									_lowercase							:		List[str]				=   [self.text_encoder, self.prior]
									for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																							cpu_offload(UpperCAmelCase_      ,		UpperCAmelCase_		)
		def 						__lowercase      (						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Tuple=0		)      ->			List[str]:







									'''simple docstring'''





									if is_accelerate_available() and is_accelerate_version('''>='''      ,		'''0.17.0.dev0'''		):
																from accelerate import cpu_offload_with_hook
									else:
																raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''		)

									_lowercase							:		Tuple				=   torch.device(F"cuda:{gpu_id}"		)

									if self.device.type != "cpu":
																self.to('''cpu'''      ,		silence_dtype_warnings=UpperCAmelCase_		)
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									_lowercase							:		Union[str, Any]				=   None
									for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_lowercase							:		Tuple				=   cpu_offload_with_hook(UpperCAmelCase_      ,		UpperCAmelCase_      ,		prev_module_hook=UpperCAmelCase_		)

									if self.safety_checker is not None:
																_lowercase							:		str				=   cpu_offload_with_hook(self.safety_checker      ,		UpperCAmelCase_      ,		prev_module_hook=UpperCAmelCase_		)

									# We'll offload the last model manually.
									_lowercase							:		Dict				=   hook
		@property
		def 						__lowercase      (						self							:       List[Any]		)      ->			Dict:







									'''simple docstring'''





									if self.device != torch.device('''meta'''		) or not hasattr(self.text_encoder      ,		'''_hf_hook'''		):
																return self.device
									for module in self.text_encoder.modules():
																if (
																    hasattr(UpperCAmelCase_      ,		'''_hf_hook'''		)
																    and hasattr(module._hf_hook      ,		'''execution_device'''		)
																    and module._hf_hook.execution_device is not None
																):
																							return torch.device(module._hf_hook.execution_device		)
									return self.device
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       Dict      ,		UpperCamelCase_							:       Dict      ,		UpperCamelCase_							:       Optional[int]      ,		)      ->			Dict:







									'''simple docstring'''





									len(UpperCAmelCase_		) if isinstance(UpperCAmelCase_      ,		UpperCAmelCase_		) else 1

									# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
									_lowercase							:		Optional[Any]				=   0
									# get prompt text embeddings
									_lowercase							:		List[Any]				=   self.tokenizer(
									    UpperCAmelCase_      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCAmelCase_      ,		return_tensors='''pt'''      ,		)
									_lowercase							:		Optional[int]				=   text_inputs.input_ids
									_lowercase							:		List[Any]				=   self.tokenizer(UpperCAmelCase_      ,		padding='''longest'''      ,		return_tensors='''pt'''		).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCAmelCase_      ,		UpperCAmelCase_		):
																_lowercase							:		Union[str, Any]				=   self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]		)
																logger.warning(
																    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																    F" {self.tokenizer.model_max_length} tokens: {removed_text}"		)

									_lowercase							:		Union[str, Any]				=   self.text_encoder(text_input_ids.to(UpperCAmelCase_		)		)
									_lowercase							:		str				=   text_encoder_output.text_embeds

									_lowercase							:		Tuple				=   prompt_embeds.repeat_interleave(UpperCAmelCase_      ,		dim=0		)
									# in Shap-E it normalize the prompt_embeds and then later rescale it
									_lowercase							:		Any				=   prompt_embeds / torch.linalg.norm(UpperCAmelCase_      ,		dim=-1      ,		keepdim=UpperCAmelCase_		)

									if do_classifier_free_guidance:
																_lowercase							:		str				=   torch.zeros_like(UpperCAmelCase_		)

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																_lowercase							:		List[Any]				=   torch.cat([negative_prompt_embeds, prompt_embeds]		)

									# Rescale the features to have unit variance
									_lowercase							:		str				=   math.sqrt(prompt_embeds.shape[1]		) * prompt_embeds

									return prompt_embeds





		@torch.no_grad()
		@replace_example_docstring(UpperCAmelCase_		)
		def __call__(						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Tuple      ,		UpperCamelCase_							:       List[Any] = 1      ,		UpperCamelCase_							:       Optional[Any] = 25      ,		UpperCamelCase_							:       Optional[Any] = None      ,		UpperCamelCase_							:       Optional[int] = None      ,		UpperCamelCase_							:       Tuple = 4.0      ,		UpperCamelCase_							:       int = 64      ,		UpperCamelCase_							:       Optional[int] = "pil"      ,		UpperCamelCase_							:       Union[str, Any] = True      ,		)      ->			Union[str, Any]:







									'''simple docstring'''





									if isinstance(UpperCAmelCase_      ,		UpperCAmelCase_		):
																_lowercase							:		List[str]				=   1
									elif isinstance(UpperCAmelCase_      ,		UpperCAmelCase_		):
																_lowercase							:		List[str]				=   len(UpperCAmelCase_		)
									else:
																raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_		)}"		)

									_lowercase							:		Tuple				=   self._execution_device

									_lowercase							:		int				=   batch_size * num_images_per_prompt

									_lowercase							:		List[Any]				=   guidance_scale > 1.0
									_lowercase							:		str				=   self._encode_prompt(UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_		)

									# prior

									self.scheduler.set_timesteps(UpperCAmelCase_      ,		device=UpperCAmelCase_		)
									_lowercase							:		Optional[int]				=   self.scheduler.timesteps

									_lowercase							:		Optional[Any]				=   self.prior.config.num_embeddings
									_lowercase							:		List[Any]				=   self.prior.config.embedding_dim

									_lowercase							:		Optional[Any]				=   self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)      ,		prompt_embeds.dtype      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		self.scheduler      ,		)

									# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
									_lowercase							:		Tuple				=   latents.reshape(latents.shape[0]      ,		UpperCAmelCase_      ,		UpperCAmelCase_		)

									for i, t in enumerate(self.progress_bar(UpperCAmelCase_		)		):
																# expand the latents if we are doing classifier free guidance
																_lowercase							:		Optional[Any]				=   torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
																_lowercase							:		Dict				=   self.scheduler.scale_model_input(UpperCAmelCase_      ,		UpperCAmelCase_		)

																_lowercase							:		int				=   self.prior(
																    UpperCAmelCase_      ,		timestep=UpperCAmelCase_      ,		proj_embedding=UpperCAmelCase_      ,		).predicted_image_embedding

																# remove the variance
																_lowercase							:		List[str]				=   noise_pred.split(
																    scaled_model_input.shape[2]      ,		dim=2		)  # batch_size, num_embeddings, embedding_dim

																if do_classifier_free_guidance is not None:
																							_lowercase							:		List[str]				=   noise_pred.chunk(2		)
																							_lowercase							:		Any				=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																_lowercase							:		List[Any]				=   self.scheduler.step(
																    UpperCAmelCase_      ,		timestep=UpperCAmelCase_      ,		sample=UpperCAmelCase_      ,		).prev_sample

									if output_type == "latent":
																return ShapEPipelineOutput(images=UpperCAmelCase_		)

									_lowercase							:		int				=   []
									for i, latent in enumerate(UpperCAmelCase_		):
																_lowercase							:		Dict				=   self.renderer.decode(
																    latent[None, :]      ,		UpperCAmelCase_      ,		size=UpperCAmelCase_      ,		ray_batch_size=4_096      ,		n_coarse_samples=64      ,		n_fine_samples=128      ,		)
																images.append(UpperCAmelCase_		)

									_lowercase							:		Tuple				=   torch.stack(UpperCAmelCase_		)

									if output_type not in ["np", "pil"]:
																raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}"		)

									_lowercase							:		List[str]				=   images.cpu().numpy()

									if output_type == "pil":
																_lowercase							:		Optional[int]				=   [self.numpy_to_pil(UpperCAmelCase_		) for image in images]

									# Offload last model to CPU
									if hasattr(self      ,		'''final_offload_hook'''		) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

									if not return_dict:
																return (images,)

									return ShapEPipelineOutput(images=UpperCAmelCase_		)











"""simple docstring"""





import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							List[str]         =     foo





   def __call__(       self)			->   Union[str, Any]:




          '''simple docstring'''




          return self.foo







class 						_a  (		datasets.Dataset       ):





   '''simple docstring'''






   @fingerprint_transform(inplace=UpperCAmelCase_)
   def __lowercase							(       self					, UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




          return DatasetChild(self.data					, fingerprint=UpperCAmelCase_)





   @fingerprint_transform(inplace=UpperCAmelCase_)
   def __lowercase							(       self					, UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




          return DatasetChild(self.data					, fingerprint=UpperCAmelCase_)







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          lowercase__:							Tuple         =     callable





   def __call__(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          if self.callable is not None:
                 return self.callable(*UpperCAmelCase_					, **UpperCAmelCase_)





   def __getstate__(       self)			->   int:




          '''simple docstring'''




          raise pickle.PicklingError()







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @require_transformers
   @pytest.mark.integration
   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          from transformers import AutoTokenizer

          def encode(UpperCAmelCase_):
                 return tokenizer(UpperCAmelCase_)

          # TODO: add hash consistency tests across sessions
          lowercase__:							Tuple         =     AutoTokenizer.from_pretrained("bert-base-uncased")
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase_: tokenizer(UpperCAmelCase_))).hexdigest()
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Tuple         =     AutoTokenizer.from_pretrained("bert-base-cased")
          lowercase__:							int         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase_: tokenizer(UpperCAmelCase_))).hexdigest()
          lowercase__:							List[str]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Optional[int]         =     AutoTokenizer.from_pretrained("bert-base-uncased")
          lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							str         =     mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase_: tokenizer(UpperCAmelCase_))).hexdigest()
          lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   @require_transformers
   @pytest.mark.integration
   def __lowercase							(       self)			->   Optional[int]:




          '''simple docstring'''




          from transformers import AutoTokenizer

          lowercase__:							Optional[int]         =     AutoTokenizer.from_pretrained("gpt2")
          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          tokenizer("Hello world !")  # call once to change the tokenizer's cache
          lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)





   @require_regex
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          import regex

          lowercase__:							List[Any]         =     regex.Regex("foo")
          lowercase__:							Optional[int]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							List[Any]         =     regex.Regex("bar")
          lowercase__:							List[str]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Union[str, Any]         =     regex.Regex("foo")
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          def func():
                 return foo

          lowercase__:							int         =     [0]
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Dict         =     [1]
          lowercase__:							int         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Union[str, Any]         =     [0]
          lowercase__:							List[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          def func():
                 pass

          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()

          def func():
                 pass

          lowercase__:							List[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     mda(datasets.utils.py_utils.dumps(Foo([0]))).hexdigest()
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(Foo([1]))).hexdigest()
          lowercase__:							int         =     mda(datasets.utils.py_utils.dumps(Foo([0]))).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(Foo([0]).__call__)).hexdigest()
          lowercase__:							List[Any]         =     mda(datasets.utils.py_utils.dumps(Foo([1]).__call__)).hexdigest()
          lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(Foo([0]).__call__)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							List[Any]         =     (
              "co_argcount",
              "co_kwonlyargcount",
              "co_nlocals",
              "co_stacksize",
              "co_flags",
              "co_code",
              "co_consts",
              "co_names",
              "co_varnames",
              "co_filename",
              "co_name",
              "co_firstlineno",
              "co_lnotab",
              "co_freevars",
              "co_cellvars",
          )

          def _create_code_pyaa(*UpperCAmelCase_):
                 if hasattr(args[-3]					, "encode"):
                        lowercase__:							Tuple         =     list(UpperCAmelCase_)
                        lowercase__:							str         =     args[-3].encode()  # co_lnotab
                        lowercase__:							Any         =     args[-10].encode()  # co_code
                 if hasattr(UpperCAmelCase_					, "co_posonlyargcount"):
                        if len(UpperCAmelCase_) == 16:
                               return CodeType(*UpperCAmelCase_)
                        elif len(UpperCAmelCase_) == 15:
                               return CodeType(args[0]					, 0					, *args[1:])
                        return CodeType(args[0]					, 0					, 0					, *args[1:])
                 elif hasattr(UpperCAmelCase_					, "co_kwonlyargcount"):
                        if len(UpperCAmelCase_) == 16:
                               return CodeType(args[0]					, *args[2:])
                        elif len(UpperCAmelCase_) == 15:
                               return CodeType(*UpperCAmelCase_)
                        return CodeType(args[0]					, 0					, *args[1:])
                 if len(UpperCAmelCase_) == 16:
                        return CodeType(args[0]					, *args[3:])
                 elif len(UpperCAmelCase_) == 15:
                        return CodeType(args[0]					, *args[2:])
                 return CodeType(*UpperCAmelCase_)

          def create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_):
                 def func():
                        return returned_obj

                 lowercase__:							Dict         =     func.__code__
                 # Use _create_code from dill in order to make it work for different python versions
                 if datasets.config.PY_VERSION < version.parse("3.8"):
                        lowercase__:							Union[str, Any]         =     _create_code_pyaa(
                            *[getattr(UpperCAmelCase_					, UpperCAmelCase_) if k != "co_filename" else co_filename for k in code_args_pyaa])
                 else:
                        lowercase__:							Tuple         =     code.replace(co_filename=UpperCAmelCase_)
                 return FunctionType(UpperCAmelCase_					, func.__globals__					, func.__name__					, func.__defaults__					, func.__closure__)

          lowercase__    ,      lowercase__:							Any         =     "<ipython-input-2-e0383a102aae>", [0]
          lowercase__:							List[str]         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          lowercase__    ,      lowercase__:							Optional[Any]         =     "<ipython-input-2-e0383a102aae>", [1]
          lowercase__:							Optional[int]         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          lowercase__    ,      lowercase__:							Tuple         =     "<ipython-input-5-713f6613acf3>", [0]
          lowercase__:							List[str]         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)

          lowercase__    ,      lowercase__:							Dict         =     os.path.join(gettempdir()					, "ipykernel_12345"					, "321456789.py"), [0]
          lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          lowercase__    ,      lowercase__:							Dict         =     os.path.join(gettempdir()					, "ipykernel_12345"					, "321456789.py"), [1]
          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          lowercase__    ,      lowercase__:							Any         =     os.path.join(gettempdir()					, "ipykernel_12345"					, "654123987.py"), [0]
          lowercase__:							Dict         =     mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase_					, UpperCAmelCase_))).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							str         =     [0], [1]

          def func():
                 return foo, bar

          lowercase__:							List[str]         =     "__main__"

          def globalvars_mocka_side_effect(UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_):
                 return {"foo": foo, "bar": bar}

          def globalvars_mocka_side_effect(UpperCAmelCase_					, *UpperCAmelCase_					, **UpperCAmelCase_):
                 return {"bar": bar, "foo": foo}

          with patch("dill.detect.globalvars"					, side_effect=UpperCAmelCase_) as globalvars_mocka:
                 lowercase__:							Optional[Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
                 self.assertGreater(globalvars_mocka.call_count					, 0)
          with patch("dill.detect.globalvars"					, side_effect=UpperCAmelCase_) as globalvars_mocka:
                 lowercase__:							Tuple         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
                 self.assertGreater(globalvars_mocka.call_count					, 0)
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          lowercase__:							List[Any]         =     Hasher.hash("hello")
          lowercase__:							Optional[int]         =     Hasher.hash("hello")
          lowercase__:							int         =     Hasher.hash("there")
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							str         =     Hasher.hash(Foo("hello"))
          lowercase__:							Tuple         =     Hasher.hash(Foo("hello"))
          lowercase__:							Union[str, Any]         =     Hasher.hash(Foo("there"))
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							str         =     Hasher()
          for x in ["hello", Foo("hello")]:
                 hasher.update(UpperCAmelCase_)
          lowercase__:							Dict         =     hasher.hexdigest()
          lowercase__:							Any         =     Hasher()
          for x in ["hello", Foo("hello")]:
                 hasher.update(UpperCAmelCase_)
          lowercase__:							List[Any]         =     hasher.hexdigest()
          lowercase__:							Optional[int]         =     Hasher()
          for x in ["there", Foo("there")]:
                 hasher.update(UpperCAmelCase_)
          lowercase__:							Tuple         =     hasher.hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          with self.assertRaises(pickle.PicklingError):
                 Hasher.hash(UnpicklableCallable(Foo("hello")))





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     "abc"
          lowercase__:							Any         =     [string, string]  # two strings have the same ids
          lowercase__:							str         =     [string, string]
          lowercase__:							Optional[int]         =     json.loads(F"""[\"{string}\", \"{string}\"]""")  # two strings have different ids
          self.assertIs(obja[0]					, UpperCAmelCase_)
          self.assertIs(obja[0]					, obja[1])
          self.assertIs(obja[0]					, UpperCAmelCase_)
          self.assertIs(obja[0]					, obja[1])
          self.assertIsNot(obja[0]					, UpperCAmelCase_)
          self.assertIsNot(obja[0]					, obja[1])
          lowercase__:							List[Any]         =     Hasher.hash(UpperCAmelCase_)
          lowercase__:							str         =     Hasher.hash(UpperCAmelCase_)
          lowercase__:							Tuple         =     Hasher.hash(UpperCAmelCase_)
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)





   @require_tiktoken
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          import tiktoken

          lowercase__:							Union[str, Any]         =     tiktoken.get_encoding("gpt2")
          lowercase__:							List[str]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Dict         =     tiktoken.get_encoding("r50k_base")
          lowercase__:							Dict         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							List[Any]         =     tiktoken.get_encoding("gpt2")
          lowercase__:							Optional[int]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   @require_torch
   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          import torch

          lowercase__:							Tuple         =     torch.tensor([1.0])
          lowercase__:							Optional[int]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Dict         =     torch.tensor([2.0])
          lowercase__:							Union[str, Any]         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Tuple         =     torch.tensor([1.0])
          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)





   @require_spacy
   @require_spacy_model("en_core_web_sm")
   @require_spacy_model("fr_core_news_sm")
   @pytest.mark.integration
   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          import spacy

          lowercase__:							Tuple         =     spacy.load("en_core_web_sm")
          lowercase__:							Any         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							Any         =     spacy.load("fr_core_news_sm")
          lowercase__:							str         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          lowercase__:							int         =     spacy.load("en_core_web_sm")
          lowercase__:							str         =     mda(datasets.utils.py_utils.dumps(UpperCAmelCase_)).hexdigest()
          self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_)
          self.assertNotEqual(UpperCAmelCase_					, UpperCAmelCase_)







@pytest.mark.integration
def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     tmp_path / "dir1"
       lowercase__:							Tuple         =     tmp_path / "dir2"
       dira.mkdir()
       dira.mkdir()
       lowercase__:							Union[str, Any]         =     "script.py"
       lowercase__:							Any         =     dedent(
           "\n    from datasets.fingerprint import Hasher\n    def foo():\n        pass\n    print(Hasher.hash(foo))\n    "						)
       lowercase__:							List[str]         =     dira / script_filename
       lowercase__:							int         =     dira / script_filename
       with script_patha.open("w"						) as f:
              f.write(snake_case_						)
       with script_patha.open("w"						) as f:
              f.write(snake_case_						)
       lowercase__:							Any         =     subprocess.check_output(["python", str(snake_case_						)]						)
       lowercase__:							Optional[Any]         =     subprocess.check_output(["python", str(snake_case_						)]						)
       assert fingerprinta == fingerprinta






def 							A(    ):
       """simple docstring"""





       lowercase__:							Optional[int]         =     {"a": [0, 1, 2]}
       lowercase__:							Dict         =     DatasetChild(InMemoryTable.from_pydict(snake_case_						)						)
       lowercase__:							List[Any]         =     dataset.funca()._fingerprint
       assert expected_fingerprint == dataset.funca()._fingerprint
       assert expected_fingerprint != dataset.funca()._fingerprint

       with Pool(2						) as p:
              assert expected_fingerprint == p.apply_async(dataset.funca						).get()._fingerprint
              assert expected_fingerprint != p.apply_async(dataset.funca						).get()._fingerprint






def 							A(    ):
       """simple docstring"""





       lowercase__:							List[str]         =     {"a": [0, 1, 2]}







       class 						_a  (		datasets.Dataset       ):





          '''simple docstring'''






          @fingerprint_transform(inplace=UpperCAmelCase_)
          def __lowercase							(       self					, UpperCAmelCase_)			->   List[str]:




                 '''simple docstring'''




                 return DummyDatasetChild(self.data					, fingerprint=UpperCAmelCase_)







       lowercase__:							Tuple         =     DummyDatasetChild(InMemoryTable.from_pydict(snake_case_						)						).func()







       class 						_a  (		datasets.Dataset       ):





          '''simple docstring'''






          @fingerprint_transform(inplace=UpperCAmelCase_					, version="1.0.0")
          def __lowercase							(       self					, UpperCAmelCase_)			->   Optional[int]:




                 '''simple docstring'''




                 return DummyDatasetChild(self.data					, fingerprint=UpperCAmelCase_)







       lowercase__:							Optional[int]         =     DummyDatasetChild(InMemoryTable.from_pydict(snake_case_						)						).func()







       class 						_a  (		datasets.Dataset       ):





          '''simple docstring'''






          @fingerprint_transform(inplace=UpperCAmelCase_					, version="2.0.0")
          def __lowercase							(       self					, UpperCAmelCase_)			->   Any:




                 '''simple docstring'''




                 return DummyDatasetChild(self.data					, fingerprint=UpperCAmelCase_)







       lowercase__:							Optional[int]         =     DummyDatasetChild(InMemoryTable.from_pydict(snake_case_						)						).func()

       assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}						) == 3







def 							A(    ):
       """simple docstring"""





       lowercase__:							int         =     Hasher()
       hasher.update(lambda snake_case_						: x						)






