

from dataclasses import dataclass, field
from typing import Optional


@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Model name or path of model to be trained."""}							)
			__A						  =  field(
			    default="""./"""							,						metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot-clean-train"""							,						metadata={"""help""": """Name or path of training dataset."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot-clean-valid"""							,						metadata={"""help""": """Name or path of validation dataset."""}							)
			__A						  =  field(default=2							,						metadata={"""help""": """Batch size for training."""}							)
			__A						  =  field(default=2							,						metadata={"""help""": """Batch size for evaluation."""}							)
			__A						  =  field(default=0.1							,						metadata={"""help""": """Value of weight decay."""}							)
			__A						  =  field(
			    default=1_0_0_0_0							,						metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}							)
			__A						  =  field(default=2E-4							,						metadata={"""help""": """Learning rate fo training."""}							)
			__A						  =  field(default="""cosine"""							,						metadata={"""help""": """Learning rate."""}							)
			__A						  =  field(
			    default=7_5_0							,						metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}							)
			__A						  =  field(
			    default=1_6							,						metadata={"""help""": """Number of gradient accumulation steps."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}							)
			__A						  =  field(default=5_0_0_0_0							,						metadata={"""help""": """Maximum number of training steps."""}							)
			__A						  =  field(
			    default=-1							,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}							)
			__A						  =  field(default=1_0_2_4							,						metadata={"""help""": """Sequence lengths used for training."""}							)
			__A						  =  field(default=1							,						metadata={"""help""": """Training seed."""}							)
			__A						  =  field(
			    default=1_0_2_4							,						metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """If True the data is pretokenized."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Model name or path of model to be evaluated."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot-clean-valid"""							,						metadata={"""help""": """Name or path of validation dataset."""}							)
			__A						  =  field(default=2							,						metadata={"""help""": """Batch size used for evaluation."""}							)
			__A						  =  field(
			    default=-1							,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}							)
			__A						  =  field(default=1_0_2_4							,						metadata={"""help""": """Length of sequences to be evaluated."""}							)
			__A						  =  field(default=1							,						metadata={"""help""": """Random seed used for evaluation."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Model name or path of model to be evaluated."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """Number of workers used for code evaluation."""}							)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """Sample from the language model's output distribution."""}							)
			__A						  =  field(default=0.2							,						metadata={"""help""": """Sampling temperature used for generation."""}							)
			__A						  =  field(default=2_5_6							,						metadata={"""help""": """Maximum number of newly generated tokens."""}							)
			__A						  =  field(default=0							,						metadata={"""help""": """Top-k parameter used for generation."""}							)
			__A						  =  field(default=0.95							,						metadata={"""help""": """Top-p parameter used for nucleus sampling."""}							)
			__A						  =  field(default=1_0							,						metadata={"""help""": """Number of generations to run in parallel."""}							)
			__A						  =  field(
			    default=2_0_0							,						metadata={"""help""": """Number of completions to generate for each sample."""}							)
			__A						  =  field(default=1							,						metadata={"""help""": """Random seed used for evaluation."""}							)
			__A						  =  field(
			    default="""eval_results.json"""							,						metadata={"""help""": """Random seed used for evaluation."""}							)
			__A						  =  field(
			    default="""0"""							,						metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}							)
			__A						  =  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 SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default=__snake_case							,						metadata={
			        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
			    }							,						)
			__A						  =  field(
			    default="""transformersbook/codeparrot"""							,						metadata={"""help""": """Folder or name of dataset to process."""}							)
			__A						  =  field(
			    default="""codeparrot-clean"""							,						metadata={"""help""": """Folder to save processed processed dataset."""}							)
			__A						  =  field(
			    default=1_0_0_0_0_0							,						metadata={"""help""": """Number of files to save per JSON output file."""}							)
			__A						  =  field(default="""content"""							,						metadata={"""help""": """Column containing text data to process."""}							)
			__A						  =  field(
			    default=1_0_0_0							,						metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}							)
			__A						  =  field(
			    default=1_0_0							,						metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}							)
			__A						  =  field(
			    default=0.25							,						metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}							)
			__A						  =  field(
			    default=1.5							,						metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}							)
			__A						  =  field(
			    default=0.7							,						metadata={"""help""": """Probability for filtering config, test and uncommon files."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Name or path to the tokenizer."""}							,						)
			__A						  =  field(
			    default=__snake_case							,						metadata={"""help""": """If True, near-duplicate samples are removed."""}							)
			__A						  =  field(
			    default=0.85							,						metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""gpt2"""							,						metadata={"""help""": """Base tokenizer to build new tokenizer from."""}							)
			__A						  =  field(
			    default="""transformersbook/codeparrot-train"""							,						metadata={"""help""": """Dataset to train tokenizer on."""}							)
			__A						  =  field(default="""content"""							,						metadata={"""help""": """Column containing text data to process."""}							)
			__A						  =  field(default=2_0_0_0_0_0							,						metadata={"""help""": """Number of examples to train tokenizer on."""}							)
			__A						  =  field(
			    default=3_2_7_6_8							,						metadata={"""help""": """Number of examples to train the tokenizer on."""}							)
			__A						  =  field(default="""codeparrot"""							,						metadata={"""help""": """Name of new tokenizer."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """Push saved tokenizer to the hub."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Name or path to the tokenizer."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot-clean-train"""							,						metadata={"""help""": """Name or path to the dataset to pretokenize."""}							)
			__A						  =  field(
			    default="""tokenized-codeparrot-train"""							,						metadata={"""help""": """Repo name of the pretokenized data."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """Number of workers used for code evaluation."""}							)







@dataclass
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			__A						  =  field(
			    default="""gpt2-large"""							,						metadata={"""help""": """Configuration to use for model initialization."""}							)
			__A						  =  field(
			    default="""codeparrot/codeparrot"""							,						metadata={"""help""": """Tokenizer attached to model."""}							)
			__A						  =  field(default="""codeparrot"""							,						metadata={"""help""": """Name of the created model."""}							)
			__A						  =  field(default=__snake_case							,						metadata={"""help""": """Push saved tokenizer to the hub."""}							)



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= VQModel(
				    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPTextConfig(
				    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
				return CLIPTextModel(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= DDIMScheduler()
				snake_case_      		= self.dummy_vq_model

				snake_case_      		= LDMPipeline(unet=__UpperCamelCase ,				vqvae=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy'							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy' ,				return_dict=__UpperCamelCase							)[0]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				snake_case_      		= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=5 ,				output_type='numpy'							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)
				snake_case_      		= np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
