
"""simple docstring"""







import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


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







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[Any]=768    ):
												super().__init__(snake_case_    )
												__snake_case     			=       proj_size

												__snake_case     			=       CLIPVisionModel(snake_case_    )
												__snake_case     			=       PaintByExampleMapper(snake_case_    )
												__snake_case     			=       nn.LayerNorm(config.hidden_size    )
												__snake_case     			=       nn.Linear(config.hidden_size							,						self.proj_size    )

												# uncondition for scaling
												__snake_case     			=       nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Dict							,						snake_case_   :							Tuple=False    ):
												__snake_case     			=       self.model(pixel_values=snake_case_    )
												__snake_case     			=       clip_output.pooler_output
												__snake_case     			=       self.mapper(latent_states[:, None]    )
												__snake_case     			=       self.final_layer_norm(snake_case_    )
												__snake_case     			=       self.proj_out(snake_case_    )
												if return_uncond_vector:
																			return latent_states, self.uncond_vector

												return latent_states







class 							__magic_name__     (		nn.Module   ):





					def __init__(				self   :							Any							,						snake_case_   :							Tuple    ):
												super().__init__()
												__snake_case     			=       (config.num_hidden_layers + 1) // 5
												__snake_case     			=       config.hidden_size
												__snake_case     			=       1
												__snake_case     			=       nn.ModuleList(
												    [
												        BasicTransformerBlock(snake_case_							,						snake_case_							,						snake_case_							,						activation_fn="gelu"							,						attention_bias=snake_case_    )
												        for _ in range(snake_case_    )
												    ]    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							List[Any]    ):
												for block in self.blocks:
																			__snake_case     			=       block(snake_case_    )

												return hidden_states


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
