
"""simple docstring"""

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     str            =       pt_attn_layer.in_proj_weight.chunk(3,		dim=0     )
		lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Tuple            =       pt_attn_layer.in_proj_bias.chunk(3,		dim=0     )

		lowerCamelCase	:     Optional[Any]            =       pt_attn_layer.out_proj.weight
		lowerCamelCase	:     List[Any]            =       pt_attn_layer.out_proj.bias

		lowerCamelCase	:     Union[str, Any]            =       q_proj
		lowerCamelCase	:     Union[str, Any]            =       q_proj_bias

		lowerCamelCase	:     Tuple            =       k_proj
		lowerCamelCase	:     Dict            =       k_proj_bias

		lowerCamelCase	:     Optional[int]            =       v_proj
		lowerCamelCase	:     Any            =       v_proj_bias

		lowerCamelCase	:     Dict            =       out_proj_weights
		lowerCamelCase	:     Any            =       out_proj_bias
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		copy_linear(hf_mlp.fca,		pt_mlp.c_fc     )
		copy_linear(hf_mlp.fca,		pt_mlp.c_proj     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       pt_linear.weight
		lowerCamelCase	:     Optional[int]            =       pt_linear.bias
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		copy_linear(hf_layer.layer_norma,		pt_layer.ln_a     )
		copy_linear(hf_layer.layer_norma,		pt_layer.ln_a     )

		# copy MLP
		copy_mlp(hf_layer.mlp,		pt_layer.mlp     )

		# copy attn
		copy_attn_layer(hf_layer.self_attn,		pt_layer.attn     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		for hf_layer, pt_layer in zip(a_,		a_     ):
				copy_layer(a_,		a_     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     List[Any]            =       pt_model.token_embedding.weight
		lowerCamelCase	:     List[Any]            =       pt_model.positional_embedding

		# copy layer norm
		copy_linear(hf_encoder.final_layer_norm,		pt_model.ln_final     )

		# copy hidden layers
		copy_layers(hf_encoder.encoder.layers,		pt_model.transformer.resblocks     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       pt_model.text_projection.data.T

		# copy text encoder
		copy_encoder(hf_model.text_model,		a_     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Tuple            =       pt_model.visual.proj.data.T

		# copy layer norms
		copy_linear(hf_model.vision_model.pre_layrnorm,		pt_model.visual.ln_pre     )
		copy_linear(hf_model.vision_model.post_layernorm,		pt_model.visual.ln_post     )

		# copy embeds
		lowerCamelCase	:     Union[str, Any]            =       pt_model.visual.conva.weight.data
		lowerCamelCase	:     List[Any]            =       pt_model.visual.class_embedding
		lowerCamelCase	:     Dict            =       pt_model.visual.positional_embedding.data

		# copy encoder
		copy_layers(hf_model.vision_model.encoder.layers,		pt_model.visual.transformer.resblocks     )
@torch.no_grad()
def 						UpperCAmelCase   (						a_,		a_,		a_=None     ):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase	:     Optional[int]            =       CLIPConfig.from_pretrained(a_     )
		else:
				lowerCamelCase	:     Any            =       CLIPConfig(projection_dim=512,		text_config={},		vision_config={}     )

		lowerCamelCase	:     Optional[Any]            =       CLIPModel(a_     ).eval()

		lowerCamelCase      ,       lowerCamelCase	:     Tuple            =       load(a_,		device='cpu',		jit=a_     )
		lowerCamelCase	:     Union[str, Any]            =       pt_model.eval()

		copy_text_model_and_projection(a_,		a_     )
		copy_vison_model_and_projection(a_,		a_     )
		lowerCamelCase	:     int            =       pt_model.logit_scale

		lowerCamelCase	:     int            =       torch.arange(0,		77     ).unsqueeze(0     )
		lowerCamelCase	:     Optional[int]            =       torch.randn(1,		3,		224,		224     )

		lowerCamelCase	:     Tuple            =       hf_model(input_ids=a_,		pixel_values=a_,		return_dict=a_     )
		lowerCamelCase	:     Optional[Any]            =       hf_outputs.logits_per_image
		lowerCamelCase	:     Optional[int]            =       hf_outputs.logits_per_text
		lowerCamelCase      ,       lowerCamelCase	:     Tuple            =       pt_model(a_,		a_     )

		assert torch.allclose(a_,		a_,		atol=1E-3     )
		assert torch.allclose(a_,		a_,		atol=1E-3     )

		hf_model.save_pretrained(a_     )


if __name__ == "__main__":
					_A				=					argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					_A				=					parser.parse_args()

					convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		assert isinstance(a_,		a_     )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory',		[False, True]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[int]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     Tuple            =       CsvDatasetReader(a_,		cache_dir=a_,		keep_in_memory=a_     ).read()
		_check_csv_dataset(a_,		a_     )
@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCamelCase	:     List[str]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     str            =       features.copy() if features else default_expected_features
		lowerCamelCase	:     List[Any]            =       (
		    Features({feature: Value(a_     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		lowerCamelCase	:     Union[str, Any]            =       CsvDatasetReader(a_,		features=a_,		cache_dir=a_     ).read()
		_check_csv_dataset(a_,		a_     )
@pytest.mark.parametrize('split',		[None, NamedSplit('train'     ), 'train', 'test']     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Dict            =       CsvDatasetReader(a_,		cache_dir=a_,		split=a_     ).read()
		_check_csv_dataset(a_,		a_     )
		assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type',		[str, list]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if issubclass(a_,		a_     ):
				lowerCamelCase	:     Dict            =       csv_path
		elif issubclass(a_,		a_     ):
				lowerCamelCase	:     Tuple            =       [csv_path]
		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Tuple            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     List[Any]            =       CsvDatasetReader(a_,		cache_dir=a_     ).read()
		_check_csv_dataset(a_,		a_     )
def 						UpperCAmelCase   (						a_,		a_,		a_=("train",)     ):





		'''simple docstring'''


		assert isinstance(a_,		a_     )
		for split in splits:
				lowerCamelCase	:     Any            =       dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory',		[False, True]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     int            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_,		keep_in_memory=a_     ).read()
		_check_csv_datasetdict(a_,		a_     )
@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     str            =       tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     str            =       features.copy() if features else default_expected_features
		lowerCamelCase	:     Any            =       (
		    Features({feature: Value(a_     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		lowerCamelCase	:     Optional[Any]            =       CsvDatasetReader({'train': csv_path},		features=a_,		cache_dir=a_     ).read()
		_check_csv_datasetdict(a_,		a_     )
@pytest.mark.parametrize('split',		[None, NamedSplit('train'     ), 'train', 'test']     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if split:
				lowerCamelCase	:     List[Any]            =       {split: csv_path}
		else:
				lowerCamelCase	:     Any            =       {'train': csv_path, 'test': csv_path}
		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Union[str, Any]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Union[str, Any]            =       CsvDatasetReader(a_,		cache_dir=a_     ).read()
		_check_csv_datasetdict(a_,		a_,		splits=list(path.keys()     )     )
		assert all(dataset[split].split == split for split in path.keys()     )
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		with open(a_,		encoding='utf-8'     ) as csvfile:
				yield from csv.reader(a_     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[int]            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     List[str]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=1     ).write()

		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )
		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )

		for rowa, rowa in zip(a_,		a_     ):
				assert rowa == rowa
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Dict            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=2     ).write()

		lowerCamelCase	:     int            =       iter_csv_file(a_     )
		lowerCamelCase	:     Optional[int]            =       iter_csv_file(a_     )

		for rowa, rowa in zip(a_,		a_     ):
				assert rowa == rowa





def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Tuple            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Optional[int]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		with pytest.raises(a_     ):
				CsvDatasetWriter(dataset['train'],		a_,		num_proc=0     )






