



"""simple docstring"""






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_	,  snake_case_	,  snake_case_						:Dict								=						pt_attn_layer.in_proj_weight.chunk(3,     dim=0							)
					snake_case_	,  snake_case_	,  snake_case_						:Dict								=						pt_attn_layer.in_proj_bias.chunk(3,     dim=0							)

					snake_case_						:str								=						pt_attn_layer.out_proj.weight
					snake_case_						:List[Any]								=						pt_attn_layer.out_proj.bias

					snake_case_						:List[Any]								=						q_proj
					snake_case_						:Tuple								=						q_proj_bias

					snake_case_						:int								=						k_proj
					snake_case_						:Tuple								=						k_proj_bias

					snake_case_						:str								=						v_proj
					snake_case_						:List[Any]								=						v_proj_bias

					snake_case_						:str								=						out_proj_weights
					snake_case_						:int								=						out_proj_bias
def      A      (			_A,     _A							):




					"""simple docstring"""
					copy_linear(hf_mlp.fca,     pt_mlp.c_fc							)
					copy_linear(hf_mlp.fca,     pt_mlp.c_proj							)
def      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_						:str								=						pt_linear.weight
					snake_case_						:List[Any]								=						pt_linear.bias
def      A      (			_A,     _A							):




					"""simple docstring"""
					# copy layer norms
					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      A      (			_A,     _A							):




					"""simple docstring"""
					for hf_layer, pt_layer in zip(_A,     _A							):
										copy_layer(_A,     _A							)
def      A      (			_A,     _A							):




					"""simple docstring"""
					# copy  embeds
					snake_case_						:Optional[Any]								=						pt_model.token_embedding.weight
					snake_case_						:Dict								=						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      A      (			_A,     _A							):




					"""simple docstring"""
					# copy projection
					snake_case_						:str								=						pt_model.text_projection.data.T

					# copy text encoder
					copy_encoder(hf_model.text_model,     _A							)
def      A      (			_A,     _A							):




					"""simple docstring"""
					# copy projection
					snake_case_						: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
					snake_case_						:Tuple								=						pt_model.visual.conva.weight.data
					snake_case_						:Optional[int]								=						pt_model.visual.class_embedding
					snake_case_						:Union[str, Any]								=						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      A      (			_A,     _A,     _A=None							):




					"""simple docstring"""
					if config_path is not None:
										snake_case_						:List[str]								=						CLIPConfig.from_pretrained(_A							)
					else:
										snake_case_						:List[str]								=						CLIPConfig(projection_dim=512,     text_config={},     vision_config={}							)

					snake_case_						:Dict								=						CLIPModel(_A							).eval()

					snake_case_	,  snake_case_						:Dict								=						load(_A,     device="cpu",     jit=_A							)
					snake_case_						:Union[str, Any]								=						pt_model.eval()

					copy_text_model_and_projection(_A,     _A							)
					copy_vison_model_and_projection(_A,     _A							)
					snake_case_						:List[Any]								=						pt_model.logit_scale

					snake_case_						:str								=						torch.arange(0,     77							).unsqueeze(0							)
					snake_case_						:str								=						torch.randn(1,     3,     224,     224							)

					snake_case_						:Optional[Any]								=						hf_model(input_ids=_A,     pixel_values=_A,     return_dict=_A							)
					snake_case_						:Dict								=						hf_outputs.logits_per_image
					snake_case_						:List[str]								=						hf_outputs.logits_per_text
					snake_case_	,  snake_case_						:str								=						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__":
				__UpperCAmelCase      :      Any   					=					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')
				__UpperCAmelCase      :      int   					=					parser.parse_args()

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase      :      Optional[int]   					=					{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      int   					=					['SegformerFeatureExtractor']
				__UpperCAmelCase      :      Dict   					=					['SegformerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Optional[Any]   					=					[
				    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SegformerDecodeHead',
				    'SegformerForImageClassification',
				    'SegformerForSemanticSegmentation',
				    'SegformerLayer',
				    'SegformerModel',
				    'SegformerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Dict   					=					[
				    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFSegformerDecodeHead',
				    'TFSegformerForImageClassification',
				    'TFSegformerForSemanticSegmentation',
				    'TFSegformerModel',
				    'TFSegformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				__UpperCAmelCase      :      Optional[Any]   					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

