"""simple docstring"""




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				pt_attn_layer.in_proj_weight.chunk(3							,     dim=0					)
							A__						  =				pt_attn_layer.in_proj_bias.chunk(3							,     dim=0					)

							A__						  =				pt_attn_layer.out_proj.weight
							A__						  =				pt_attn_layer.out_proj.bias

							A__						  =				q_proj
							A__						  =				q_proj_bias

							A__						  =				k_proj
							A__						  =				k_proj_bias

							A__						  =				v_proj
							A__						  =				v_proj_bias

							A__						  =				out_proj_weights
							A__						  =				out_proj_bias






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							copy_linear(hf_mlp.fca							,     pt_mlp.c_fc					)
							copy_linear(hf_mlp.fca							,     pt_mlp.c_proj					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				pt_linear.weight
							A__						  =				pt_linear.bias






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""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       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							for hf_layer, pt_layer in zip(UpperCamelCase__							,     UpperCamelCase__					):
														copy_layer(UpperCamelCase__							,     UpperCamelCase__					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				pt_model.token_embedding.weight
							A__						  =				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       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				pt_model.text_projection.data.T

							# copy text encoder
							copy_encoder(hf_model.text_model							,     UpperCamelCase__					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				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
							A__						  =				pt_model.visual.conva.weight.data
							A__						  =				pt_model.visual.class_embedding
							A__						  =				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       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=None					):






							"""simple docstring"""





							if config_path is not None:
														A__						  =				CLIPConfig.from_pretrained(UpperCamelCase__					)
							else:
														A__						  =				CLIPConfig(projection_dim=512							,     text_config={}							,     vision_config={}					)

							A__						  =				CLIPModel(UpperCamelCase__					).eval()

							A__						  =				load(UpperCamelCase__							,     device='cpu'							,     jit=UpperCamelCase__					)
							A__						  =				pt_model.eval()

							copy_text_model_and_projection(UpperCamelCase__							,     UpperCamelCase__					)
							copy_vison_model_and_projection(UpperCamelCase__							,     UpperCamelCase__					)
							A__						  =				pt_model.logit_scale

							A__						  =				torch.arange(0							,     77					).unsqueeze(0					)
							A__						  =				torch.randn(1							,     3							,     224							,     224					)

							A__						  =				hf_model(input_ids=UpperCamelCase__							,     pixel_values=UpperCamelCase__							,     return_dict=UpperCamelCase__					)
							A__						  =				hf_outputs.logits_per_image
							A__						  =				hf_outputs.logits_per_text
							A__						  =				pt_model(UpperCamelCase__							,     UpperCamelCase__					)

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

							hf_model.save_pretrained(UpperCamelCase__					)


if __name__ == "__main__":
				__lowerCamelCase									=	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")
				__lowerCamelCase									=	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_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

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


