

import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase_					(						unittest.TestCase  ):



      @slow
      @unittest.skip('''Model is not available.'''	)
      def 		UpperCamelCase_	(    self   :       int	)					->  Tuple:
        _snake_case		=							'''nvidia/megatron-gpt2-345m/'''
        if "MYDIR" in os.environ:
          _snake_case		=							os.path.join(os.environ['''MYDIR''']    ,      A__	)
        _snake_case		=							GPTaLMHeadModel.from_pretrained(A__	)
        model.to(A__	)
        model.half()

        _snake_case		=							torch.tensor(
            [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]    ,      device=A__    ,      dtype=torch.long    ,      )

        with torch.no_grad():
          _snake_case		=							model(A__	).logits

        _snake_case		=							torch.Size((1, 9, 50257)	)
        self.assertEqual(output.shape    ,      A__	)

        _snake_case		=							torch.tensor(
            [
                4.9414,
                -0.2920,
                -1.2148,
                -4.0273,
                -0.5161,
                -5.2109,
                -1.2412,
                -1.8301,
                -1.7734,
                -4.7148,
                -0.2317,
                -1.0811,
                -2.1777,
                0.4141,
                -3.7969,
                -4.0586,
                -2.5332,
                -3.3809,
                4.3867,
            ]    ,      device=A__    ,      dtype=torch.half    ,      )

        for i in range(19	):
          _snake_case,  _snake_case		=							8 * i // 17, 2792 * i  # along the diagonal
          _snake_case,  _snake_case		=							output[0, r, c], expected_diag[i]
          _snake_case		=							f"""row={r} col={c} computed={computed} expected={expected}"""
          self.assertAlmostEqual(A__    ,      A__    ,      delta=1e-4    ,      msg=A__	)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






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

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _snake_case		=							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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							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		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _snake_case		=							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    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






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

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

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

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							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__":
 __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)

