

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]				):





  '''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__							(     _A :					int      , _A :					List[str]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					int				):





  '''simple docstring'''







  a__    =pt_linear.weight
  a__    =pt_linear.bias




def      UpperCAmelCase__							(     _A :					List[str]      , _A :					Any				):





  '''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 :					Optional[Any]      , _A :					Dict				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					List[str]				):





  '''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__							(     _A :					int      , _A :					Optional[Any]				):





  '''simple docstring'''







  a__    =pt_model.text_projection.data.T

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




def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					List[str]				):





  '''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__							(     _A :					str      , _A :					Union[str, Any]      , _A :					Optional[int]=None				):





  '''simple docstring'''







  if config_path is not None:
    a__    =CLIPConfig.from_pretrained(__lowercase				)
  else:
    a__    =CLIPConfig(projection_dim=5_12      , text_config={}      , vision_config={}				)

  a__    =CLIPModel(__lowercase				).eval()

  a__    =load(__lowercase      , device='''cpu'''      , jit=__lowercase				)
  a__    =pt_model.eval()

  copy_text_model_and_projection(__lowercase      , __lowercase				)
  copy_vison_model_and_projection(__lowercase      , __lowercase				)
  a__    =pt_model.logit_scale

  a__    =torch.arange(0      , 77				).unsqueeze(0				)
  a__    =torch.randn(1      , 3      , 2_24      , 2_24				)

  a__    =hf_model(input_ids=__lowercase      , pixel_values=__lowercase      , return_dict=__lowercase				)
  a__    =hf_outputs.logits_per_image
  a__    =hf_outputs.logits_per_text
  a__    =pt_model(__lowercase      , __lowercase				)

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

  hf_model.save_pretrained(__lowercase				)


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'''


import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
     import torch

     from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
     import pyanvml.pyanvml as nvml


UpperCamelCase			     =					logging.get_logger(__name__)






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      PyTorchBenchmarkArguments
  UpperCamelCase_			:      PretrainedConfig
  UpperCamelCase_			:      str										=				"PyTorch"



  @property
  def 					_snake_case     ( self				:							int	)			->		List[str]:







       '''simple docstring'''



       return torch.__version__



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		float:







       '''simple docstring'''



       A:		List[str]          =		self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_speed(_inference	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		[Memory, Optional[MemorySummary]]:







       '''simple docstring'''



       A:		Any          =		self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_memory(_inference	)



  def 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		float:







       '''simple docstring'''



       A:		List[Any]          =		self._prepare_train_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_speed(_train	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		[Memory, Optional[MemorySummary]]:







       '''simple docstring'''



       A:		List[Any]          =		self._prepare_train_func(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       return self._measure_memory(_train	)



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Callable[[], None]:







       '''simple docstring'''



       A:		int          =		self.config_dict[model_name]

       if self.args.torchscript:
            A:		int          =		True

       A:		Any          =		(
           hasattr(SCREAMING_SNAKE_CASE_					,  '''architectures'''	)
           and isinstance(config.architectures					,  SCREAMING_SNAKE_CASE_	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                 A:		Optional[int]          =		config.architectures[0]
                 A:		Optional[int]          =		__import__('''transformers'''					,  fromlist=[model_class]	)
                 A:		Dict          =		getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
                 A:		Optional[Any]          =		model_cls(SCREAMING_SNAKE_CASE_	)
            except ImportError:
                 raise ImportError(
                     f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                     ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
       else:
            A:		Union[str, Any]          =		MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

       model.eval()
       model.to(self.args.device	)

       # encoder-decoder has vocab size saved differently
       A:		Tuple          =		config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_					,  '''vocab_size'''	) else config.encoder.vocab_size
       A:		List[Any]          =		torch.randint(SCREAMING_SNAKE_CASE_					,  (batch_size, sequence_length)					,  dtype=torch.long					,  device=self.args.device	)

       if self.args.fpaa:
            logger.info('''Running training in Mixed Precision...'''	)
            if not self.args.is_gpu:
                 raise ValueError('''Mixed precision is possible only for GPU.'''	)
            # amp seems to have memory leaks so that memory usage
            # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
            model.half()

       if self.args.torchscript:
            with torch.no_grad():
                 A:		Any          =		torch.jit.trace(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       else:
            A:		Dict          =		model

       def encoder_decoder_forward():
            with torch.no_grad():
                 A:		List[str]          =		inference_model(SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_	)
            return outputs

       def encoder_forward():
            with torch.no_grad():
                 A:		Union[str, Any]          =		inference_model(SCREAMING_SNAKE_CASE_	)
            return outputs

       A:		List[Any]          =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
       return _forward



  def 					_snake_case     ( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Callable[[], None]:







       '''simple docstring'''



       A:		int          =		self.config_dict[model_name]

       A:		str          =		(
           hasattr(SCREAMING_SNAKE_CASE_					,  '''architectures'''	)
           and isinstance(config.architectures					,  SCREAMING_SNAKE_CASE_	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                 A:		List[str]          =		config.architectures[0]
                 A:		List[str]          =		__import__('''transformers'''					,  fromlist=[model_class]	)
                 A:		Optional[int]          =		getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
                 A:		Union[str, Any]          =		model_cls(SCREAMING_SNAKE_CASE_	)
            except ImportError:
                 raise ImportError(
                     f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                     ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
       else:
            A:		Union[str, Any]          =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

       if self.args.torchscript:
            raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
       else:
            A:		str          =		model

       model.train()
       model.to(self.args.device	)

       # encoder-decoder has vocab size saved differently
       A:		Tuple          =		config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_					,  '''vocab_size'''	) else config.encoder.vocab_size
       A:		str          =		torch.randint(SCREAMING_SNAKE_CASE_					,  (batch_size, sequence_length)					,  dtype=torch.long					,  device=self.args.device	)

       if self.args.fpaa:
            logger.info('''Running training in Mixed Precision...'''	)
            if not self.args.is_gpu:
                 raise ValueError('''Mixed precision is possible only for GPU.'''	)

            # amp seems to have memory leaks so that memory usage
            # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
            model.half()

       def compute_loss_and_backprob_encoder():
            A:		str          =		train_model(SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)[0]
            loss.backward()
            return loss

       def compute_loss_and_backprob_encoder_decoder():
            A:		Dict          =		train_model(SCREAMING_SNAKE_CASE_					,  decoder_input_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)[0]
            loss.backward()
            return loss

       A:		str          =		(
           compute_loss_and_backprob_encoder_decoder
           if config.is_encoder_decoder
           else compute_loss_and_backprob_encoder
       )
       return _train



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							Any	)			->		float:







       '''simple docstring'''



       try:
            if self.args.is_tpu or self.args.torchscript:
                 # run additional 10 times to stabilize compilation for tpu and torchscript
                 logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''	)
                 timeit.repeat(
                     SCREAMING_SNAKE_CASE_					,  repeat=1					,  number=5					,  )

            # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
            A:		Any          =		timeit.repeat(
                SCREAMING_SNAKE_CASE_					,  repeat=self.args.repeat					,  number=10					,  )

            if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                 import torch_xla.debug.metrics as met

                 self.print_fn(met.metrics_report()	)

            return min(SCREAMING_SNAKE_CASE_	) / 10.0
       except RuntimeError as e:
            self.print_fn(f"""Doesn't fit on GPU. {e}"""	)
            return "N/A"




  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Callable[[], None]	)			->		[Memory, MemorySummary]:







       '''simple docstring'''



       try:
            if self.args.trace_memory_line_by_line:
                 A:		List[Any]          =		start_memory_tracing('''transformers'''	)

            if self.args.is_tpu:
                 # tpu
                 raise NotImplementedError(
                     '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
                     ''' `--no-memory` or `args.memory=False`'''	)
            elif self.args.is_gpu:
                 if not is_pyanvml_available():
                      logger.warning(
                          '''py3nvml not installed, we won\'t log GPU memory usage. '''
                          '''Install py3nvml (pip install py3nvml) to log information about GPU.'''	)
                      A:		Optional[int]          =		'''N/A'''
                 else:
                      logger.info(
                          '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
                          ''' on the same GPU.'''	)
                      # init nvml
                      nvml.nvmlInit()
                      func()
                      A:		Union[str, Any]          =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
                      A:		Union[str, Any]          =		nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE_	)
                      A:		Tuple          =		meminfo.used
                      A:		Tuple          =		Memory(SCREAMING_SNAKE_CASE_	)
                      # shutdown nvml
                      nvml.nvmlShutdown()
            else:
                 # cpu
                 A:		int          =		measure_peak_memory_cpu(SCREAMING_SNAKE_CASE_	)
                 A:		str          =		Memory(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) else memory_bytes

            if self.args.trace_memory_line_by_line:
                 A:		Dict          =		stop_memory_tracing(SCREAMING_SNAKE_CASE_	)
            else:
                 A:		List[str]          =		None

            return memory, summary
       except RuntimeError as e:
            self.print_fn(f"""Doesn't fit on GPU. {e}"""	)
            return "N/A", None






