"""simple docstring"""


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
    from comet_ml import OfflineExperiment

if is_tensorboard_available():
    import struct

    import tensorboard.compat.proto.event_pba as event_pba

__UpperCamelCase										=     logging.getLogger(__name__)




@require_tensorboard
class 					UpperCamelCase   (							unittest.TestCase  ):







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   'test_project_with_config'
            with tempfile.TemporaryDirectory() as dirpath:
                 snake_case_   			=   Accelerator(log_with='tensorboard',				project_dir=lowerCAmelCase__)
                 snake_case_   			=   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(lowerCAmelCase__,				lowerCAmelCase__)
                 accelerator.end_training()
                 for child in Path(f'{dirpath}/{project_name}').glob('*/**'):
                      snake_case_   			=   list(filter(lambda lowerCAmelCase__: x.is_file(),				child.iterdir()))[0]
                 self.assertNotEqual(str(lowerCAmelCase__),				'')







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   'test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 snake_case_   			=   Accelerator(log_with='tensorboard',				project_dir=lowerCAmelCase__)
                 accelerator.init_trackers(lowerCAmelCase__)
                 snake_case_   			=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(lowerCAmelCase__,				step=0)
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 snake_case_   			=   list(filter(lambda lowerCAmelCase__: x.is_file(),				Path(f'{dirpath}/{project_name}').iterdir()))[0]
                 self.assertNotEqual(str(lowerCAmelCase__),				'')







       def 						a_		(					self) -> Dict:
            snake_case_   			=   'test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 snake_case_   			=   Accelerator(log_with='tensorboard',				project_dir=lowerCAmelCase__)
                 accelerator.init_trackers(lowerCAmelCase__)
                 snake_case_   			=   {'tensor': torch.tensor(1)}
                 accelerator.log(lowerCAmelCase__,				step=0)
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 snake_case_   			=   list(filter(lambda lowerCAmelCase__: x.is_file(),				Path(f'{dirpath}/{project_name}').iterdir()))[0]
                 # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
                 with open(lowerCAmelCase__,				'rb') as f:
                      snake_case_   			=   f.read()
                 snake_case_   			=   False
                 while data:
                      snake_case_   			=   struct.unpack('Q',				data[:8])

                      snake_case_   			=   data[12 : 12 + int(header[0])]  # 8+4
                      snake_case_   			=   data[12 + int(header[0]) + 4 :]
                      snake_case_   			=   event_pba.Event()

                      event.ParseFromString(lowerCAmelCase__)
                      if event.HasField('summary'):
                           for value in event.summary.value:
                                if value.simple_value == 1.0 and value.tag == "tensor":
                                     snake_case_   			=   True
                 self.assertTrue(lowerCAmelCase__,				'Converted tensor was not found in the log file!')







       def 						a_		(					self) -> Optional[int]:
            with self.assertRaisesRegex(lowerCAmelCase__,				'Logging with `tensorboard` requires a `logging_dir`'):
                 snake_case_   			=   Accelerator(log_with='tensorboard')
            with tempfile.TemporaryDirectory() as dirpath:
                 snake_case_   			=   Accelerator(log_with='tensorboard',				project_dir=lowerCAmelCase__)







       def 						a_		(					self) -> Optional[int]:
            snake_case_   			=   ProjectConfiguration(total_limit=30)
            with tempfile.TemporaryDirectory() as dirpath:
                 snake_case_   			=   Accelerator(log_with='tensorboard',				project_dir=lowerCAmelCase__,				project_config=lowerCAmelCase__)




@require_wandb
@mock.patch.dict(os.environ       ,						{"WANDB_MODE": "offline"}  )
class 					UpperCamelCase   (							lowerCAmelCase__       ,						lowerCAmelCase__  ):







       def 						a_		(					self) -> str:
            super().setUp()
            # wandb let's us override where logs are stored to via the WANDB_DIR env var
            self.add_mocks(mock.patch.dict(os.environ,				{'WANDB_DIR': self.tmpdir}))







       @staticmethod
       def 						a_		(					lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = True) -> Union[str, Any]:
            snake_case_   			=   Rf'{section} ([\S\s]*?)\n\n'
            if record:
                 snake_case_   			=   Rf'Record: {pattern}'
            snake_case_   			=   re.findall(lowerCAmelCase__,				lowerCAmelCase__)[0]
            # A config
            if section == "config" or section == "history":
                 snake_case_   			=   re.findall(R'"([a-zA-Z0-9_.,]+)',				lowerCAmelCase__)
                 return {key: val for key, val in zip(cleaned_record[0::2],				cleaned_record[1::2])}
            # Everything else
            else:
                 return dict(re.findall(R'(\w+): "([^\s]+)"',				lowerCAmelCase__))







       @skip
       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   'test_project_with_config'
            snake_case_   			=   Accelerator(log_with='wandb')
            snake_case_   			=   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
            snake_case_   			=   {'wandb': {'tags': ['my_tag']}}
            accelerator.init_trackers(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            snake_case_   			=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
            accelerator.log(lowerCAmelCase__,				step=0)
            accelerator.end_training()
            # The latest offline log is stored at wandb/latest-run/*.wandb
            for child in Path(f'{self.tmpdir}/wandb/latest-run').glob('*'):
                 if child.is_file() and child.suffix == ".wandb":
                      snake_case_   			=   subprocess.check_output(
                          ['wandb', 'sync', '--view', '--verbose', str(lowerCAmelCase__)],				env=os.environ.copy()).decode('utf8',				'ignore')
                      break

        # Check HPS through careful parsing and cleaning
            snake_case_   			=   self.parse_log(lowerCAmelCase__,				'config')
            self.assertEqual(logged_items['num_iterations'],				'12')
            self.assertEqual(logged_items['learning_rate'],				'0.01')
            self.assertEqual(logged_items['some_boolean'],				'false')
            self.assertEqual(logged_items['some_string'],				'some_value')
            self.assertEqual(logged_items['some_string'],				'some_value')

            # Run tags
            snake_case_   			=   self.parse_log(lowerCAmelCase__,				'run',				lowerCAmelCase__)
            self.assertEqual(logged_items['tags'],				'my_tag')

            # Actual logging
            snake_case_   			=   self.parse_log(lowerCAmelCase__,				'history')
            self.assertEqual(logged_items['total_loss'],				'0.1')
            self.assertEqual(logged_items['iteration'],				'1')
            self.assertEqual(logged_items['my_text'],				'some_value')
            self.assertEqual(logged_items['_step'],				'0')







def    UpperCAmelCase       (						self		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     snake_case_   			=   run_name
     snake_case_   			=   OfflineExperiment(project_name=UpperCAmelCase		,   offline_directory=UpperCAmelCase   )
     logger.info(f'Initialized offline CometML project {self.run_name}'   )
     logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'   )




@require_comet_ml
@mock.patch.object(lowerCAmelCase__       ,						"__init__"       ,						lowerCAmelCase__  )
class 					UpperCamelCase   (							unittest.TestCase  ):







       @staticmethod
       def 						a_		(					lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__ = False) -> Tuple:
            for log in log_list:
                 snake_case_   			=   json.loads(lowerCAmelCase__)['payload']
                 if is_param and "param" in j.keys():
                      if j["param"]["paramName"] == key:
                           return j["param"]["paramValue"]
                 if "log_other" in j.keys():
                      if j["log_other"]["key"] == key:
                           return j["log_other"]["val"]
                 if "metric" in j.keys():
                      if j["metric"]["metricName"] == key:
                           return j["metric"]["metricValue"]







       def 						a_		(					self) -> Dict:
            with tempfile.TemporaryDirectory() as d:
                 snake_case_   			=   CometMLTracker('test_project_with_config',				lowerCAmelCase__)
                 snake_case_   			=   Accelerator(log_with=lowerCAmelCase__)
                 snake_case_   			=   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(lowerCAmelCase__,				lowerCAmelCase__)
                 accelerator.end_training()
                 snake_case_   			=   os.listdir(lowerCAmelCase__)[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case_   			=   os.path.join(lowerCAmelCase__,				lowerCAmelCase__)
                 snake_case_   			=   zipfile.ZipFile(lowerCAmelCase__,				'r')
                 snake_case_   			=   archive.open('messages.json').read().decode('utf-8')
            snake_case_   			=   log.split('\n')[:-1]
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'num_iterations',				lowerCAmelCase__),				12)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'learning_rate',				lowerCAmelCase__),				0.01)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'some_boolean',				lowerCAmelCase__),				lowerCAmelCase__)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'some_string',				lowerCAmelCase__),				'some_value')







       def 						a_		(					self) -> List[Any]:
            with tempfile.TemporaryDirectory() as d:
                 snake_case_   			=   CometMLTracker('test_project_with_config',				lowerCAmelCase__)
                 snake_case_   			=   Accelerator(log_with=lowerCAmelCase__)
                 accelerator.init_trackers(lowerCAmelCase__)
                 snake_case_   			=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(lowerCAmelCase__,				step=0)
                 accelerator.end_training()
                 snake_case_   			=   os.listdir(lowerCAmelCase__)[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case_   			=   os.path.join(lowerCAmelCase__,				lowerCAmelCase__)
                 snake_case_   			=   zipfile.ZipFile(lowerCAmelCase__,				'r')
                 snake_case_   			=   archive.open('messages.json').read().decode('utf-8')
            snake_case_   			=   log.split('\n')[:-1]
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'curr_step',				lowerCAmelCase__),				0)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'total_loss'),				0.1)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'iteration'),				1)
            self.assertEqual(self.get_value_from_key(lowerCAmelCase__,				'my_text'),				'some_value')




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = [
           "total_loss",
           "iteration",
           "my_text",
           "learning_rate",
           "num_iterations",
           "some_boolean",
           "some_string",
       ]

       SCREAMING_SNAKE_CASE_						      = "my_custom_tracker"
       SCREAMING_SNAKE_CASE_						      = False







       def __init__(					self,				lowerCAmelCase__) -> List[str]:
            snake_case_   			=   open(f'{dir}/log.csv',				'w+')
            snake_case_   			=   csv.DictWriter(self.f,				fieldnames=self._col_names)
            self.writer.writeheader()







       @property
       def 						a_		(					self) -> Optional[int]:
            return self.writer







       def 						a_		(					self,				lowerCAmelCase__) -> Union[str, Any]:
            logger.info('Call init')
            self.writer.writerow(lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Optional[Any]:
            logger.info('Call log')
            self.writer.writerow(lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            self.f.close()




class 					UpperCamelCase   (							unittest.TestCase  ):







       def 						a_		(					self) -> Any:
            with tempfile.TemporaryDirectory() as d:
                 snake_case_   			=   MyCustomTracker(lowerCAmelCase__)
                 snake_case_   			=   Accelerator(log_with=lowerCAmelCase__)
                 snake_case_   			=   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers('Some name',				lowerCAmelCase__)
                 accelerator.end_training()
                 with open(f'{d}/log.csv',				'r') as f:
                      snake_case_   			=   csv.DictReader(lowerCAmelCase__)
                      snake_case_   			=   next(lowerCAmelCase__)
                      snake_case_   			=   {
                          'total_loss': '',
                          'iteration': '',
                          'my_text': '',
                          'learning_rate': '0.01',
                          'num_iterations': '12',
                          'some_boolean': 'False',
                          'some_string': 'some_value',
                      }
                      self.assertDictEqual(lowerCAmelCase__,				lowerCAmelCase__)







       def 						a_		(					self) -> Optional[int]:
            with tempfile.TemporaryDirectory() as d:
                 snake_case_   			=   MyCustomTracker(lowerCAmelCase__)
                 snake_case_   			=   Accelerator(log_with=lowerCAmelCase__)
                 accelerator.init_trackers('Some name')
                 snake_case_   			=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(lowerCAmelCase__,				step=0)
                 accelerator.end_training()
                 with open(f'{d}/log.csv',				'r') as f:
                      snake_case_   			=   csv.DictReader(lowerCAmelCase__)
                      snake_case_   			=   next(lowerCAmelCase__)
                      snake_case_   			=   {
                          'total_loss': '0.1',
                          'iteration': '1',
                          'my_text': 'some_value',
                          'learning_rate': '',
                          'num_iterations': '',
                          'some_boolean': '',
                          'some_string': '',
                      }
                      self.assertDictEqual(lowerCAmelCase__,				lowerCAmelCase__)


"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # 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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     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__":
    __UpperCamelCase										=     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										=     parser.parse_args()

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

