



"""simple docstring"""





import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


lowerCamelCase      =		TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("""""", """|""", """|"""),
    datarow=DataRow("""""", """|""", """|"""),
    padding=1,
    with_header_hide=None,
)


lowerCamelCase      =		[]
lowerCamelCase      =		[]

lowerCamelCase      =		{"""type""": """section""", """text""": {"""type""": """plain_text""", """text""": """No failed tests! 🤗""", """emoji""": True}}

lowerCamelCase      =		[
    {
        """type""": """header""",
        """text""": {
            """type""": """plain_text""",
            """text""": F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            """emoji""": True,
        },
    }
]

lowerCamelCase      =		0
for log in Path().glob("""*.log"""):
  lowerCamelCase      =		0
  with open(log, """r""") as f:
    for line in f:
      lowerCamelCase      =		json.loads(line)
      if line.get("""nodeid""", """""") != "":
        lowerCamelCase      =		line["""nodeid"""]
        if line.get("""duration""", None) is not None:
          lowerCamelCase      =		F"{line['duration']:.4f}"
          if line.get("""outcome""", """""") == "failed":
            section_num_failed += 1
            failed.append([test, duration, log.name.split("""_""")[0]])
            total_num_failed += 1
  group_info.append([str(log), section_num_failed, failed])
  lowerCamelCase      =		[]
  log.unlink()

lowerCamelCase      =		""""""
lowerCamelCase      =		[]
if total_num_failed > 0:
  for name, num_failed, failed_tests in group_info:
    if num_failed > 0:
      if num_failed == 1:
        message += F"*{name[1:]}: {num_failed} failed test*\n"
      else:
        message += F"*{name[1:]}: {num_failed} failed tests*\n"
      lowerCamelCase      =		[]
      lowerCamelCase      =		{}
      for test in failed_tests:
        lowerCamelCase      =		test[0].split("""::""")
        lowerCamelCase      =		data[0].split("""/""")[-1]
        if data[0] not in filesafailed:
          lowerCamelCase      =		[data[1:]]
        else:
          filesafailed[data[0]] += [data[1:]]
        failed_table.append(data)

      lowerCamelCase      =		[test[0] for test in failed_table]
      lowerCamelCase      =		list(set(files))
      # Count number of instances in failed_tests
      lowerCamelCase      =		[]
      for file in individual_files:
        table.append([file, len(filesafailed[file])])

      lowerCamelCase      =		tabulate(
          table,
          headers=["""Test Location""", """Num Failed"""],
          tablefmt=hf_table_format,
          stralign="""right""",
      )
      message += F"\n```\n{failed_table}\n```"
      all_filesafailed.append(filesafailed)
  if len(message) > 3_000:
    lowerCamelCase      =		"""Too many failed tests, please see the full report in the Action results."""
    lowerCamelCase      =		len(err) + 10
    lowerCamelCase      =		message[: 3_000 - offset] + F"\n...\n```\n{err}"
  print(F"### {message}")
else:
  lowerCamelCase      =		"""No failed tests! 🤗"""
  print(F"## {message}")
  payload.append(no_error_payload)

if os.environ.get("""TEST_TYPE""", """""") != "":
  from slack_sdk import WebClient

  lowerCamelCase      =		WebClient(token=os.environ["""SLACK_API_TOKEN"""])
  if message != "No failed tests! 🤗":
    lowerCamelCase      =		{
        """type""": """section""",
        """text""": {
            """type""": """mrkdwn""",
            """text""": message,
        },
    }
    payload.append(md_report)
    lowerCamelCase      =		{
        """type""": """section""",
        """text""": {
            """type""": """mrkdwn""",
            """text""": """*For more details:*""",
        },
        """accessory""": {
            """type""": """button""",
            """text""": {
                """type""": """plain_text""",
                """text""": """Check Action results""",
                """emoji""": True,
            },
            """url""": F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
        },
    }
    payload.append(action_button)
    lowerCamelCase      =		{
        """type""": """context""",
        """elements""": [
            {
                """type""": """plain_text""",
                """text""": F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
            }
        ],
    }
    payload.append(date_report)
  lowerCamelCase      =		client.chat_postMessage(channel="""#accelerate-ci-daily""", text=message, blocks=payload)
  lowerCamelCase      =		response.data["""ts"""]
  for failed_file in all_filesafailed:
    for test_location, test_failures in failed_file.items():
      # Keep only the first instance of the test name
      lowerCamelCase      =		""""""
      for i, row in enumerate(test_failures):
        if row[0] != test_class:
          lowerCamelCase      =		row[0]
        else:
          lowerCamelCase      =		""""""

      lowerCamelCase      =		{
          """type""": """section""",
          """text""": {
              """type""": """mrkdwn""",
              """text""": F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
          },
      }

      client.chat_postMessage(
          channel="""#accelerate-ci-daily""",
          thread_ts=ts,
          blocks=[payload],
      )









"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase      =		{
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

lowerCamelCase      =		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							create_model(
          "HTSAT-tiny"			,    "roberta"			,    lowerCAmelCase__			,    precision="fp32"			,    device="cuda:0" if torch.cuda.is_available() else "cpu"			,    enable_fusion=lowerCAmelCase__			,    fusion_type="aff_2d" if enable_fusion else None			,    )
      return model, model_cfg
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{}

      UpperCAmelCase_    				=							r".*sequential.(\d+).*"
      UpperCAmelCase_    				=							r".*_projection.(\d+).*"

      for key, value in state_dict.items():
            # check if any key needs to be modified
            for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
                  if key_to_modify in key:
                        UpperCAmelCase_    				=							key.replace(lowerCAmelCase__			,    lowerCAmelCase__   )

            if re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  # replace sequential layers with list
                  UpperCAmelCase_    				=							re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )   )

                  # Because in CLAP they use `nn.Sequential`...
                  UpperCAmelCase_    				=							1 if projecton_layer == 0 else 2

                  UpperCAmelCase_    				=							key.replace(f"""_projection.{projecton_layer}."""			,    f"""_projection.linear{transformers_projection_layer}."""   )

            if "audio" and "qkv" in key:
                  # split qkv into query key and value
                  UpperCAmelCase_    				=							value
                  UpperCAmelCase_    				=							mixed_qkv.size(0   ) // 3

                  UpperCAmelCase_    				=							mixed_qkv[:qkv_dim]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim : qkv_dim * 2]
                  UpperCAmelCase_    				=							mixed_qkv[qkv_dim * 2 :]

                  UpperCAmelCase_    				=							query_layer
                  UpperCAmelCase_    				=							key_layer
                  UpperCAmelCase_    				=							value_layer
            else:
                  UpperCAmelCase_    				=							value

      return model_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

      clap_model.eval()
      UpperCAmelCase_    				=							clap_model.state_dict()
      UpperCAmelCase_    				=							rename_state_dict(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							ClapModel(lowerCAmelCase__   )

      # ignore the spectrogram embedding layer
      model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )

      model.save_pretrained(lowerCAmelCase__   )
      transformers_config.save_pretrained(lowerCAmelCase__   )


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""")
  parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
  lowerCamelCase      =		parser.parse_args()

  convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




