




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

from tabulate import DataRow, TableFormat, tabulate


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


UpperCAmelCase_		:				Tuple									=     []
UpperCAmelCase_		:				List[str]									=     []

UpperCAmelCase_		:				Optional[Any]									=     {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

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

UpperCAmelCase_		:				Optional[int]									=     0
for log in Path().glob("*.log"):
							UpperCAmelCase_		:				str									=     0
							with open(log, "r") as f:
														for line in f:
																					UpperCAmelCase_		:				str									=     json.loads(line)
																					if line.get("nodeid", "") != "":
																												UpperCAmelCase_		:				str									=     line["nodeid"]
																												if line.get("duration", None) is not None:
																																			UpperCAmelCase_		:				Optional[Any]									=     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])
							UpperCAmelCase_		:				Dict									=     []
							log.unlink()

UpperCAmelCase_		:				Union[str, Any]									=     ""
UpperCAmelCase_		:				Optional[Any]									=     []
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"
																					UpperCAmelCase_		:				Dict									=     []
																					UpperCAmelCase_		:				List[str]									=     {}
																					for test in failed_tests:
																												UpperCAmelCase_		:				Union[str, Any]									=     test[0].split("::")
																												UpperCAmelCase_		:				Tuple									=     data[0].split("/")[-1]
																												if data[0] not in filesafailed:
																																			UpperCAmelCase_		:				Optional[Any]									=     [data[1:]]
																												else:
																																			filesafailed[data[0]] += [data[1:]]
																												failed_table.append(data)

																					UpperCAmelCase_		:				List[str]									=     [test[0] for test in failed_table]
																					UpperCAmelCase_		:				Union[str, Any]									=     list(set(files))
																					# Count number of instances in failed_tests
																					UpperCAmelCase_		:				Optional[int]									=     []
																					for file in individual_files:
																												table.append([file, len(filesafailed[file])])

																					UpperCAmelCase_		:				str									=     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) > 3000:
														UpperCAmelCase_		:				int									=     "Too many failed tests, please see the full report in the Action results."
														UpperCAmelCase_		:				List[Any]									=     len(err) + 10
														UpperCAmelCase_		:				Optional[Any]									=     message[: 3000 - offset] + F"""\n...\n```\n{err}"""
							print(F"""### {message}""")
else:
							UpperCAmelCase_		:				int									=     "No failed tests! 🤗"
							print(F"""## {message}""")
							payload.append(no_error_payload)

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

							UpperCAmelCase_		:				List[Any]									=     WebClient(token=os.environ["SLACK_API_TOKEN"])
							if message != "No failed tests! 🤗":
														UpperCAmelCase_		:				Tuple									=     {
														    "type": "section",
														    "text": {
														        "type": "mrkdwn",
														        "text": message,
														    },
														}
														payload.append(md_report)
														UpperCAmelCase_		:				Optional[Any]									=     {
														    "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)
														UpperCAmelCase_		:				List[str]									=     {
														    "type": "context",
														    "elements": [
														        {
														            "type": "plain_text",
														            "text": F"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
														        }
														    ],
														}
														payload.append(date_report)
							UpperCAmelCase_		:				List[Any]									=     client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
							UpperCAmelCase_		:				Union[str, Any]									=     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
																					UpperCAmelCase_		:				Tuple									=     ""
																					for i, row in enumerate(test_failures):
																												if row[0] != test_class:
																																			UpperCAmelCase_		:				int									=     row[0]
																												else:
																																			UpperCAmelCase_		:				Union[str, Any]									=     ""

																					UpperCAmelCase_		:				List[str]									=     {
																					    "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],
																					)










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[int]									=     {
    "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",
}

UpperCAmelCase_		:				Optional[int]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Any								=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 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	Union[str, Any]								=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:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

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

												__magic_name__	:	List[Any]								=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
												__magic_name__	:	Dict								=value
												__magic_name__	:	List[str]								=mixed_qkv.size(0   ) // 3

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=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__":
							UpperCAmelCase_		:				Optional[int]									=     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")
							UpperCAmelCase_		:				Dict									=     parser.parse_args()

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




