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

from tabulate import DataRow, TableFormat, tabulate


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


__A							=[]
__A							=[]

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

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

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

__A							=''''''
__A							=[]
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"
									__A							=[]
									__A							={}
									for test in failed_tests:
												__A							=test[0].split('''::''')
												__A							=data[0].split('''/''')[-1]
												if data[0] not in filesafailed:
															__A							=[data[1:]]
												else:
															filesafailed[data[0]] += [data[1:]]
												failed_table.append(data)

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

									__A							=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_0_0_0:
						__A							='''Too many failed tests, please see the full report in the Action results.'''
						__A							=len(err) + 1_0
						__A							=message[: 3_0_0_0 - offset] + F"""\n...\n```\n{err}"""
			print(F"""### {message}""")
else:
			__A							='''No failed tests! 🤗'''
			print(F"""## {message}""")
			payload.append(no_error_payload)

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

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

									__A							={
									    '''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

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase													={
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		list(s_dict.keys()				)
      for key in keys:
            A				  =		r""".*/layers_(\d+)"""
            A				  =		key
            if re.match(_a     ,   _a				):
                  A				  =		re.sub(r"""layers_(\d+)"""     ,   r"""block/\1/layer"""     ,   _a				)

            A				  =		r"""(encoder|decoder)\/"""

            if re.match(_a     ,   _a				):
                  A				  =		re.match(_a     ,   _a				).groups()
                  if groups[0] == "encoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/1/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/1/layer_norm/"""     ,   _a				)

                  elif groups[0] == "decoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/2/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/2/layer_norm/"""     ,   _a				)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        A				  =		new_key.replace(_a     ,   _a				)

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()				):
            if "expert" in key:
                  A				  =		s_dict[key].shape[0]
                  A				  =		s_dict[key]
                  for idx in range(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}



def   _A					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

      A				  =		re.findall(r"""(.*) = ([0-9.]*)"""     ,   _a				)
      A				  =		{}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  A				  =		float(_a				) if """.""" in value else int(_a				)

      A				  =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,   _a				)[0]
      A				  =		str(activation[1]				)

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		unflatten_dict(_a     ,   sep="""/"""				)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(_a     ,   _a				)

      print(f'Save PyTorch model to {pytorch_dump_path}'				)
      pt_model.save_pretrained(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  UpperCAmelCase													=parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )




