

import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_UpperCamelCase 			=		WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Any					=					test_results.split(''' '''						)

      __lowerCAmelCase					:						List[Any]					=					0
      __lowerCAmelCase					:						int					=					0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      __lowerCAmelCase					:						List[Any]					=					expressions[-2] if '''=''' in expressions[-1] else expressions[-1]

      for i, expression in enumerate(lowercase__						):
            if "failed" in expression:
                  failed += int(expressions[i - 1]						)
            if "passed" in expression:
                  success += int(expressions[i - 1]						)

      return failed, success, time_spent







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						str					=					{}
      __lowerCAmelCase					:						Dict					=					None
      __lowerCAmelCase					:						Any					=					False
      for line in failures_short_lines.split('''\n'''						):
            if re.search(r'''_ \[doctest\]'''   ,	lowercase__						):
                  __lowerCAmelCase					:						int					=					True
                  __lowerCAmelCase					:						Optional[Any]					=					line.split(''' '''						)[2]
            elif in_error and not line.split(''' '''						)[0].isdigit():
                  __lowerCAmelCase					:						Tuple					=					line
                  __lowerCAmelCase					:						Optional[Any]					=					False

      return failures







class   __lowercase      :
 def __init__(							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					title

       __lowerCAmelCase					:						Optional[int]					=					doc_test_results['''time_spent'''].split(''','''					)[0]
       __lowerCAmelCase					:						Any					=					doc_test_results['''success''']
       __lowerCAmelCase					:						Optional[int]					=					doc_test_results['''failures''']
       __lowerCAmelCase					:						Union[str, Any]					=					self.n_success + self.n_failures

       # Failures and success of the modeling tests
       __lowerCAmelCase					:						Tuple					=					doc_test_results
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					[self._time_spent]
       __lowerCAmelCase					:						Optional[Any]					=					0

       for time in time_spent:
             __lowerCAmelCase					:						int					=					time.split(''':'''					)

             # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
             if len(A_					) == 1:
                   __lowerCAmelCase					:						List[Any]					=					[0, 0, time_parts[0]]

             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Union[str, Any]					=					int(time_parts[0]					), int(time_parts[1]					), float(time_parts[2]					)
             total_secs += hours * 3600 + minutes * 60 + seconds

       __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Union[str, Any]					=					total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
       return f"""{int(A_					)}h{int(A_					)}m{int(A_					)}s"""
 @property
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
 @property
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       return {
           "type": "section",
           "text": {
               "type": "plain_text",
               "text": f"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
               "emoji": True,
           },
           "accessory": {
               "type": "button",
               "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
               "url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
           },
       }
 @property
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       return {
           "type": "section",
           "text": {
               "type": "plain_text",
               "text": (
                   f"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
                   f""" {self.time}."""
               ),
               "emoji": True,
           },
           "accessory": {
               "type": "button",
               "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
               "url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
           },
       }
 @property
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					40
       __lowerCAmelCase					:						Optional[Any]					=					{k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(A_      ,							A_					)}

       __lowerCAmelCase					:						Dict					=					''''''
       for category, failures in category_failures.items():
             if len(A_					) == 0:
                   continue

             if report != "":
                   report += "\n\n"

             report += f"""*{category} failures*:""".ljust(line_length // 2					).rjust(line_length // 2					) + "\n"
             report += "`"
             report += "`\n`".join(A_					)
             report += "`"

       return {
           "type": "section",
           "text": {
               "type": "mrkdwn",
               "text": f"""The following examples had failures:\n\n\n{report}\n""",
           },
       }
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					[self.header]

       if self.n_failures > 0:
             blocks.append(self.failures					)

       if self.n_failures > 0:
             blocks.extend([self.category_failures]					)

       if self.n_failures == 0:
             blocks.append(self.no_failures					)

       return json.dumps(A_					)
 @staticmethod
 def 	UpperCamelCase__  (							)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					[
           {
               '''type''': '''section''',
               '''text''': {
                   '''type''': '''plain_text''',
                   '''text''': '''There was an issue running the tests.''',
               },
               '''accessory''': {
                   '''type''': '''button''',
                   '''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
                   '''url''': f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
               },
           }
       ]

       print('''Sending the following payload'''					)
       print(json.dumps({'''blocks''': json.loads(A_					)}					)					)

       client.chat_postMessage(
           channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']      ,							text='''There was an issue running the tests.'''      ,							blocks=A_      ,							)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       print('''Sending the following payload'''					)
       print(json.dumps({'''blocks''': json.loads(self.payload					)}					)					)

       __lowerCAmelCase					:						List[Any]					=					f"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else '''All tests passed.'''

       __lowerCAmelCase					:						Optional[Any]					=					client.chat_postMessage(
           channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']      ,							blocks=self.payload      ,							text=A_      ,							)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					''''''
       for key, value in failures.items():
             __lowerCAmelCase					:						Tuple					=					value[:200] + ''' [Truncated]''' if len(A_					) > 250 else value
             failures_text += f"""*{key}*\n_{value}_\n\n"""

       __lowerCAmelCase					:						Union[str, Any]					=					job_name
       __lowerCAmelCase					:						Dict					=					{'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

       if job_link is not None:
             __lowerCAmelCase					:						List[Any]					=					{
                 '''type''': '''button''',
                 '''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
                 '''url''': job_link,
             }

       return [
           {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
           content,
           {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
       ]





 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       if self.thread_ts is None:
             raise ValueError('''Can only post reply if a post has been made.'''					)

       __lowerCAmelCase					:						Optional[Any]					=					self.doc_test_results.pop('''job_link'''					)
       self.doc_test_results.pop('''failures'''					)
       self.doc_test_results.pop('''success'''					)
       self.doc_test_results.pop('''time_spent'''					)

       __lowerCAmelCase					:						List[str]					=					sorted(self.doc_test_results.items()      ,							key=lambda A_					: t[0]					)
       for job, job_result in sorted_dict:
             if len(job_result['''failures''']					):
                   __lowerCAmelCase					:						List[Any]					=					f"""*Num failures* :{len(job_result["failed"]					)} \n"""
                   __lowerCAmelCase					:						List[str]					=					job_result['''failures''']
                   __lowerCAmelCase					:						Any					=					self.get_reply_blocks(A_      ,							A_      ,							A_      ,							text=A_					)

                   print('''Sending the following reply'''					)
                   print(json.dumps({'''blocks''': blocks}					)					)

                   client.chat_postMessage(
                       channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']      ,							text=f"""Results for {job}"""      ,							blocks=A_      ,							thread_ts=self.thread_ts['''ts''']      ,							)

                   time.sleep(1					)







def 		_lowercase (     ):
      __lowerCAmelCase					:						Dict					=					os.environ['''GITHUB_RUN_ID''']
      __lowerCAmelCase					:						Optional[int]					=					f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
      __lowerCAmelCase					:						Tuple					=					requests.get(lowercase__						).json()
      __lowerCAmelCase					:						List[Any]					=					{}

      try:
            jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)
            __lowerCAmelCase					:						List[str]					=					math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0						)

            for i in range(lowercase__						):
                  __lowerCAmelCase					:						Optional[int]					=					requests.get(url + f"""&page={i + 2}"""						).json()
                  jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)

            return jobs
      except Exception as e:
            print('''Unknown error, could not fetch links.'''   ,	lowercase__						)

      return {}







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					{}

      if os.path.exists(lowercase__						):
            __lowerCAmelCase					:						Optional[int]					=					os.listdir(lowercase__						)
            for file in files:
                  try:
                        with open(os.path.join(lowercase__   ,	lowercase__						)   ,	encoding='''utf-8'''						) as f:
                              __lowerCAmelCase					:						Optional[int]					=					f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(f"""Could not open {os.path.join(lowercase__   ,	lowercase__						)}."""						) from e

      return _artifact







def 		_lowercase (     ):







      class   __lowercase      :
       def __init__(							self      ,							A_					)					->Union[str, Any]:



             '''simple docstring'''





             __lowerCAmelCase					:						Optional[int]					=					name
             __lowerCAmelCase					:						Any					=					[]
       def __str__(							self					)					->Optional[int]:



             '''simple docstring'''





             return self.name





       def 	UpperCamelCase__  (							self      ,							A_					)					->Any:



             '''simple docstring'''





             self.paths.append({'''name''': self.name, '''path''': path}					)



      __lowerCAmelCase					:						Dict[str, Artifact]					=					{}

      __lowerCAmelCase					:						Union[str, Any]					=					filter(os.path.isdir   ,	os.listdir()						)
      for directory in directories:
            __lowerCAmelCase					:						List[Any]					=					directory
            if artifact_name not in _available_artifacts:
                  __lowerCAmelCase					:						Optional[int]					=					Artifact(lowercase__						)

                  _available_artifacts[artifact_name].add_path(lowercase__						)

      return _available_artifacts


if __name__ == "__main__":
 _UpperCamelCase 			=		get_job_links()
 _UpperCamelCase 			=		retrieve_available_artifacts()

 _UpperCamelCase 			=		collections.OrderedDict(
     [
         ("*.py", "API Examples"),
         ("*.md", "MD Examples"),
     ]
 )

 # This dict will contain all the information relative to each doc test category:
 # - failed: list of failed tests
 # - failures: dict in the format 'test': 'error_message'
 _UpperCamelCase 			=		{
     v: {
         "failed": [],
         "failures": {},
     }
     for v in docs.values()
 }

 # Link to the GitHub Action job
 _UpperCamelCase 			=		github_actions_job_links.get("run_doctests")

 _UpperCamelCase 			=		available_artifacts["doc_tests_gpu_test_reports"].paths[0]
 _UpperCamelCase 			=		retrieve_artifact(artifact_path["name"])
 if "stats" in artifact:
  _UpperCamelCase			,			_UpperCamelCase			,			_UpperCamelCase 			=		handle_test_results(artifact["stats"])
  _UpperCamelCase 			=		failed
  _UpperCamelCase 			=		success
  _UpperCamelCase 			=		time_spent[1:-1] + ", "

  _UpperCamelCase 			=		extract_first_line_failure(artifact["failures_short"])
  for line in artifact["summary_short"].split("\n"):
   if re.search("FAILED", line):
    _UpperCamelCase 			=		line.replace("FAILED ", "")
    _UpperCamelCase 			=		line.split()[0].replace("\n", "")

    if "::" in line:
     _UpperCamelCase			,			_UpperCamelCase 			=		line.split("::")
    else:
     _UpperCamelCase			,			_UpperCamelCase 			=		line, line

    for file_regex in docs.keys():
     if fnmatch(file_path, file_regex):
      _UpperCamelCase 			=		docs[file_regex]
      doc_test_results[category]["failed"].append(test)

      _UpperCamelCase 			=		all_failures[test] if test in all_failures else "N/A"
      _UpperCamelCase 			=		failure
      break

 _UpperCamelCase 			=		Message("🤗 Results of the doc tests.", doc_test_results)
 message.post()
 message.post_reply()





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
_UpperCamelCase 			=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						List[str]					=					{}
      with open(lowercase__   ,	'''r'''						) as file:
            for line_number, line in enumerate(lowercase__						):
                  __lowerCAmelCase					:						Any					=					line.strip()
                  if line:
                        __lowerCAmelCase					:						Dict					=					line.split()
                        __lowerCAmelCase					:						str					=					line_number
                        __lowerCAmelCase					:						List[str]					=					words[0]
                        __lowerCAmelCase					:						Any					=					value
      return result







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      for attribute in key.split('''.'''						):
            __lowerCAmelCase					:						List[Any]					=					getattr(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						Tuple					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						List[Any]					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						str					=					getattr(lowercase__   ,	lowercase__						).shape
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						Dict					=					hf_pointer
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						str					=					shape_pointer.shape

            # let's reduce dimension
            __lowerCAmelCase					:						Any					=					value[0]
      else:
            __lowerCAmelCase					:						str					=					hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
                f""" {value.shape} for {full_name}"""						)

      if weight_type == "weight":
            __lowerCAmelCase					:						Union[str, Any]					=					value
      elif weight_type == "weight_g":
            __lowerCAmelCase					:						List[str]					=					value
      elif weight_type == "weight_v":
            __lowerCAmelCase					:						int					=					value
      elif weight_type == "bias":
            __lowerCAmelCase					:						Union[str, Any]					=					value
      elif weight_type == "param":
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						Tuple					=					value
      else:
            __lowerCAmelCase					:						Any					=					value

      logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						str					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						int					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						Tuple					=					'''.'''.join([key, weight_type]						)
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						List[str]					=					'''.'''.join([key, hf_param_name]						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					key

      __lowerCAmelCase					:						Union[str, Any]					=					value if '''lm_head''' in full_key else value[0]


_UpperCamelCase 			=		{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None						):
      __lowerCAmelCase					:						Any					=					False
      for key, mapped_key in MAPPING.items():
            __lowerCAmelCase					:						Tuple					=					'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                  __lowerCAmelCase					:						Optional[Any]					=					True
                  if "*" in mapped_key:
                        __lowerCAmelCase					:						List[str]					=					name.split(lowercase__						)[0].split('''.'''						)[-2]
                        __lowerCAmelCase					:						Dict					=					mapped_key.replace('''*'''   ,	lowercase__						)
                  if "weight_g" in name:
                        __lowerCAmelCase					:						List[Any]					=					'''weight_g'''
                  elif "weight_v" in name:
                        __lowerCAmelCase					:						List[Any]					=					'''weight_v'''
                  elif "bias" in name:
                        __lowerCAmelCase					:						Any					=					'''bias'''
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __lowerCAmelCase					:						int					=					'''weight'''
                  else:
                        __lowerCAmelCase					:						Any					=					None
                  if hf_dict is not None:
                        rename_dict(lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)
                  else:
                        set_recursively(lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)
                  return is_used
      return is_used







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					[]
      __lowerCAmelCase					:						Optional[Any]					=					fairseq_model.state_dict()

      __lowerCAmelCase					:						Tuple					=					hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCAmelCase					:						Any					=					False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
                  __lowerCAmelCase					:						int					=					True
            else:
                  __lowerCAmelCase					:						Dict					=					load_wavaveca_layer(lowercase__   ,	lowercase__   ,	lowercase__						)
            if not is_used:
                  unused_weights.append(lowercase__						)

      logger.warning(f"""Unused weights: {unused_weights}"""						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					full_name.split('''conv_layers.'''						)[-1]
      __lowerCAmelCase					:						List[str]					=					name.split('''.'''						)
      __lowerCAmelCase					:						Any					=					int(items[0]						)
      __lowerCAmelCase					:						str					=					int(items[1]						)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""						)
                  __lowerCAmelCase					:						List[str]					=					value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""						)
                  __lowerCAmelCase					:						List[str]					=					value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""						)
                  __lowerCAmelCase					:						List[str]					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""						)
                  __lowerCAmelCase					:						Optional[int]					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      else:
            unused_weights.append(lowercase__						)







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=True   ,	lowercase__=False						):
      if config_path is not None:
            __lowerCAmelCase					:						Union[str, Any]					=					WavaVecaConfig.from_pretrained(lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaConfig()

      if is_seq_class:
            __lowerCAmelCase					:						Optional[Any]					=					read_txt_into_dict(lowercase__						)
            __lowerCAmelCase					:						int					=					idalabel
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaForSequenceClassification(lowercase__						)
            __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
            feature_extractor.save_pretrained(lowercase__						)

      elif is_finetuned:
            if dict_path:
                  __lowerCAmelCase					:						List[str]					=					Dictionary.load(lowercase__						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __lowerCAmelCase					:						List[Any]					=					target_dict.pad_index
                  __lowerCAmelCase					:						List[Any]					=					target_dict.bos_index
                  __lowerCAmelCase					:						Optional[int]					=					target_dict.eos_index
                  __lowerCAmelCase					:						Any					=					len(target_dict.symbols						)
                  __lowerCAmelCase					:						Union[str, Any]					=					os.path.join(lowercase__   ,	'''vocab.json'''						)
                  if not os.path.isdir(lowercase__						):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase__						)						)
                        return
                  os.makedirs(lowercase__   ,	exist_ok=lowercase__						)
                  __lowerCAmelCase					:						Optional[int]					=					target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  __lowerCAmelCase					:						List[str]					=					0
                  __lowerCAmelCase					:						int					=					1
                  with open(lowercase__   ,	'''w'''   ,	encoding='''utf-8'''						) as vocab_handle:
                        json.dump(lowercase__   ,	lowercase__						)
                  __lowerCAmelCase					:						Dict					=					WavaVecaCTCTokenizer(
                      lowercase__   ,	unk_token=target_dict.unk_word   ,	pad_token=target_dict.pad_word   ,	bos_token=target_dict.bos_word   ,	eos_token=target_dict.eos_word   ,	word_delimiter_token='''|'''   ,	do_lower_case=lowercase__   ,	)
                  __lowerCAmelCase					:						List[str]					=					True if config.feat_extract_norm == '''layer''' else False
                  __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                      feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
                  __lowerCAmelCase					:						List[Any]					=					WavaVecaProcessor(feature_extractor=lowercase__   ,	tokenizer=lowercase__						)
                  processor.save_pretrained(lowercase__						)

            __lowerCAmelCase					:						str					=					WavaVecaForCTC(lowercase__						)
      else:
            __lowerCAmelCase					:						Any					=					WavaVecaForPreTraining(lowercase__						)

      if is_finetuned or is_seq_class:
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[int]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					argparse.Namespace(task='''audio_pretraining'''						)
            __lowerCAmelCase					:						str					=					fairseq.tasks.setup_task(lowercase__						)

            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,	task=lowercase__						)

      __lowerCAmelCase					:						int					=					model[0].eval()

      recursively_load_weights(lowercase__   ,	lowercase__   ,	not is_finetuned						)

      hf_wavavec.save_pretrained(lowercase__						)


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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 _UpperCamelCase 			=		parser.parse_args()

 _UpperCamelCase 			=		not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )


