"""simple docstring"""




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


__lowerCAmelCase    :							List[str]													=						WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   test_results.split(""" """     )

		lowerCAmelCase__        =   0
		lowerCAmelCase__        =   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__        =   expressions[-2] if """=""" in expressions[-1] else expressions[-1]

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

		return failed, success, time_spent







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {}
		lowerCAmelCase__        =   None
		lowerCAmelCase__        =   False
		for line in failures_short_lines.split("""\n"""     ):
				if re.search(r"""_ \[doctest\]"""			,  lowerCamelCase__     ):
						lowerCAmelCase__        =   True
						lowerCAmelCase__        =   line.split(""" """     )[2]
				elif in_error and not line.split(""" """     )[0].isdigit():
						lowerCAmelCase__        =   line
						lowerCAmelCase__        =   False

		return failures

class       a_      :




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     str  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   title

							lowerCAmelCase__        =   doc_test_results["""time_spent"""].split(""","""     )[0]
							lowerCAmelCase__        =   doc_test_results["""success"""]
							lowerCAmelCase__        =   doc_test_results["""failures"""]
							lowerCAmelCase__        =   self.n_success + self.n_failures

							# Failures and success of the modeling tests
							lowerCAmelCase__        =   doc_test_results




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   [self._time_spent]
							lowerCAmelCase__        =   0

							for time in time_spent:
									lowerCAmelCase__        =   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(snake_case__     ) == 1:
											lowerCAmelCase__        =   [0, 0, time_parts[0]]

									lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   int(time_parts[0]     ), int(time_parts[1]     ), float(time_parts[2]     )
									total_secs += hours * 3600 + minutes * 60 + seconds

							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
							return F"""{int(snake_case__     )}h{int(snake_case__     )}m{int(snake_case__     )}s"""




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							return {"type": "header", "text": {"type": "plain_text", "text": self.title}}




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   40
							lowerCAmelCase__        =   {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(snake_case__  ,						snake_case__     )}

							lowerCAmelCase__        =   """"""
							for category, failures in category_failures.items():
									if len(snake_case__     ) == 0:
											continue

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

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

							return {
							    "type": "section",
							    "text": {
							        "type": "mrkdwn",
							        "text": F"""The following examples had failures:\n\n\n{report}\n""",
							    },
							}




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   [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(snake_case__     )




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	):
							lowerCAmelCase__        =   [
							    {
							        """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(snake_case__     )}     )     )

							client.chat_postMessage(
							    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]  ,						text="""There was an issue running the tests."""  ,						blocks=snake_case__  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							print("""Sending the following payload"""     )
							print(json.dumps({"""blocks""": json.loads(self.payload     )}     )     )

							lowerCAmelCase__        =   F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else """All tests passed."""

							lowerCAmelCase__        =   client.chat_postMessage(
							    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]  ,						blocks=self.payload  ,						text=snake_case__  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     str  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__        =   """"""
							for key, value in failures.items():
									lowerCAmelCase__        =   value[:200] + """ [Truncated]""" if len(snake_case__     ) > 250 else value
									failures_text += F"""*{key}*\n_{value}_\n\n"""

							lowerCAmelCase__        =   job_name
							lowerCAmelCase__        =   {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

							if job_link is not None:
									lowerCAmelCase__        =   {
									    """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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							if self.thread_ts is None:
									raise ValueError("""Can only post reply if a post has been made."""     )

							lowerCAmelCase__        =   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__        =   sorted(self.doc_test_results.items()  ,						key=lambda snake_case__     : t[0]     )
							for job, job_result in sorted_dict:
									if len(job_result["""failures"""]     ):
											lowerCAmelCase__        =   F"""*Num failures* :{len(job_result['failed']     )} \n"""
											lowerCAmelCase__        =   job_result["""failures"""]
											lowerCAmelCase__        =   self.get_reply_blocks(snake_case__  ,						snake_case__  ,						snake_case__  ,						text=snake_case__     )

											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=snake_case__  ,						thread_ts=self.thread_ts["""ts"""]  ,						)

											time.sleep(1     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   os.environ["""GITHUB_RUN_ID"""]
		lowerCAmelCase__        =   f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
		lowerCAmelCase__        =   requests.get(lowerCamelCase__     ).json()
		lowerCAmelCase__        =   {}

		try:
				jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}     )
				lowerCAmelCase__        =   math.ceil((result["""total_count"""] - 100) / 100     )

				for i in range(lowerCamelCase__     ):
						lowerCAmelCase__        =   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."""			,  lowerCamelCase__     )

		return {}







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {}

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

		return _artifact







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		class       a_      :




							def __init__(	self						:     Optional[int]  ,						snake_case__						:     str     ):
									lowerCAmelCase__        =   name
									lowerCAmelCase__        =   []




							def __str__(	self						:     List[str]     ):
									return self.name




							def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     str     ):
									self.paths.append({"""name""": self.name, """path""": path}     )
		lowerCAmelCase__        =   {}

		lowerCAmelCase__        =   filter(os.path.isdir			,  os.listdir()     )
		for directory in directories:
				lowerCAmelCase__        =   directory
				if artifact_name not in _available_artifacts:
						lowerCAmelCase__        =   Artifact(lowerCamelCase__     )

						_available_artifacts[artifact_name].add_path(lowerCamelCase__     )

		return _available_artifacts


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						get_job_links()
			__lowerCAmelCase    :							str													=						retrieve_available_artifacts()

			__lowerCAmelCase    :							Optional[int]													=						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'
			__lowerCAmelCase    :							Optional[int]													=						{
			    v: {
			        "failed": [],
			        "failures": {},
			    }
			    for v in docs.values()
			}

			# Link to the GitHub Action job
			__lowerCAmelCase    :							Optional[int]													=						github_actions_job_links.get("run_doctests")

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

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

												if "::" in line:
															__lowerCAmelCase							,	__lowerCAmelCase    :							List[Any]													=						line.split("::")
												else:
															__lowerCAmelCase							,	__lowerCAmelCase    :							Optional[Any]													=						line, line

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

																		__lowerCAmelCase    :							List[str]													=						all_failures[test] if test in all_failures else "N/A"
																		__lowerCAmelCase    :							Optional[int]													=						failure
																		break

			__lowerCAmelCase    :							Optional[Any]													=						Message("🤗 Results of the doc tests.", doc_test_results)
			message.post()
			message.post_reply()





"""simple docstring"""




import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
			raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
			raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Any													=						"The Nymphenburg Palace is a beautiful palace in Munich!"







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {
		    """attention_cell""": """multi_head""",
		    """num_layers""": 4,
		    """units""": 1024,
		    """hidden_size""": 768,
		    """max_length""": 512,
		    """num_heads""": 8,
		    """scaled""": True,
		    """dropout""": 0.1,
		    """use_residual""": True,
		    """embed_size""": 1024,
		    """embed_dropout""": 0.1,
		    """word_embed""": None,
		    """layer_norm_eps""": 1e-5,
		    """token_type_vocab_size""": 2,
		}

		lowerCAmelCase__        =   bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowerCAmelCase__        =   BERTEncoder(
		    attention_cell=predefined_args["""attention_cell"""]			,  num_layers=predefined_args["""num_layers"""]			,  units=predefined_args["""units"""]			,  hidden_size=predefined_args["""hidden_size"""]			,  max_length=predefined_args["""max_length"""]			,  num_heads=predefined_args["""num_heads"""]			,  scaled=predefined_args["""scaled"""]			,  dropout=predefined_args["""dropout"""]			,  output_attention=lowerCamelCase__			,  output_all_encodings=lowerCamelCase__			,  use_residual=predefined_args["""use_residual"""]			,  activation=predefined_args.get("""activation"""			,  """gelu"""     )			,  layer_norm_eps=predefined_args.get("""layer_norm_eps"""			,  lowerCamelCase__     )			,  )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase__        =   """openwebtext_ccnews_stories_books_cased"""

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase__        =   os.path.join(get_home_dir()			,  """models"""     )
		lowerCAmelCase__        =   _load_vocab(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  cls=lowerCamelCase__     )

		lowerCAmelCase__        =   nlp.model.BERTModel(
		    lowerCamelCase__			,  len(lowerCamelCase__     )			,  units=predefined_args["""units"""]			,  embed_size=predefined_args["""embed_size"""]			,  embed_dropout=predefined_args["""embed_dropout"""]			,  word_embed=predefined_args["""word_embed"""]			,  use_pooler=lowerCamelCase__			,  use_token_type_embed=lowerCamelCase__			,  token_type_vocab_size=predefined_args["""token_type_vocab_size"""]			,  use_classifier=lowerCamelCase__			,  use_decoder=lowerCamelCase__			,  )

		original_bort.load_parameters(lowerCamelCase__			,  cast_dtype=lowerCamelCase__			,  ignore_extra=lowerCamelCase__     )
		lowerCAmelCase__        =   original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase__        =   {
		    """architectures""": ["""BertForMaskedLM"""],
		    """attention_probs_dropout_prob""": predefined_args["""dropout"""],
		    """hidden_act""": """gelu""",
		    """hidden_dropout_prob""": predefined_args["""dropout"""],
		    """hidden_size""": predefined_args["""embed_size"""],
		    """initializer_range""": 0.02,
		    """intermediate_size""": predefined_args["""hidden_size"""],
		    """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
		    """max_position_embeddings""": predefined_args["""max_length"""],
		    """model_type""": """bort""",
		    """num_attention_heads""": predefined_args["""num_heads"""],
		    """num_hidden_layers""": predefined_args["""num_layers"""],
		    """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
		    """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
		    """vocab_size""": len(lowerCamelCase__     ),
		}

		lowerCAmelCase__        =   BertConfig.from_dict(lowerCamelCase__     )
		lowerCAmelCase__        =   BertForMaskedLM(lowerCamelCase__     )
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(lowerCamelCase__     ) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()     )     )

		# Check param shapes and map new HF param back
		def check_and_map_params(lowerCamelCase__			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   hf_param.shape

				lowerCAmelCase__        =   to_torch(params[gluon_param]     )
				lowerCAmelCase__        =   gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

				return gluon_param

		lowerCAmelCase__        =   check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight			,  """word_embed.0.weight"""     )
		lowerCAmelCase__        =   check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight			,  """encoder.position_weight"""     )
		lowerCAmelCase__        =   check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias			,  """encoder.layer_norm.beta"""     )
		lowerCAmelCase__        =   check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight			,  """encoder.layer_norm.gamma"""     )

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowerCAmelCase__        =   torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data     )

		for i in range(hf_bort_config.num_hidden_layers     ):
				lowerCAmelCase__        =   hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowerCAmelCase__        =   layer.attention.self

				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.key.bias.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""     )

				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.key.weight.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.query.bias.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.query.weight.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.value.bias.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_attn.value.weight.data			,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""     )

				# self attention output
				lowerCAmelCase__        =   layer.attention.output

				lowerCAmelCase__        =   check_and_map_params(
				    self_output.dense.bias			,  f"""encoder.transformer_cells.{i}.proj.bias"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_output.dense.weight			,  f"""encoder.transformer_cells.{i}.proj.weight"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_output.LayerNorm.bias			,  f"""encoder.transformer_cells.{i}.layer_norm.beta"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    self_output.LayerNorm.weight			,  f"""encoder.transformer_cells.{i}.layer_norm.gamma"""     )

				# intermediate
				lowerCAmelCase__        =   layer.intermediate

				lowerCAmelCase__        =   check_and_map_params(
				    intermediate.dense.bias			,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    intermediate.dense.weight			,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""     )

				# output
				lowerCAmelCase__        =   layer.output

				lowerCAmelCase__        =   check_and_map_params(
				    bert_output.dense.bias			,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    bert_output.dense.weight			,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    bert_output.LayerNorm.bias			,  f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""     )
				lowerCAmelCase__        =   check_and_map_params(
				    bert_output.LayerNorm.weight			,  f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""     )

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowerCAmelCase__        =   RobertaTokenizer.from_pretrained("""roberta-base"""     )

		lowerCAmelCase__        =   tokenizer.encode_plus(lowerCamelCase__     )["""input_ids"""]

		# Get gluon output
		lowerCAmelCase__        =   mx.nd.array([input_ids]     )
		lowerCAmelCase__        =   original_bort(inputs=lowerCamelCase__			,  token_types=[]     )

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(lowerCamelCase__     )
		lowerCAmelCase__        =   BertModel.from_pretrained(lowerCamelCase__     )
		hf_bort_model.eval()

		lowerCAmelCase__        =   tokenizer.encode_plus(lowerCamelCase__			,  return_tensors="""pt"""     )
		lowerCAmelCase__        =   hf_bort_model(**lowerCamelCase__     )[0]

		lowerCAmelCase__        =   output_gluon[0].asnumpy()
		lowerCAmelCase__        =   output_hf[0].detach().numpy()

		lowerCAmelCase__        =   np.max(np.abs(hf_layer - gluon_layer     )     ).item()
		lowerCAmelCase__        =   np.allclose(lowerCamelCase__			,  lowerCamelCase__			,  atol=1e-3     )

		if success:
				print("""✔️ Both model do output the same tensors"""     )
		else:
				print("""❌ Both model do **NOT** output the same tensors"""     )
				print("""Absolute difference is:"""			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							str													=						parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




