"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDeiTForImageClassification,
			    TFDeiTForImageClassificationWithTeacher,
			    TFDeiTForMaskedImageModeling,
			    TFDeiTModel,
			)
			from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any=13  ,						snake_case__						:     int=30  ,						snake_case__						:     int=2  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     Dict=True  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     List[Any]=32  ,						snake_case__						:     List[str]=2  ,						snake_case__						:     Optional[Any]=4  ,						snake_case__						:     Optional[int]=37  ,						snake_case__						:     Tuple="gelu"  ,						snake_case__						:     str=0.1  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     int=10  ,						snake_case__						:     Dict=0.02  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     str=None  ,						snake_case__						:     List[Any]=2  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   image_size
							lowerCAmelCase__        =   patch_size
							lowerCAmelCase__        =   num_channels
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   scope
							lowerCAmelCase__        =   encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							lowerCAmelCase__        =   (image_size // patch_size) ** 2
							lowerCAmelCase__        =   num_patches + 2




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )

							lowerCAmelCase__        =   self.get_config()

							return config, pixel_values, labels




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return DeiTConfig(
							    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						is_decoder=snake_case__  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   TFDeiTModel(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(
							    result.reconstruction.shape  ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.reconstruction.shape  ,						(self.batch_size, 1, self.image_size, self.image_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.type_sequence_label_size
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_tf
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	int			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   TFDeiTModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						has_text_modality=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									self.assertIsInstance(model.get_input_embeddings()  ,						(tf.keras.layers.Layer)     )
									lowerCAmelCase__        =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case__  ,						tf.keras.layers.Dense     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									lowerCAmelCase__        =   inspect.signature(model.call     )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCAmelCase__        =   [*signature.parameters.keys()]

									lowerCAmelCase__        =   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]=False     ):
							lowerCAmelCase__        =   super()._prepare_for_class(snake_case__  ,						snake_case__  ,						return_labels=snake_case__     )

							if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
											del inputs_dict["labels"]

							return inputs_dict




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   TFDeiTModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
		return image






@require_tf
@require_vision
class       a_      (     unittest.TestCase			):




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )
							    if is_vision_available()
							    else None
							)




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )

							lowerCAmelCase__        =   self.default_image_processor
							lowerCAmelCase__        =   prepare_img()
							lowerCAmelCase__        =   image_processor(images=snake_case__  ,						return_tensors="""tf"""     )

							# forward pass
							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							lowerCAmelCase__        =   tf.TensorShape((1, 1000)     )
							self.assertEqual(outputs.logits.shape  ,						snake_case__     )

							lowerCAmelCase__        =   tf.constant([-1.0266, 0.1912, -1.2861]     )

							self.assertTrue(np.allclose(outputs.logits[0, :3]  ,						snake_case__  ,						atol=1E-4     )     )





"""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)




