


'''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()
UpperCamelCase__          = logging.get_logger(__name__)

UpperCamelCase__          = '''The Nymphenburg Palace is a beautiful palace in Munich!'''





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[str]:
    UpperCAmelCase__     :						Optional[Any]						=							{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 10_24,
        '''hidden_size''': 7_68,
        '''max_length''': 5_12,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 10_24,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1E-5,
        '''token_type_vocab_size''': 2,
    }

    UpperCAmelCase__     :						int						=							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
    UpperCAmelCase__     :						Tuple						=							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
    UpperCAmelCase__     :						Union[str, Any]						=							'''openwebtext_ccnews_stories_books_cased'''

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

    UpperCAmelCase__     :						List[str]						=							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__		)
    UpperCAmelCase__     :						Optional[Any]						=							original_bort._collect_params_with_prefix()

    # Build our config 🤗
    UpperCAmelCase__     :						List[str]						=							{
        '''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''': 0.0_2,
        '''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__		),
    }

    UpperCAmelCase__     :						Tuple						=							BertConfig.from_dict(lowerCAmelCase__		)
    UpperCAmelCase__     :						Optional[Any]						=							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__		):
        UpperCAmelCase__     :						Optional[Any]						=							hf_param.shape

        UpperCAmelCase__     :						Optional[int]						=							to_torch(params[gluon_param]		)
        UpperCAmelCase__     :						List[str]						=							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

    UpperCAmelCase__     :						Dict						=							check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight    ,   '''word_embed.0.weight'''		)
    UpperCAmelCase__     :						Union[str, Any]						=							check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight    ,   '''encoder.position_weight'''		)
    UpperCAmelCase__     :						Any						=							check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias    ,   '''encoder.layer_norm.beta'''		)
    UpperCAmelCase__     :						Optional[int]						=							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)
    UpperCAmelCase__     :						Any						=							torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

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

        # self attention
        UpperCAmelCase__     :						BertSelfAttention						=							layer.attention.self

        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            self_attn.key.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""		)

        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            self_attn.key.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""		)
        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            self_attn.query.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""		)
        UpperCAmelCase__     :						Dict						=							check_and_map_params(
            self_attn.query.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""		)
        UpperCAmelCase__     :						Any						=							check_and_map_params(
            self_attn.value.bias.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""		)
        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            self_attn.value.weight.data    ,   F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""		)

        # self attention output
        UpperCAmelCase__     :						BertSelfOutput						=							layer.attention.output

        UpperCAmelCase__     :						List[Any]						=							check_and_map_params(
            self_output.dense.bias    ,   F"""encoder.transformer_cells.{i}.proj.bias"""		)
        UpperCAmelCase__     :						Tuple						=							check_and_map_params(
            self_output.dense.weight    ,   F"""encoder.transformer_cells.{i}.proj.weight"""		)
        UpperCAmelCase__     :						Optional[int]						=							check_and_map_params(
            self_output.LayerNorm.bias    ,   F"""encoder.transformer_cells.{i}.layer_norm.beta"""		)
        UpperCAmelCase__     :						Optional[Any]						=							check_and_map_params(
            self_output.LayerNorm.weight    ,   F"""encoder.transformer_cells.{i}.layer_norm.gamma"""		)

        # intermediate
        UpperCAmelCase__     :						BertIntermediate						=							layer.intermediate

        UpperCAmelCase__     :						List[Any]						=							check_and_map_params(
            intermediate.dense.bias    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""		)
        UpperCAmelCase__     :						List[str]						=							check_and_map_params(
            intermediate.dense.weight    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""		)

        # output
        UpperCAmelCase__     :						BertOutput						=							layer.output

        UpperCAmelCase__     :						str						=							check_and_map_params(
            bert_output.dense.bias    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""		)
        UpperCAmelCase__     :						Tuple						=							check_and_map_params(
            bert_output.dense.weight    ,   F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""		)
        UpperCAmelCase__     :						Tuple						=							check_and_map_params(
            bert_output.LayerNorm.bias    ,   F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""		)
        UpperCAmelCase__     :						Any						=							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
    UpperCAmelCase__     :						Optional[int]						=							RobertaTokenizer.from_pretrained('''roberta-base'''		)

    UpperCAmelCase__     :						Tuple						=							tokenizer.encode_plus(lowerCAmelCase__		)['''input_ids''']

    # Get gluon output
    UpperCAmelCase__     :						Union[str, Any]						=							mx.nd.array([input_ids]		)
    UpperCAmelCase__     :						Optional[int]						=							original_bort(inputs=lowerCAmelCase__    ,   token_types=[]		)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[Any]						=							BertModel.from_pretrained(lowerCAmelCase__		)
    hf_bort_model.eval()

    UpperCAmelCase__     :						Union[str, Any]						=							tokenizer.encode_plus(lowerCAmelCase__    ,   return_tensors='''pt'''		)
    UpperCAmelCase__     :						List[str]						=							hf_bort_model(**lowerCAmelCase__		)[0]

    UpperCAmelCase__     :						List[str]						=							output_gluon[0].asnumpy()
    UpperCAmelCase__     :						Any						=							output_hf[0].detach().numpy()

    UpperCAmelCase__     :						Any						=							np.max(np.abs(hf_layer - gluon_layer		)		).item()
    UpperCAmelCase__     :						List[Any]						=							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__":
  UpperCamelCase__          = 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.'''
  )
  UpperCamelCase__          = parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							snake_case  :   List[str]      =							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      hf_hub_download(
														    repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      VideoClassificationPipeline(model=__lowerCAmelCase	,			image_processor=__lowerCAmelCase	,			top_k=2	)
														UpperCamelCase__					  =      [
														    example_video_filepath,
														    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
														]
														return video_classifier, examples






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														for example in examples:
																					UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	)

																					self.assertEqual(
																					    __lowerCAmelCase	,			[
																					        {"""score""": ANY(__lowerCAmelCase	), """label""": ANY(__lowerCAmelCase	)},
																					        {"""score""": ANY(__lowerCAmelCase	), """label""": ANY(__lowerCAmelCase	)},
																					    ]	,			)






							@require_torch
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
														UpperCamelCase__					  =      VideoMAEFeatureExtractor(
														    size={"""shortest_edge""": 10}	,			crop_size={"""height""": 10, """width""": 10}	)
														UpperCamelCase__					  =      pipeline(
														    """video-classification"""	,			model=__lowerCAmelCase	,			feature_extractor=__lowerCAmelCase	,			frame_sampling_rate=4	)

														UpperCamelCase__					  =      hf_hub_download(repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	,			top_k=2	)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			decimals=4	)	,			[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}]	,			)

														UpperCamelCase__					  =      video_classifier(
														    [
														        video_file_path,
														        video_file_path,
														    ]	,			top_k=2	,			)
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase	,			decimals=4	)	,			[
														        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
														        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
														    ]	,			)







							@require_tf
							def 					_lowerCamelCase		(    self	):
														pass




