
"""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()
_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      'The Nymphenburg Palace is a beautiful palace in Munich!'
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       {
							    '''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,
							}

							__snake_case     			=       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
							__snake_case     			=       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
							__snake_case     			=       '''openwebtext_ccnews_stories_books_cased'''

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

							__snake_case     			=       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_ )
							__snake_case     			=       original_bort._collect_params_with_prefix()

							# Build our config 🤗
							__snake_case     			=       {
							    '''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_ ),
							}

							__snake_case     			=       BertConfig.from_dict(lowerCAmelCase_ )
							__snake_case     			=       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(SCREAMING_SNAKE_CASE ) -> 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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       hf_param.shape

														__snake_case     			=       to_torch(params[gluon_param] )
														__snake_case     			=       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

							__snake_case     			=       check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight							,    "word_embed.0.weight" )
							__snake_case     			=       check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight							,    "encoder.position_weight" )
							__snake_case     			=       check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias							,    "encoder.layer_norm.beta" )
							__snake_case     			=       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)
							__snake_case     			=       torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

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

														# self attention
														__snake_case     			=       layer.attention.self

														__snake_case     			=       check_and_map_params(
														    self_attn.key.bias.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )

														__snake_case     			=       check_and_map_params(
														    self_attn.key.weight.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
														__snake_case     			=       check_and_map_params(
														    self_attn.query.bias.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
														__snake_case     			=       check_and_map_params(
														    self_attn.query.weight.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
														__snake_case     			=       check_and_map_params(
														    self_attn.value.bias.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
														__snake_case     			=       check_and_map_params(
														    self_attn.value.weight.data							,    F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )

														# self attention output
														__snake_case     			=       layer.attention.output

														__snake_case     			=       check_and_map_params(
														    self_output.dense.bias							,    F'''encoder.transformer_cells.{i}.proj.bias''' )
														__snake_case     			=       check_and_map_params(
														    self_output.dense.weight							,    F'''encoder.transformer_cells.{i}.proj.weight''' )
														__snake_case     			=       check_and_map_params(
														    self_output.LayerNorm.bias							,    F'''encoder.transformer_cells.{i}.layer_norm.beta''' )
														__snake_case     			=       check_and_map_params(
														    self_output.LayerNorm.weight							,    F'''encoder.transformer_cells.{i}.layer_norm.gamma''' )

														# intermediate
														__snake_case     			=       layer.intermediate

														__snake_case     			=       check_and_map_params(
														    intermediate.dense.bias							,    F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
														__snake_case     			=       check_and_map_params(
														    intermediate.dense.weight							,    F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )

														# output
														__snake_case     			=       layer.output

														__snake_case     			=       check_and_map_params(
														    bert_output.dense.bias							,    F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
														__snake_case     			=       check_and_map_params(
														    bert_output.dense.weight							,    F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
														__snake_case     			=       check_and_map_params(
														    bert_output.LayerNorm.bias							,    F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
														__snake_case     			=       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
							__snake_case     			=       RobertaTokenizer.from_pretrained("roberta-base" )

							__snake_case     			=       tokenizer.encode_plus(lowerCAmelCase_ )['''input_ids''']

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

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

							__snake_case     			=       tokenizer.encode_plus(lowerCAmelCase_							,    return_tensors="pt" )
							__snake_case     			=       hf_bort_model(**lowerCAmelCase_ )[0]

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

							__snake_case     			=       np.max(np.abs(hf_layer - gluon_layer ) ).item()
							__snake_case     			=       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__":
						_SCREAMING_SNAKE_CASE	       =      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."""
						)
						_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(lowercase__   )
class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Tuple							,						**snake_case_   :							str    ):
												super().__init__(**snake_case_    )

												requires_backends(self							,						"vision"    )
												self.check_model_type(
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == "tf"
												    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





					def __call__(				self   :							Optional[Any]							,						snake_case_   :							Union[str, List[str], "Image", List["Image"]]							,						**snake_case_   :							Optional[int]    ):
												return super().__call__(snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						**snake_case_   :							Any    ):
												__snake_case     			=       {}
												if "candidate_labels" in kwargs:
																			__snake_case     			=       kwargs["candidate_labels"]
												if "hypothesis_template" in kwargs:
																			__snake_case     			=       kwargs["hypothesis_template"]

												return preprocess_params, {}, {}





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]=None							,						snake_case_   :							int="This is a photo of {}."    ):
												__snake_case     			=       load_image(snake_case_    )
												__snake_case     			=       self.image_processor(images=[image]							,						return_tensors=self.framework    )
												__snake_case     			=       candidate_labels
												__snake_case     			=       [hypothesis_template.format(snake_case_    ) for x in candidate_labels]
												__snake_case     			=       self.tokenizer(snake_case_							,						return_tensors=self.framework							,						padding=snake_case_    )
												__snake_case     			=       [text_inputs]
												return inputs





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Dict    ):
												__snake_case     			=       model_inputs.pop("candidate_labels"    )
												__snake_case     			=       model_inputs.pop("text_inputs"    )
												if isinstance(text_inputs[0]							,						snake_case_    ):
																			__snake_case     			=       text_inputs[0]
												else:
																			# Batching case.
																			__snake_case     			=       text_inputs[0][0]

												__snake_case     			=       self.model(**snake_case_							,						**snake_case_    )

												__snake_case     			=       {
												    "candidate_labels": candidate_labels,
												    "logits": outputs.logits_per_image,
												}
												return model_outputs





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Any    ):
												__snake_case     			=       model_outputs.pop("candidate_labels"    )
												__snake_case     			=       model_outputs["logits"][0]
												if self.framework == "pt":
																			__snake_case     			=       logits.softmax(dim=-1    ).squeeze(-1    )
																			__snake_case     			=       probs.tolist()
																			if not isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       [scores]
												elif self.framework == "tf":
																			__snake_case     			=       stable_softmax(snake_case_							,						axis=-1    )
																			__snake_case     			=       probs.numpy().tolist()
												else:
																			raise ValueError(F'''Unsupported framework: {self.framework}'''    )

												__snake_case     			=       [
												    {"score": score, "label": candidate_label}
												    for score, candidate_label in sorted(zip(snake_case_							,						snake_case_    )							,						key=lambda snake_case_    : -x[0]    )
												]
												return result
