




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_					: str       ,    lowerCAmelCase_					: str      ):
					"""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=_SCREAMING_SNAKE_CASE       ,    output_all_encodings=_SCREAMING_SNAKE_CASE       ,    use_residual=predefined_args["use_residual"]       ,    activation=predefined_args.get("activation"       ,    "gelu"      )       ,    layer_norm_eps=predefined_args.get("layer_norm_eps"       ,    _SCREAMING_SNAKE_CASE      )       ,    )

					# 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(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    cls=_SCREAMING_SNAKE_CASE      )

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

					original_bort.load_parameters(_SCREAMING_SNAKE_CASE       ,    cast_dtype=_SCREAMING_SNAKE_CASE       ,    ignore_extra=_SCREAMING_SNAKE_CASE      )
					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(_SCREAMING_SNAKE_CASE      ),
					}

					lowerCAmelCase__          =			BertConfig.from_dict(_SCREAMING_SNAKE_CASE      )
					lowerCAmelCase__          =			BertForMaskedLM(_SCREAMING_SNAKE_CASE      )
					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_					: Tuple      ) -> 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_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]      ):
										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(_SCREAMING_SNAKE_CASE      )["input_ids"]

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

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

					lowerCAmelCase__          =			tokenizer.encode_plus(_SCREAMING_SNAKE_CASE       ,    return_tensors="pt"      )
					lowerCAmelCase__          =			hf_bort_model(**_SCREAMING_SNAKE_CASE      )[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(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    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:"       ,    _SCREAMING_SNAKE_CASE      )


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)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=7       ,     SCREAMING_SNAKE_CASE__					:					str=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					int=99       ,     SCREAMING_SNAKE_CASE__					:					List[str]=32       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]=4       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=37       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=512       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=16       ,     SCREAMING_SNAKE_CASE__					:					List[str]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.02       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3       ,     SCREAMING_SNAKE_CASE__					:					int=4       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					str=0       ,     )				->	List[Any]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_input_mask
         lowerCAmelCase__          =			use_token_type_ids
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			vocab_size
         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__          =			max_position_embeddings
         lowerCAmelCase__          =			type_vocab_size
         lowerCAmelCase__          =			type_sequence_label_size
         lowerCAmelCase__          =			initializer_range
         lowerCAmelCase__          =			num_labels
         lowerCAmelCase__          =			num_choices
         lowerCAmelCase__          =			scope
         lowerCAmelCase__          =			projection_dim


    def        a				(	self					:					Any       )				->	Any:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_input_mask:
              # follow test_modeling_tf_ctrl.py
              lowerCAmelCase__          =			random_attention_mask([self.batch_size, self.seq_length]       )

         lowerCAmelCase__          =			None
         if self.use_token_type_ids:
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )

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

         lowerCAmelCase__          =			BertConfig(
             vocab_size=self.vocab_size       ,     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       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=SCREAMING_SNAKE_CASE__       ,     initializer_range=self.initializer_range       ,     )
         lowerCAmelCase__          =			DPRConfig(projection_dim=self.projection_dim       ,     **config.to_dict()       )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRContextEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	List[Any]:
         lowerCAmelCase__          =			TFDPRReader(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       )

         self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.relevance_logits.shape       ,     (self.batch_size,)       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		
         )          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids}
         return config, inputs_dict






@require_tf
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    snake_case__ =				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					Dict       )				->	int:
         lowerCAmelCase__          =			TFDPRModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     hidden_size=37       )


    def        a				(	self					:					List[Any]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Tuple       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRReader.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"       )

         lowerCAmelCase__          =			tf.constant(
             [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]       )  # [CLS] hello, is my dog cute? [SEP]
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         lowerCAmelCase__          =			tf.constant(
             [
                 [
                     0.03_236_253,
                     0.12_753_335,
                     0.16_818_509,
                     0.00_279_786,
                     0.3_896_933,
                     0.24_264_945,
                     0.2_178_971,
                     -0.02_335_227,
                     -0.08_481_959,
                     -0.14_324_117,
                 ]
             ]       )
         self.assertTrue(numpy.allclose(output[:, :10].numpy()       ,     expected_slice.numpy()       ,     atol=1e-4       )       )
