import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
     SpeechaTextaDecoder,
     SpeechaTextaForCausalLM,
 )




@require_torch
class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Union[str, Any]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Optional[Any]=9_9							,      lowerCamelCase							:		str=1_3							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Union[str, Any]=7							,      lowerCamelCase							:		str=True							,      lowerCamelCase							:		str=True							,      lowerCamelCase							:		Any=True							,      lowerCamelCase							:		Dict=False							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Dict=2							,      lowerCamelCase							:		int=3_2							,      lowerCamelCase							:		Optional[int]=4							,      lowerCamelCase							:		int=4							,      lowerCamelCase							:		int=3_0							,      lowerCamelCase							:		int=0							,      lowerCamelCase							:		List[str]=1							,      lowerCamelCase							:		Union[str, Any]=2							,      lowerCamelCase							:		Union[str, Any]=None							,      )			->				Optional[int]:
        _UpperCamelCase  :							Optional[Any]	    =	parent
        _UpperCamelCase  :							int	    =	batch_size
        _UpperCamelCase  :							Any	    =	decoder_seq_length
        # For common tests
        _UpperCamelCase  :							Optional[int]	    =	self.decoder_seq_length
        _UpperCamelCase  :							Tuple	    =	is_training
        _UpperCamelCase  :							Any	    =	use_attention_mask
        _UpperCamelCase  :							Optional[Any]	    =	use_labels

        _UpperCamelCase  :							str	    =	vocab_size
        _UpperCamelCase  :							str	    =	d_model
        _UpperCamelCase  :							int	    =	d_model
        _UpperCamelCase  :							int	    =	decoder_layers
        _UpperCamelCase  :							List[Any]	    =	decoder_layers
        _UpperCamelCase  :							List[Any]	    =	decoder_ffn_dim
        _UpperCamelCase  :							Optional[Any]	    =	decoder_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	decoder_attention_heads
        _UpperCamelCase  :							List[str]	    =	eos_token_id
        _UpperCamelCase  :							Tuple	    =	bos_token_id
        _UpperCamelCase  :							List[str]	    =	pad_token_id
        _UpperCamelCase  :							Tuple	    =	decoder_start_token_id
        _UpperCamelCase  :							Optional[int]	    =	use_cache
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings

        _UpperCamelCase  :							Optional[Any]	    =	None
        _UpperCamelCase  :							Union[str, Any]	    =	decoder_seq_length
        _UpperCamelCase  :							Any	    =	2
        _UpperCamelCase  :							Union[str, Any]	    =	1

 def        __snake_case       (      self							:		Optional[int]	)			->				Dict:
        _UpperCamelCase  :							List[Any]	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[int]	    =	None
        if self.use_attention_mask:
               _UpperCamelCase  :							int	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      vocab_size=2	)

        _UpperCamelCase  :							Optional[Any]	    =	None
        if self.use_labels:
               _UpperCamelCase  :							Optional[int]	    =	ids_tensor([self.batch_size, self.decoder_seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[Any]	    =	SpeechaTextaConfig(
            vocab_size=self.vocab_size							,      d_model=self.d_model							,      decoder_layers=self.decoder_layers							,      decoder_ffn_dim=self.decoder_ffn_dim							,      decoder_attention_heads=self.decoder_attention_heads							,      eos_token_id=self.eos_token_id							,      bos_token_id=self.bos_token_id							,      use_cache=self.use_cache							,      pad_token_id=self.pad_token_id							,      decoder_start_token_id=self.decoder_start_token_id							,      max_position_embeddings=self.max_position_embeddings							,      )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		int							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	True
        _UpperCamelCase  :							Optional[int]	    =	SpeechaTextaDecoder(config=lowerCamelCase	).to(lowerCamelCase	).eval()
        _UpperCamelCase  :							List[Any]	    =	input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        _UpperCamelCase  :							int	    =	model(lowerCamelCase							,      use_cache=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	model(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	model(lowerCamelCase							,      use_cache=lowerCamelCase	)

        self.parent.assertTrue(len(lowerCamelCase	) == len(lowerCamelCase	)	)
        self.parent.assertTrue(len(lowerCamelCase	) == len(lowerCamelCase	) + 1	)

        _UpperCamelCase  :							List[Any]	    =	outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        _UpperCamelCase  :							Optional[int]	    =	ids_tensor((2, 1)							,      config.vocab_size - 1	) + 1

        # append to next input_ids and
        _UpperCamelCase  :							List[str]	    =	torch.cat([input_ids, next_tokens]							,      dim=-1	)

        print(lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	model(lowerCamelCase	)['last_hidden_state']
        _UpperCamelCase  :							str	    =	model(lowerCamelCase							,      past_key_values=lowerCamelCase	)['last_hidden_state']

        # select random slice
        _UpperCamelCase  :							List[str]	    =	ids_tensor((1,)							,      output_from_past.shape[-1]	).item()
        _UpperCamelCase  :							Optional[Any]	    =	output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        _UpperCamelCase  :							List[Any]	    =	output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=1e-3	)

 def        __snake_case       (      self							:		Any	)			->				str:
        _UpperCamelCase  :							Optional[Any]	    =	self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   (
            _UpperCamelCase
        )    ,   
        )  :							List[str]	    =	config_and_inputs

        _UpperCamelCase  :							int	    =	{
            'input_ids': input_ids,
            'attention_mask': attention_mask,
        }
        return config, inputs_dict







@require_torch
class  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Any					     =			(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
 _lowerCamelCase    :						str					     =			(SpeechaTextaForCausalLM,) if is_torch_available() else ()
 _lowerCamelCase    :						Dict					     =			{'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
 _lowerCamelCase    :						Dict					     =			True
 _lowerCamelCase    :						Optional[Any]					     =			False

 def        __snake_case       (      self							:		List[str]							,      )			->				Tuple:
        _UpperCamelCase  :							List[str]	    =	SpeechaTextaStandaloneDecoderModelTester(self							,      is_training=lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	ConfigTester(self							,      config_class=lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Dict:
        pass

 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        pass

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[Any]:
        pass

 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        self.config_tester.run_common_tests()

 def        __snake_case       (      self							:		Dict	)			->				str:
        _UpperCamelCase  :							List[str]	    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        return




import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
UpperCAmelCase      		=					False
UpperCAmelCase      		=					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase      		=					datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase  :							Dict	    =	arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(lowerCamelCase	), f"{outputfile}"
        if subset_list is not None:
               with open(os.path.realpath(lowerCamelCase	)	) as f:
                      _UpperCamelCase  :							str	    =	{self._vqa_file_split()[0] for x in tryload(lowerCamelCase	)}
        else:
               _UpperCamelCase  :							Any	    =	None

        _UpperCamelCase  :							Any	    =	CONFIG
        if torch.cuda.is_available():
               _UpperCamelCase  :							List[str]	    =	'cuda'
        _UpperCamelCase  :							int	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	Preprocess(self.config	)
        _UpperCamelCase  :							Tuple	    =	GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,      config=self.config	)
        _UpperCamelCase  :							List[Any]	    =	batch_size if batch_size != 0 else 1
        _UpperCamelCase  :							Any	    =	DEFAULT_SCHEMA

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							str	    =	[]
        for i, file in enumerate(os.listdir(self.inputdir	)	):
               if self.subset_list is not None and i not in self.subset_list:
                      continue
               batch.append(self._vqa_file_split(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

        for i in range(1	):
               yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

 def __call__(      self							:		Optional[Any]	)			->				Any:
        # make writer
        if not TEST:
               _UpperCamelCase  :							int	    =	datasets.ArrowWriter(features=self.schema							,      path=self.outputfile	)
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	self.preprocess(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.model(
                   lowerCamelCase							,      lowerCamelCase							,      scales_yx=lowerCamelCase							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
               _UpperCamelCase  :							Tuple	    =	output_dict.pop('normalized_boxes'	)
               if not TEST:
                      _UpperCamelCase  :							Dict	    =	np.array(lowerCamelCase	)
                      _UpperCamelCase  :							Optional[int]	    =	self.schema.encode_batch(lowerCamelCase	)
                      writer.write_batch(lowerCamelCase	)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	stream.read().split('\n'					)
       return data


if __name__ == "__main__":
 UpperCAmelCase      		=					Extract(sys.argv[1:])
 extract()
 if not TEST:
  UpperCAmelCase      		=					datasets.Dataset.from_file(extract.outputfile)
  # wala!
  # print(np.array(dataset[0:2]["roi_features"]).shape)



