import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met



class      _A	(						__UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     List[Any]	,      *A_							:     Tuple	,      A_							:     Optional[Any]=None	,      A_							:     Dict=None	,      **A_							:     Dict    )    ->       List[str]:
         super().__init__(*_lowerCamelCase	,      **_lowerCamelCase    )
         __snake_case				=							eval_examples
         __snake_case				=							post_process_function
    def  lowercase					(		self							:     Tuple	,      A_							:     Optional[Dataset] = None	,      A_							:     List[str]=None	,      A_							:     Optional[List[str]] = None	,      A_							:     str = "eval"	,      **A_							:     Union[str, Any]	,      )    ->       List[str]:
         __snake_case				=							gen_kwargs.copy()
         __snake_case				=							(
             gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''    ) is not None else self.args.generation_max_length
         )
         __snake_case				=							(
             gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''    ) is not None else self.args.generation_num_beams
         )
         __snake_case				=							gen_kwargs

         __snake_case				=							self.eval_dataset if eval_dataset is None else eval_dataset
         __snake_case				=							self.get_eval_dataloader(_lowerCamelCase    )
         __snake_case				=							self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case				=							self.compute_metrics
         __snake_case				=							None
         __snake_case				=							time.time()
         __snake_case				=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         try:
              __snake_case				=							eval_loop(
                  _lowerCamelCase	,      description='''Evaluation'''	,      prediction_loss_only=True if compute_metrics is None else None	,      ignore_keys=_lowerCamelCase	,      metric_key_prefix=_lowerCamelCase	,      )
         finally:
              __snake_case				=							compute_metrics
         __snake_case				=							self.args.eval_batch_size * self.args.world_size
         if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
              start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
         output.metrics.update(
             speed_metrics(
                 _lowerCamelCase	,      _lowerCamelCase	,      num_samples=output.num_samples	,      num_steps=math.ceil(output.num_samples / total_batch_size    )	,      )    )

         if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
              # Only the main node write the results by default
              __snake_case				=							self.post_process_function(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase    )
              __snake_case				=							self.compute_metrics(_lowerCamelCase    )

              # Prefix all keys with metric_key_prefix + '_'
              for key in list(metrics.keys()    ):
                   if not key.startswith(f"{metric_key_prefix}_"    ):
                        __snake_case				=							metrics.pop(_lowerCamelCase    )

              metrics.update(output.metrics    )
         else:
              __snake_case				=							output.metrics

         if self.args.should_log:
              # Only the main node log the results by default
              self.log(_lowerCamelCase    )

         if self.args.tpu_metrics_debug or self.args.debug:
              # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
              xm.master_print(met.metrics_report()    )

         __snake_case				=							self.callback_handler.on_evaluate(self.args	,      self.state	,      self.control	,      _lowerCamelCase    )
         return metrics







    def  lowercase					(		self							:     List[str]	,      A_							:     int	,      A_							:     List[str]	,      A_							:     Optional[int]=None	,      A_							:     str = "test"	,      **A_							:     str    )    ->       List[Any]:
         __snake_case				=							gen_kwargs.copy()

         __snake_case				=							self.get_test_dataloader(_lowerCamelCase    )

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case				=							self.compute_metrics
         __snake_case				=							None
         __snake_case				=							time.time()
         __snake_case				=							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         try:
              __snake_case				=							eval_loop(
                  _lowerCamelCase	,      description='''Prediction'''	,      prediction_loss_only=True if compute_metrics is None else None	,      ignore_keys=_lowerCamelCase	,      metric_key_prefix=_lowerCamelCase	,      )
         finally:
              __snake_case				=							compute_metrics

         __snake_case				=							self.args.eval_batch_size * self.args.world_size
         if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
              start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
         output.metrics.update(
             speed_metrics(
                 _lowerCamelCase	,      _lowerCamelCase	,      num_samples=output.num_samples	,      num_steps=math.ceil(output.num_samples / total_batch_size    )	,      )    )
         if self.post_process_function is None or self.compute_metrics is None:
              return output

         __snake_case				=							self.post_process_function(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      '''predict'''    )
         __snake_case				=							self.compute_metrics(_lowerCamelCase    )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()    ):
              if not key.startswith(f"{metric_key_prefix}_"    ):
                   __snake_case				=							metrics.pop(_lowerCamelCase    )
         metrics.update(output.metrics    )
         return PredictionOutput(predictions=predictions.predictions	,      label_ids=predictions.label_ids	,      metrics=_lowerCamelCase    )
"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							BlipImageProcessor()
         __snake_case				=							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         __snake_case				=							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         __snake_case				=							InstructBlipProcessor(A_	,      A_	,      A_    )

         processor.save_pretrained(self.tmpdirname    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     str    )    ->       List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).tokenizer
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).image_processor
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).qformer_tokenizer
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case				=							InstructBlipProcessor(
             tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							InstructBlipProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
         self.assertIsInstance(processor.qformer_tokenizer	,      A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_	,      return_token_type_ids=A_    )
         __snake_case				=							qformer_tokenizer(A_	,      return_token_type_ids=A_    )

         for key in encoded_tokens.keys():
              self.assertListEqual(encoded_tokens[key]	,      encoded_processor[key]    )

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor['''qformer_''' + key]    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )