



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					BlenderbotConfig
       UpperCamelCase						=					{}
       UpperCamelCase						=					'''gelu'''



       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Optional[Any]=13		,    _UpperCAmelCase      :  Tuple=7		,    _UpperCAmelCase      :  Dict=True		,    _UpperCAmelCase      :  Dict=False		,    _UpperCAmelCase      :  Union[str, Any]=99		,    _UpperCAmelCase      :  Any=32		,    _UpperCAmelCase      :  List[Any]=2		,    _UpperCAmelCase      :  int=4		,    _UpperCAmelCase      :  Tuple=37		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Dict=0.1		,    _UpperCAmelCase      :  Tuple=20		,    _UpperCAmelCase      :  str=2		,    _UpperCAmelCase      :  Union[str, Any]=1		,    _UpperCAmelCase      :  Tuple=0		,    )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							parent
             UpperCAmelCase_      			=							batch_size
             UpperCAmelCase_      			=							seq_length
             UpperCAmelCase_      			=							is_training
             UpperCAmelCase_      			=							use_labels
             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							intermediate_size

             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							eos_token_id
             UpperCAmelCase_      			=							pad_token_id
             UpperCAmelCase_      			=							bos_token_id



       def 					lowercase__    (							self      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size   )
             UpperCAmelCase_      			=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )		,    1   )
             UpperCAmelCase_      			=							tf.concat([input_ids, eos_tensor]		,    axis=1   )

             UpperCAmelCase_      			=							ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size   )

             UpperCAmelCase_      			=							self.config_cls(
                 vocab_size=self.vocab_size		,    d_model=self.hidden_size		,    encoder_layers=self.num_hidden_layers		,    decoder_layers=self.num_hidden_layers		,    encoder_attention_heads=self.num_attention_heads		,    decoder_attention_heads=self.num_attention_heads		,    encoder_ffn_dim=self.intermediate_size		,    decoder_ffn_dim=self.intermediate_size		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    eos_token_ids=[2]		,    bos_token_id=self.bos_token_id		,    pad_token_id=self.pad_token_id		,    decoder_start_token_id=self.pad_token_id		,    **self.config_updates		,    )
             UpperCAmelCase_      			=							prepare_blenderbot_inputs_dict(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             return config, inputs_dict







       def 					lowercase__    (							self      :  Any		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Tuple   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							TFBlenderbotModel(config=_UpperCAmelCase   ).get_decoder()
             UpperCAmelCase_      			=							inputs_dict["input_ids"]

             UpperCAmelCase_      			=							input_ids[:1, :]
             UpperCAmelCase_      			=							inputs_dict["attention_mask"][:1, :]
             UpperCAmelCase_      			=							inputs_dict["head_mask"]
             UpperCAmelCase_      			=							1

             # first forward pass
             UpperCAmelCase_      			=							model(_UpperCAmelCase		,    attention_mask=_UpperCAmelCase		,    head_mask=_UpperCAmelCase		,    use_cache=_UpperCAmelCase   )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             UpperCAmelCase_      			=							ids_tensor((self.batch_size, 3)		,    config.vocab_size   )
             UpperCAmelCase_      			=							tf.cast(ids_tensor((self.batch_size, 3)		,    2   )		,    tf.inta   )

             # append to next input_ids and
             UpperCAmelCase_      			=							tf.concat([input_ids, next_tokens]		,    axis=-1   )
             UpperCAmelCase_      			=							tf.concat([attention_mask, next_attn_mask]		,    axis=-1   )

             UpperCAmelCase_      			=							model(_UpperCAmelCase		,    attention_mask=_UpperCAmelCase   )[0]
             UpperCAmelCase_      			=							model(_UpperCAmelCase		,    attention_mask=_UpperCAmelCase		,    past_key_values=_UpperCAmelCase   )[0]

             self.parent.assertEqual(next_tokens.shape[1]		,    output_from_past.shape[1]   )

             # select random slice
             UpperCAmelCase_      			=							int(ids_tensor((1,)		,    output_from_past.shape[-1]   )   )
             UpperCAmelCase_      			=							output_from_no_past[:, -3:, random_slice_idx]
             UpperCAmelCase_      			=							output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(_UpperCAmelCase		,    _UpperCAmelCase		,    rtol=1e-3   )



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      ):
      if attention_mask is None:
            UpperCAmelCase_      			=							tf.cast(tf.math.not_equal(lowerCAmelCase__     ,      config.pad_token_id				)     ,      tf.inta				)
      if decoder_attention_mask is None:
            UpperCAmelCase_      			=							tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape     ,      dtype=tf.inta				),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,      config.pad_token_id				)     ,      tf.inta				),
                ]     ,      axis=-1     ,      )
      if head_mask is None:
            UpperCAmelCase_      			=							tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            UpperCAmelCase_      			=							tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            UpperCAmelCase_      			=							tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }
@require_tf
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       UpperCamelCase						=					(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       UpperCamelCase						=					(
           {
               '''conversational''': TFBlenderbotForConditionalGeneration,
               '''feature-extraction''': TFBlenderbotModel,
               '''summarization''': TFBlenderbotForConditionalGeneration,
               '''text2text-generation''': TFBlenderbotForConditionalGeneration,
               '''translation''': TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase						=					True
       UpperCamelCase						=					False
       UpperCamelCase						=					False



       def 					lowercase__    (							self      :  int   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							TFBlenderbotModelTester(self   )
             UpperCAmelCase_      			=							ConfigTester(self		,    config_class=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						Optional[int]:


             '''simple docstring'''




             self.config_tester.run_common_tests()







       def 					lowercase__    (							self      :  List[Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase   )



@require_tokenizers
@require_tf
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					['''My friends are cool but they eat too many carbs.''']
       UpperCamelCase						=					'''facebook/blenderbot-400M-distill'''



       @cached_property
       def 					lowercase__    (							self      :  Optional[Any]   )			->						Tuple:


             '''simple docstring'''




             return BlenderbotTokenizer.from_pretrained(self.model_name   )



       @cached_property
       def 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
             return model







       @slow
       def 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    return_tensors="tf"   )
             UpperCAmelCase_      			=							self.model.generate(
                 model_inputs.input_ids		,    )
             UpperCAmelCase_      			=							self.tokenizer.batch_decode(generated_ids.numpy()		,    skip_special_tokens=_UpperCAmelCase   )[0]
             assert (
                 generated_words
                 == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
             )









"""simple docstring"""





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						1  # (0 is vertical, 1 is horizontal)
def        a__     (				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_dataset(lowerCAmelCase__     ,      lowerCAmelCase__				)
      print("Processing..."				)
      UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							update_image_and_anno(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      for index, image in enumerate(lowerCAmelCase__				):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            UpperCAmelCase_      			=							random_chars(32				)
            UpperCAmelCase_      			=							paths[index].split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            UpperCAmelCase_      			=							f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
            cva.imwrite(f"""/{file_root}.jpg"""     ,      lowerCAmelCase__     ,      [cva.IMWRITE_JPEG_QUALITY, 85]				)
            print(f"""Success {index+1}/{len(lowerCAmelCase__				)} with {file_name}"""				)
            UpperCAmelCase_      			=							[]
            for anno in new_annos[index]:
                  UpperCAmelCase_      			=							f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
                  annos_list.append(lowerCAmelCase__				)
            with open(f"""/{file_root}.txt"""     ,      "w"				) as outfile:
                  outfile.write("\n".join(line for line in annos_list				)				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for label_file in glob.glob(os.path.join(lowerCAmelCase__     ,      "*.txt"				)				):
            UpperCAmelCase_      			=							label_file.split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            with open(lowerCAmelCase__				) as in_file:
                  UpperCAmelCase_      			=							in_file.readlines()
            UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      f"""{label_name}.jpg"""				)

            UpperCAmelCase_      			=							[]
            for obj_list in obj_lists:
                  UpperCAmelCase_      			=							obj_list.rstrip("\n"				).split(" "				)
                  boxes.append(
                      [
                          int(obj[0]				),
                          float(obj[1]				),
                          float(obj[2]				),
                          float(obj[3]				),
                          float(obj[4]				),
                      ]				)
            if not boxes:
                  continue
            img_paths.append(lowerCAmelCase__				)
            labels.append(lowerCAmelCase__				)
      return img_paths, labels
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 1				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for idx in range(len(lowerCAmelCase__				)				):
            UpperCAmelCase_      			=							[]
            UpperCAmelCase_      			=							img_list[idx]
            path_list.append(lowerCAmelCase__				)
            UpperCAmelCase_      			=							anno_list[idx]
            UpperCAmelCase_      			=							cva.imread(lowerCAmelCase__				)
            if flip_type == 1:
                  UpperCAmelCase_      			=							cva.flip(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
            elif flip_type == 0:
                  UpperCAmelCase_      			=							cva.flip(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
            new_annos_lists.append(lowerCAmelCase__				)
            new_imgs_list.append(lowerCAmelCase__				)
      return new_imgs_list, new_annos_lists, path_list
def        a__     (				lowerCAmelCase__ = 32				):
      assert number_char > 1, "The number of character should greater than 1"
      UpperCAmelCase_      			=							ascii_lowercase + digits
      return "".join(random.choice(lowerCAmelCase__				) for _ in range(lowerCAmelCase__				)				)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")




