

'''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"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    BlenderbotConfig
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    {}
  SCREAMING_SNAKE_CASE					:						Dict           =    'gelu'






  def __init__(   self     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple=1_3      ,lowercase__     :  Optional[int]=7      ,lowercase__     :  List[str]=True      ,lowercase__     :  Tuple=False      ,lowercase__     :  Union[str, Any]=9_9      ,lowercase__     :  Dict=3_2      ,lowercase__     :  Dict=2      ,lowercase__     :  Dict=4      ,lowercase__     :  Any=3_7      ,lowercase__     :  Tuple=0.1      ,lowercase__     :  Tuple=0.1      ,lowercase__     :  List[str]=2_0      ,lowercase__     :  List[str]=2      ,lowercase__     :  Optional[int]=1      ,lowercase__     :  Any=0      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size

   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				max_position_embeddings
   __lowercase													=				eos_token_id
   __lowercase													=				pad_token_id
   __lowercase													=				bos_token_id






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length - 1]      ,self.vocab_size    )
   __lowercase													=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )      ,1    )
   __lowercase													=				tf.concat([input_ids, eos_tensor]      ,axis=1    )

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

   __lowercase													=				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      ,)
   __lowercase													=				prepare_blenderbot_inputs_dict(lowercase__      ,lowercase__      ,lowercase__    )
   return config, inputs_dict






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  str    ):
   __lowercase													=				TFBlenderbotModel(config=lowercase__    ).get_decoder()
   __lowercase													=				inputs_dict['''input_ids''']

   __lowercase													=				input_ids[:1, :]
   __lowercase													=				inputs_dict['''attention_mask'''][:1, :]
   __lowercase													=				inputs_dict['''head_mask''']
   __lowercase													=				1

   # first forward pass
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,head_mask=lowercase__      ,use_cache=lowercase__    )

   __lowercase						,			__lowercase													=				outputs.to_tuple()

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

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

   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__    )[0]
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,past_key_values=lowercase__    )[0]

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

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

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


def 	_A  (					A__  ,       A__  ,       A__  ,       A__=None  ,       A__=None  ,       A__=None  ,       A__=None  ,       A__=None  ,       ):






 """simple docstring"""



 if attention_mask is None:
  __lowercase													=				tf.cast(tf.math.not_equal(A__  ,       config.pad_token_id   )  ,       tf.inta   )
 if decoder_attention_mask is None:
  __lowercase													=				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:
  __lowercase													=				tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
 if decoder_head_mask is None:
  __lowercase													=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
 if cross_attn_head_mask is None:
  __lowercase													=				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_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE					:						List[str]           =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE					:						int           =    (
      {
          'conversational': TFBlenderbotForConditionalGeneration,
          'feature-extraction': TFBlenderbotModel,
          'summarization': TFBlenderbotForConditionalGeneration,
          'text2text-generation': TFBlenderbotForConditionalGeneration,
          'translation': TFBlenderbotForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						str           =    True
  SCREAMING_SNAKE_CASE					:						Any           =    False
  SCREAMING_SNAKE_CASE					:						str           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				TFBlenderbotModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*lowercase__    )






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    ['My friends are cool but they eat too many carbs.']
  SCREAMING_SNAKE_CASE					:						List[str]           =    'facebook/blenderbot-400M-distill'






  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return BlenderbotTokenizer.from_pretrained(self.model_name    )






  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
   return model






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.tokenizer(self.src_text      ,return_tensors='''tf'''    )
   __lowercase													=				self.model.generate(
       model_inputs.input_ids      ,)
   __lowercase													=				self.tokenizer.batch_decode(generated_ids.numpy()      ,skip_special_tokens=lowercase__    )[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
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				F"{anno[0]} {x_center} {y_center} {width} {height}"
   annos_list.append(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       F"{label_name}.jpg"   )

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) + float(obj[4]   ) / 2

   boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
  if not boxes:
   continue
  img_paths.append(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __lowercase													=				np.zeros([output_size[0], output_size[1], 3]  ,       dtype=np.uinta   )
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				int(scale_x * output_size[1]   )
 __lowercase													=				int(scale_y * output_size[0]   )

 __lowercase													=				[]
 __lowercase													=				[]
 for i, index in enumerate(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   __lowercase													=				cva.resize(A__  ,       (output_size[1] - divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   __lowercase													=				cva.resize(
       A__  ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  __lowercase													=				[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]


def 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


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

