

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

import cva
import numpy as np

# Parrameters
a_    :						Dict    		=      (720, 1280)  # Height, Width
a_    :						Tuple    		=      (0.4, 0.6)  # if height or width lower than this scale, drop it.
a_    :						str    		=      1 / 100
a_    :						Dict    		=      ""
a_    :						Optional[int]    		=      ""
a_    :						int    		=      ""
a_    :						Any    		=      250


def        __lowerCAmelCase    (       )		->							None:
 '''simple docstring'''


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

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							tuple[list, list]:
 '''simple docstring'''


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

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

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  list		,							_UpperCamelCase		:  list		,							_UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  tuple[int, int]		,							_UpperCamelCase		:  tuple[float, float]		,							_UpperCamelCase		:  float = 0.0		,							)		->							tuple[list, list, str]:
 '''simple docstring'''


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

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


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


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




import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
      import torch

      from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      str=1_3							,    snake_case__ :      Tuple=7							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      List[str]=False							,    snake_case__ :      Any=True							,    snake_case__ :      Union[str, Any]=9_9							,    snake_case__ :      Dict=3_2							,    snake_case__ :      Optional[Any]=5							,    snake_case__ :      Optional[Any]=4							,    snake_case__ :      Union[str, Any]=3_7							,    snake_case__ :      Tuple="gelu"							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      int=5_1_2							,    snake_case__ :      Dict=1_6							,    snake_case__ :      str=2							,    snake_case__ :      Any=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      int=4							,    snake_case__ :      List[str]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

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

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

     SCREAMING_SNAKE_CASE								=			self.get_config()

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

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     return OpenLlamaConfig(
         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=snake_case__							,    initializer_range=self.initializer_range							,    use_stable_embedding=snake_case__							,    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    use_cache=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    vocab_size=2    )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE								=			torch.cat([input_ids, next_tokens]							,    dim=-1    )
     SCREAMING_SNAKE_CASE								=			torch.cat([input_mask, next_mask]							,    dim=-1    )

     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    past_key_values=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]

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

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-3    )    )




    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    __UpperCamelCase         =(OpenLlamaForCausalLM,) if is_torch_available() else ()
    __UpperCamelCase         =(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'single_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'multi_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor(
         [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'    )
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     pass




    @parameterized.expand([('linear',), ('dynamic',)]    )
    def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, 1_0]							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, int(config.max_position_embeddings * 1.5    )]							,    config.vocab_size    )

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     original_model.to(snake_case__    )
     original_model.eval()
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			{'type': scaling_type, 'factor': 10.0}
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     scaled_model.to(snake_case__    )
     scaled_model.eval()
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state

     # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
     # maximum sequence length, so the outputs for the short input should match.
     if scaling_type == "dynamic":
      self.assertTrue(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )
     else:
      self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

     # The output should be different for long inputs
     self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

