



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					"""timesformer"""


							def __init__(							self  ,  a_=224  ,  a_=16  ,  a_=3  ,  a_=8  ,  a_=768  ,  a_=12  ,  a_=12  ,  a_=3_072  ,  a_="gelu"  ,  a_=0.0  ,  a_=0.0  ,  a_=0.02  ,  a_=1E-6  ,  a_=True  ,  a_="divided_space_time"  ,  a_=0  ,  **a_  ,  ):
											super().__init__(**a_		)

											a__				      =		image_size
											a__				      =		patch_size
											a__				      =		num_channels
											a__				      =		num_frames

											a__				      =		hidden_size
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads
											a__				      =		intermediate_size
											a__				      =		hidden_act
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		initializer_range
											a__				      =		layer_norm_eps
											a__				      =		qkv_bias

											a__				      =		attention_type
											a__				      =		drop_path_rate






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor, OwlViTProcessor

@require_vision
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		tempfile.mkdtemp()

											# fmt: off
											a__				      =		["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
											# fmt: on
											a__				      =		dict(zip(a_  ,  range(len(a_		)		)		)		)
											a__				      =		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											a__				      =		{"""unk_token""": """<unk>"""}

											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""merges_file"""]		)
											with open(self.vocab_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write(json.dumps(a_		) + """\n"""		)
											with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write("""\n""".join(a_		)		)

											a__				      =		{
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											a__				      =		os.path.join(self.tmpdirname  ,  a_		)
											with open(self.image_processor_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															json.dump(a_  ,  a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizer.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return OwlViTImageProcessor.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self		):
											shutil.rmtree(self.tmpdirname		)


							def _a      (							self		):
											a__				      =		[np.random.randint(255  ,  size=(3, 30, 400)  ,  dtype=np.uinta		)]

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

											return image_inputs


							def _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_rust_tokenizer()
											a__				      =		self.get_image_processor()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_slow.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.from_pretrained(self.tmpdirname  ,  use_fast=a_		)

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_fast.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.from_pretrained(self.tmpdirname		)

											self.assertEqual(processor_slow.tokenizer.get_vocab()  ,  tokenizer_slow.get_vocab()		)
											self.assertEqual(processor_fast.tokenizer.get_vocab()  ,  tokenizer_fast.get_vocab()		)
											self.assertEqual(tokenizer_slow.get_vocab()  ,  tokenizer_fast.get_vocab()		)
											self.assertIsInstance(processor_slow.tokenizer  ,  a_		)
											self.assertIsInstance(processor_fast.tokenizer  ,  a_		)

											self.assertEqual(processor_slow.image_processor.to_json_string()  ,  image_processor.to_json_string()		)
											self.assertEqual(processor_fast.image_processor.to_json_string()  ,  image_processor.to_json_string()		)
											self.assertIsInstance(processor_slow.image_processor  ,  a_		)
											self.assertIsInstance(processor_fast.image_processor  ,  a_		)


							def _a      (							self		):
											a__				      =		OwlViTProcessor(tokenizer=self.get_tokenizer()  ,  image_processor=self.get_image_processor()		)
											processor.save_pretrained(self.tmpdirname		)

											a__				      =		self.get_tokenizer(bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""		)
											a__				      =		self.get_image_processor(do_normalize=a_		)

											a__				      =		OwlViTProcessor.from_pretrained(
											    self.tmpdirname  ,  bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""  ,  do_normalize=a_		)

											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_		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()

											a__				      =		image_processor(a_  ,  return_tensors="""np"""		)
											a__				      =		processor(images=a_  ,  return_tensors="""np"""		)

											for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()  ,  input_processor[key].sum()  ,  delta=1E-2		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""lower newer"""

											a__				      =		processor(text=a_  ,  return_tensors="""np"""		)

											a__				      =		tokenizer(a_  ,  return_tensors="""np"""		)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key][0].tolist()  ,  encoded_processor[key][0].tolist()		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""lower newer"""
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(text=a_  ,  images=a_		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask""", """pixel_values"""]		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		[["""cat""", """nasa badge"""], ["""person"""]]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		len(a_		)
											a__				      =		max([len(a_		) for texts in input_texts]		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (batch_size * num_max_text_queries, seq_length)		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		inputs["""input_ids"""]
											a__				      =		[
											    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											]

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)
											self.assertListEqual(list(input_ids[0]		)  ,  predicted_ids[0]		)
											self.assertListEqual(list(input_ids[1]		)  ,  predicted_ids[1]		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(images=a_  ,  query_images=a_		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""query_pixel_values""", """pixel_values"""]		)

											# test if it raises when no input is passed
											with pytest.raises(a_		):
															processor()







							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

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

											a__				      =		processor.batch_decode(a_		)
											a__				      =		tokenizer.batch_decode(a_		)

											self.assertListEqual(a_  ,  a_		)

