

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 							A_ (		unittest.TestCase					):


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	str:
      __lowerCAmelCase:   Optional[int]   	=      tempfile.mkdtemp()

      # fmt: off
      __lowerCAmelCase:   Optional[int]   	=      ["", "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
      __lowerCAmelCase:   Optional[int]   	=      dict(zip(UpperCamelCase__			,  range(len(UpperCamelCase__					)					)					)					)
      __lowerCAmelCase:   Optional[Any]   	=      ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
      __lowerCAmelCase:   List[str]   	=      {"unk_token": "<unk>"}

      __lowerCAmelCase:   Optional[int]   	=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES['vocab_file']					)
      __lowerCAmelCase:   int   	=      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(UpperCamelCase__					) + '\n'					)
      with open(self.merges_file			,  'w'			,  encoding='utf-8'					) as fp:
       fp.write('\n'.join(UpperCamelCase__					)					)

      __lowerCAmelCase:   List[str]   	=      {
          "do_resize": True,
          "size": 2_0,
          "do_center_crop": True,
          "crop_size": 1_8,
          "do_normalize": True,
          "image_mean": [0.48145466, 0.4578275, 0.40821073],
          "image_std": [0.26862954, 0.26130258, 0.27577711],
      }
      __lowerCAmelCase:   Dict   	=      os.path.join(self.tmpdirname			,  UpperCamelCase__					)
      with open(self.image_processor_file			,  'w'			,  encoding='utf-8'					) as fp:
       json.dump(UpperCamelCase__			,  UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Optional[Any]			,  **UpperCAmelCase			:		List[Any]					)							->	Tuple:
      return CLIPTokenizer.from_pretrained(self.tmpdirname			,  pad_token='!'			,  **UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		List[Any]			,  **UpperCAmelCase			:		Union[str, Any]					)							->	Union[str, Any]:
      return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,  pad_token='!'			,  **UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Any			,  **UpperCAmelCase			:		int					)							->	Tuple:
      return OwlViTImageProcessor.from_pretrained(self.tmpdirname			,  **UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Union[str, Any]:
      shutil.rmtree(self.tmpdirname					)


     def   UpperCAmelCase  (							self			:		List[str]					)							->	Any:
      __lowerCAmelCase:   str   	=      [np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta					)]

      __lowerCAmelCase:   Union[str, Any]   	=      [Image.fromarray(np.moveaxis(UpperCamelCase__			,  0			,  -1					)					) for x in image_inputs]

      return image_inputs


     def   UpperCAmelCase  (							self			:		Any					)							->	int:
      __lowerCAmelCase:   Tuple   	=      self.get_tokenizer()
      __lowerCAmelCase:   Tuple   	=      self.get_rust_tokenizer()
      __lowerCAmelCase:   str   	=      self.get_image_processor()

      __lowerCAmelCase:   str   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)
      processor_slow.save_pretrained(self.tmpdirname					)
      __lowerCAmelCase:   str   	=      OwlViTProcessor.from_pretrained(self.tmpdirname			,  use_fast=UpperCamelCase__					)

      __lowerCAmelCase:   Union[str, Any]   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)
      processor_fast.save_pretrained(self.tmpdirname					)
      __lowerCAmelCase:   Union[str, Any]   	=      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			,  UpperCamelCase__					)
      self.assertIsInstance(processor_fast.tokenizer			,  UpperCamelCase__					)

      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			,  UpperCamelCase__					)
      self.assertIsInstance(processor_fast.image_processor			,  UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Dict					)							->	Optional[Any]:
      __lowerCAmelCase:   str   	=      OwlViTProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)

      __lowerCAmelCase:   Tuple   	=      self.get_tokenizer(bos_token='(BOS)'			,  eos_token='(EOS)'					)
      __lowerCAmelCase:   List[Any]   	=      self.get_image_processor(do_normalize=UpperCamelCase__					)

      __lowerCAmelCase:   Dict   	=      OwlViTProcessor.from_pretrained(
          self.tmpdirname			,  bos_token='(BOS)'			,  eos_token='(EOS)'			,  do_normalize=UpperCamelCase__					)

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

      self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor			,  UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		int					)							->	Tuple:
      __lowerCAmelCase:   int   	=      self.get_image_processor()
      __lowerCAmelCase:   List[str]   	=      self.get_tokenizer()

      __lowerCAmelCase:   Tuple   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Union[str, Any]   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   Union[str, Any]   	=      image_processor(UpperCamelCase__			,  return_tensors='np'					)
      __lowerCAmelCase:   Optional[int]   	=      processor(images=UpperCamelCase__			,  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   UpperCAmelCase  (							self			:		Dict					)							->	Optional[int]:
      __lowerCAmelCase:   Any   	=      self.get_image_processor()
      __lowerCAmelCase:   Optional[int]   	=      self.get_tokenizer()

      __lowerCAmelCase:   str   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Any   	=      "lower newer"

      __lowerCAmelCase:   List[Any]   	=      processor(text=UpperCamelCase__			,  return_tensors='np'					)

      __lowerCAmelCase:   Dict   	=      tokenizer(UpperCamelCase__			,  return_tensors='np'					)

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


     def   UpperCAmelCase  (							self			:		List[Any]					)							->	int:
      __lowerCAmelCase:   Any   	=      self.get_image_processor()
      __lowerCAmelCase:   Optional[int]   	=      self.get_tokenizer()

      __lowerCAmelCase:   Union[str, Any]   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Tuple   	=      "lower newer"
      __lowerCAmelCase:   Tuple   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   Any   	=      processor(text=UpperCamelCase__			,  images=UpperCamelCase__					)

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

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


     def   UpperCAmelCase  (							self			:		List[str]					)							->	List[Any]:
      __lowerCAmelCase:   List[Any]   	=      "google/owlvit-base-patch32"
      __lowerCAmelCase:   str   	=      OwlViTProcessor.from_pretrained(UpperCamelCase__					)

      __lowerCAmelCase:   Optional[Any]   	=      ["cat", "nasa badge"]
      __lowerCAmelCase:   List[Any]   	=      processor(text=UpperCamelCase__					)

      __lowerCAmelCase:   str   	=      1_6
      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(UpperCamelCase__					):
       processor()


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Tuple:
      __lowerCAmelCase:   Optional[Any]   	=      "google/owlvit-base-patch32"
      __lowerCAmelCase:   Dict   	=      OwlViTProcessor.from_pretrained(UpperCamelCase__					)

      __lowerCAmelCase:   Tuple   	=      [["cat", "nasa badge"], ["person"]]
      __lowerCAmelCase:   List[str]   	=      processor(text=UpperCamelCase__					)

      __lowerCAmelCase:   int   	=      1_6
      __lowerCAmelCase:   Union[str, Any]   	=      len(UpperCamelCase__					)
      __lowerCAmelCase:   Optional[int]   	=      max([len(UpperCamelCase__					) 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(UpperCamelCase__					):
       processor()


     def   UpperCAmelCase  (							self			:		List[Any]					)							->	str:
      __lowerCAmelCase:   Any   	=      "google/owlvit-base-patch32"
      __lowerCAmelCase:   Dict   	=      OwlViTProcessor.from_pretrained(UpperCamelCase__					)

      __lowerCAmelCase:   str   	=      ["cat", "nasa badge"]
      __lowerCAmelCase:   Optional[Any]   	=      processor(text=UpperCamelCase__					)

      __lowerCAmelCase:   Any   	=      1_6
      __lowerCAmelCase:   Any   	=      inputs["input_ids"]
      __lowerCAmelCase:   List[Any]   	=      [
          [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
          [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 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   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Tuple:
      __lowerCAmelCase:   Optional[Any]   	=      self.get_image_processor()
      __lowerCAmelCase:   str   	=      self.get_tokenizer()

      __lowerCAmelCase:   Optional[Any]   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   int   	=      self.prepare_image_inputs()
      __lowerCAmelCase:   str   	=      self.prepare_image_inputs()

      __lowerCAmelCase:   int   	=      processor(images=UpperCamelCase__			,  query_images=UpperCamelCase__					)

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

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


     def   UpperCAmelCase  (							self			:		int					)							->	Optional[Any]:
      __lowerCAmelCase:   Any   	=      self.get_image_processor()
      __lowerCAmelCase:   List[Any]   	=      self.get_tokenizer()

      __lowerCAmelCase:   str   	=      OwlViTProcessor(tokenizer=UpperCamelCase__			,  image_processor=UpperCamelCase__					)

      __lowerCAmelCase:   Dict   	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __lowerCAmelCase:   Dict   	=      processor.batch_decode(UpperCamelCase__					)
      __lowerCAmelCase:   int   	=      tokenizer.batch_decode(UpperCamelCase__					)

      self.assertListEqual(UpperCamelCase__			,  UpperCamelCase__					)








"""simple docstring"""


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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  """switch_transformers"""
				SCREAMING_SNAKE_CASE_   :      Tuple         =  ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__(	self							:				List[str]     ,  UpperCamelCase__							:				List[str]=3_2_1_2_8     ,  UpperCamelCase__							:				str=7_6_8     ,  UpperCamelCase__							:				Union[str, Any]=6_4     ,  UpperCamelCase__							:				Optional[int]=2_0_4_8     ,  UpperCamelCase__							:				Dict=6_4     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Any=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Dict=1_2     ,  UpperCamelCase__							:				List[str]=8     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[int]=0.01     ,  UpperCamelCase__							:				Optional[int]="float32"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Dict=3_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2_8     ,  UpperCamelCase__							:				Union[str, Any]=0.1     ,  UpperCamelCase__							:				Optional[int]=1e-6     ,  UpperCamelCase__							:				Optional[Any]=0.001     ,  UpperCamelCase__							:				Dict=0.001     ,  UpperCamelCase__							:				int=1.0     ,  UpperCamelCase__							:				str="relu"     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Tuple=0     ,  UpperCamelCase__							:				str=1     ,  **UpperCamelCase__							:				Tuple     ,  )->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			vocab_size
						__lowerCAmelCase:       str          =			d_model
						__lowerCAmelCase:       str          =			d_kv
						__lowerCAmelCase:       str          =			d_ff

						__lowerCAmelCase:       List[str]          =			num_sparse_encoder_layers

						__lowerCAmelCase:       List[Any]          =			num_layers
						__lowerCAmelCase:       Optional[Any]          =			(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__lowerCAmelCase:       Tuple          =			num_sparse_decoder_layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_encoder_layers > 0:
								__lowerCAmelCase:       int          =			self.num_layers // self.num_sparse_encoder_layers
						else:
								__lowerCAmelCase:       Union[str, Any]          =			self.num_layers  # HACK: this will create 0 sparse layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_decoder_layers > 0:
								__lowerCAmelCase:       Dict          =			self.num_decoder_layers // self.num_sparse_decoder_layers
						else:
								__lowerCAmelCase:       Any          =			self.num_decoder_layers  # HACK: this will create 0 sparse layers

						__lowerCAmelCase:       Dict          =			num_heads
						__lowerCAmelCase:       Dict          =			num_experts
						__lowerCAmelCase:       Any          =			expert_capacity
						__lowerCAmelCase:       List[Any]          =			router_bias
						__lowerCAmelCase:       int          =			router_jitter_noise
						if router_dtype not in ["float32", "float16", "bfloat16"]:
								raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
						__lowerCAmelCase:       Dict          =			router_dtype

						__lowerCAmelCase:       Optional[Any]          =			router_ignore_padding_tokens
						__lowerCAmelCase:       Union[str, Any]          =			relative_attention_num_buckets
						__lowerCAmelCase:       str          =			relative_attention_max_distance

						__lowerCAmelCase:       Optional[int]          =			dropout_rate
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_epsilon
						__lowerCAmelCase:       int          =			initializer_factor
						__lowerCAmelCase:       Tuple          =			feed_forward_proj
						__lowerCAmelCase:       int          =			use_cache
						__lowerCAmelCase:       int          =			add_router_probs

						__lowerCAmelCase:       Optional[Any]          =			router_z_loss_coef
						__lowerCAmelCase:       Dict          =			router_aux_loss_coef

						__lowerCAmelCase:       Union[str, Any]          =			self.feed_forward_proj.split("-")
						__lowerCAmelCase:       Tuple          =			act_info[-1]
						__lowerCAmelCase:       str          =			act_info[0] == "gated"

						if len(UpperCamelCase__) > 1 and act_info[0] != "gated" or len(UpperCamelCase__) > 2:
								raise ValueError(
								    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
								    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
								    "'gated-gelu' or 'relu'")

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
								__lowerCAmelCase:       List[str]          =			"gelu_new"

						super().__init__(
						    pad_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__     ,  )


