
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     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      tempfile.mkdtemp()

           # fmt: off
           lowercase							:     Optional[Any]       	=      ["""""", """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
           lowercase							:     Union[str, Any]       	=      dict(zip(snake_case		,range(len(snake_case      )      )      )      )
           lowercase							:     Tuple       	=      ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
           lowercase							:     Dict       	=      {"""unk_token""": """<unk>"""}

           lowercase							:     Tuple       	=      os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]      )
           lowercase							:     Any       	=      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(snake_case      ) + """\n"""      )
           with open(self.merges_file		,"""w"""		,encoding="""utf-8"""      ) as fp:
                  fp.write("""\n""".join(snake_case      )      )

           lowercase							:     List[Any]       	=      {
               """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],
           }
           lowercase							:     Optional[Any]       	=      os.path.join(self.tmpdirname		,snake_case      )
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""      ) as fp:
                  json.dump(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







           return OwlViTImageProcessor.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     Tuple       	=      [np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta      )]

           lowercase							:     Optional[int]       	=      [Image.fromarray(np.moveaxis(snake_case		,0		,-1      )      ) for x in image_inputs]

           return image_inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_tokenizer()
           lowercase							:     Optional[Any]       	=      self.get_rust_tokenizer()
           lowercase							:     Tuple       	=      self.get_image_processor()

           lowercase							:     str       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )
           processor_slow.save_pretrained(self.tmpdirname      )
           lowercase							:     List[str]       	=      OwlViTProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case      )

           lowercase							:     int       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )
           processor_fast.save_pretrained(self.tmpdirname      )
           lowercase							:     Optional[int]       	=      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		,snake_case      )
           self.assertIsInstance(processor_fast.tokenizer		,snake_case      )

           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		,snake_case      )
           self.assertIsInstance(processor_fast.image_processor		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      OwlViTProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()      )
           processor.save_pretrained(self.tmpdirname      )

           lowercase							:     int       	=      self.get_tokenizer(bos_token="""(BOS)"""		,eos_token="""(EOS)"""      )
           lowercase							:     Union[str, Any]       	=      self.get_image_processor(do_normalize=snake_case      )

           lowercase							:     Dict       	=      OwlViTProcessor.from_pretrained(
               self.tmpdirname		,bos_token="""(BOS)"""		,eos_token="""(EOS)"""		,do_normalize=snake_case      )

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

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()      )
           self.assertIsInstance(processor.image_processor		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.get_image_processor()
           lowercase							:     Tuple       	=      self.get_tokenizer()

           lowercase							:     Dict       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     Optional[Any]       	=      self.prepare_image_inputs()

           lowercase							:     Union[str, Any]       	=      image_processor(snake_case		,return_tensors="""np"""      )
           lowercase							:     Union[str, Any]       	=      processor(images=snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.get_image_processor()
           lowercase							:     Optional[int]       	=      self.get_tokenizer()

           lowercase							:     Optional[Any]       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     str       	=      """lower newer"""

           lowercase							:     Optional[Any]       	=      processor(text=snake_case		,return_tensors="""np"""      )

           lowercase							:     List[str]       	=      tokenizer(snake_case		,return_tensors="""np"""      )

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



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.get_image_processor()
           lowercase							:     List[str]       	=      self.get_tokenizer()

           lowercase							:     Optional[Any]       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     Optional[Any]       	=      """lower newer"""
           lowercase							:     List[Any]       	=      self.prepare_image_inputs()

           lowercase							:     List[Any]       	=      processor(text=snake_case		,images=snake_case      )

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

           # test if it raises when no input is passed
           with pytest.raises(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      """google/owlvit-base-patch32"""
           lowercase							:     Union[str, Any]       	=      OwlViTProcessor.from_pretrained(snake_case      )

           lowercase							:     List[Any]       	=      ["""cat""", """nasa badge"""]
           lowercase							:     Union[str, Any]       	=      processor(text=snake_case      )

           lowercase							:     List[Any]       	=      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(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      """google/owlvit-base-patch32"""
           lowercase							:     str       	=      OwlViTProcessor.from_pretrained(snake_case      )

           lowercase							:     str       	=      [["""cat""", """nasa badge"""], ["""person"""]]
           lowercase							:     int       	=      processor(text=snake_case      )

           lowercase							:     Union[str, Any]       	=      16
           lowercase							:     Union[str, Any]       	=      len(snake_case      )
           lowercase							:     Optional[int]       	=      max([len(snake_case      ) 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(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      """google/owlvit-base-patch32"""
           lowercase							:     Optional[int]       	=      OwlViTProcessor.from_pretrained(snake_case      )

           lowercase							:     Dict       	=      ["""cat""", """nasa badge"""]
           lowercase							:     Tuple       	=      processor(text=snake_case      )

           lowercase							:     List[Any]       	=      16
           lowercase							:     List[str]       	=      inputs["""input_ids"""]
           lowercase							:     List[str]       	=      [
               [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [49406, 6841, 11301, 49407, 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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_image_processor()
           lowercase							:     Union[str, Any]       	=      self.get_tokenizer()

           lowercase							:     int       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     int       	=      self.prepare_image_inputs()
           lowercase							:     Optional[int]       	=      self.prepare_image_inputs()

           lowercase							:     List[Any]       	=      processor(images=snake_case		,query_images=snake_case      )

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

           # test if it raises when no input is passed
           with pytest.raises(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.get_image_processor()
           lowercase							:     Optional[int]       	=      self.get_tokenizer()

           lowercase							:     Any       	=      OwlViTProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     str       	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:     List[Any]       	=      processor.batch_decode(snake_case      )
           lowercase							:     Dict       	=      tokenizer.batch_decode(snake_case      )

           self.assertListEqual(snake_case		,snake_case      )


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
