


import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 (
			    BioGptForCausalLM,
			    BioGptForSequenceClassification,
			    BioGptForTokenClassification,
			    BioGptModel,
			    BioGptTokenizer,
			)
			from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class    UpperCAmelCase					:




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







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







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

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

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

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

						UpperCamelCase         =					self.get_config()

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




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return BioGptConfig(
						    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=__magic_name__       ,       initializer_range=self.initializer_range       ,       )




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Dict       ,       __magic_name__							:		int       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptModel(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__						)
						UpperCamelCase         =					model(__magic_name__						)
						self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)						)




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







						"""simple docstring"""







						UpperCamelCase         =					BioGptForCausalLM(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__       ,       labels=__magic_name__						)
						self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Any       ,       *__magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptModel(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()

						# create attention mask
						UpperCamelCase         =					torch.ones(input_ids.shape       ,       dtype=torch.long       ,       device=__magic_name__						)
						UpperCamelCase         =					self.seq_length // 2
						UpperCamelCase         =					0

						# first forward pass
						UpperCamelCase  ,  UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__						).to_tuple()

						# create hypothetical next token and extent to next_input_ids
						UpperCamelCase         =					ids_tensor((self.batch_size, 1)       ,       config.vocab_size						)

						# change a random masked slice from input_ids
						UpperCamelCase         =					ids_tensor((1,)       ,       __magic_name__						).item() + 1
						UpperCamelCase         =					ids_tensor((self.batch_size, 1)       ,       config.vocab_size						).squeeze(-1						)
						UpperCamelCase         =					random_other_next_tokens

						# append to next input_ids and attn_mask
						UpperCamelCase         =					torch.cat([input_ids, next_tokens]       ,       dim=-1						)
						UpperCamelCase         =					torch.cat(
						    [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,       dtype=torch.long       ,       device=__magic_name__						)]       ,       dim=1       ,       )

						# get two different outputs
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__						)["""last_hidden_state"""]
						UpperCamelCase         =					model(__magic_name__       ,       past_key_values=__magic_name__       ,       attention_mask=__magic_name__						)["""last_hidden_state"""]

						# select random slice
						UpperCamelCase         =					ids_tensor((1,)       ,       output_from_past.shape[-1]						).item()
						UpperCamelCase         =					output_from_no_past[:, -1, random_slice_idx].detach()
						UpperCamelCase         =					output_from_past[:, 0, random_slice_idx].detach()

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




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[int]       ,       *__magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptModel(config=__magic_name__						).to(__magic_name__						).eval()

						UpperCamelCase         =					torch.ones(input_ids.shape       ,       dtype=torch.long       ,       device=__magic_name__						)

						# first forward pass
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       use_cache=__magic_name__						)

						UpperCamelCase  ,  UpperCamelCase         =					outputs.to_tuple()

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

						# append to next input_ids and
						UpperCamelCase         =					torch.cat([input_ids, next_tokens]       ,       dim=-1						)
						UpperCamelCase         =					torch.cat([attention_mask, next_attn_mask]       ,       dim=-1						)

						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__						)["""last_hidden_state"""]
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       past_key_values=__magic_name__						)[
						    """last_hidden_state"""
						]

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




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		List[str]       ,       *__magic_name__							:		Dict       ,       __magic_name__							:		Optional[Any]=False						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptForCausalLM(__magic_name__						)
						model.to(__magic_name__						)
						if gradient_checkpointing:
									model.gradient_checkpointing_enable()

						UpperCamelCase         =					model(__magic_name__       ,       labels=__magic_name__						)
						self.parent.assertEqual(result.loss.shape       ,       ()						)
						self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)						)
						result.loss.backward()




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Any       ,       *__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptModel(__magic_name__						)
						UpperCamelCase         =					model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers						)
						for key in model.state_dict().keys():
									if "c_proj" in key and "weight" in key:
												self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]						) - model_std						)       ,       0.001						)
												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]						) - 0.0						)       ,       0.01						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Any       ,       __magic_name__							:		str       ,       *__magic_name__							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.num_labels
						UpperCamelCase         =					BioGptForTokenClassification(__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)
						self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.num_labels)						)





			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







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




@require_torch
class    UpperCAmelCase					(       __snake_case      , __snake_case      , __snake_case      , unittest.TestCase    ):
			lowercase						= (
			    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
			    if is_torch_available()
			    else ()
			)
			lowercase						= (BioGptForCausalLM,) if is_torch_available() else ()
			lowercase						= (
			    {
			        """feature-extraction""": BioGptModel,
			        """text-classification""": BioGptForSequenceClassification,
			        """text-generation""": BioGptForCausalLM,
			        """token-classification""": BioGptForTokenClassification,
			        """zero-shot""": BioGptForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowercase						= False




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptModelTester(self						)
						UpperCamelCase         =					ConfigTester(self       ,       config_class=__magic_name__       ,       hidden_size=3_7						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						self.config_tester.run_common_tests()




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







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




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







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*__magic_name__       ,       gradient_checkpointing=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*__magic_name__						)




			@slow
			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
						model.to(__magic_name__						)
						UpperCamelCase         =					BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)

						UpperCamelCase         =					"""left"""

						# Define PAD Token = EOS Token = 50256
						UpperCamelCase         =					tokenizer.eos_token
						UpperCamelCase         =					model.config.eos_token_id

						# use different length sentences to test batching
						UpperCamelCase         =					[
						    """Hello, my dog is a little""",
						    """Today, I""",
						]

						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""pt"""       ,       padding=__magic_name__						)
						UpperCamelCase         =					inputs["""input_ids"""].to(__magic_name__						)

						UpperCamelCase         =					model.generate(
						    input_ids=__magic_name__       ,       attention_mask=inputs["""attention_mask"""].to(__magic_name__						)       ,       )

						UpperCamelCase         =					tokenizer(sentences[0]       ,       return_tensors="""pt"""						).input_ids.to(__magic_name__						)
						UpperCamelCase         =					model.generate(input_ids=__magic_name__						)

						UpperCamelCase         =					inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
						UpperCamelCase         =					tokenizer(sentences[1]       ,       return_tensors="""pt"""						).input_ids.to(__magic_name__						)
						UpperCamelCase         =					model.generate(input_ids=__magic_name__       ,       max_length=model.config.max_length - num_paddings						)

						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__       ,       skip_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.decode(output_non_padded[0]       ,       skip_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.decode(output_padded[0]       ,       skip_special_tokens=__magic_name__						)

						UpperCamelCase         =					[
						    """Hello, my dog is a little bit bigger than a little bit.""",
						    """Today, I have a good idea of how to use the information""",
						]
						self.assertListEqual(__magic_name__       ,       __magic_name__						)
						self.assertListEqual(__magic_name__       ,       [non_padded_sentence, padded_sentence]						)




			@slow
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase         =					BioGptModel.from_pretrained(__magic_name__						)
									self.assertIsNotNone(__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase         =					3
						UpperCamelCase         =					input_dict["""input_ids"""]
						UpperCamelCase         =					input_ids.ne(1						).to(__magic_name__						)
						UpperCamelCase         =					ids_tensor([self.model_tester.batch_size]       ,       self.model_tester.type_sequence_label_size						)
						UpperCamelCase         =					BioGptForSequenceClassification(__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       labels=__magic_name__						)
						self.assertEqual(result.logits.shape       ,       (self.model_tester.batch_size, self.model_tester.num_labels)						)





			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







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




@require_torch
class    UpperCAmelCase					(       unittest.TestCase    ):




			@slow
			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
						UpperCamelCase         =					torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]						)
						UpperCamelCase         =					model(__magic_name__						)[0]

						UpperCamelCase         =					4_2_3_8_4

						UpperCamelCase         =					torch.Size((1, 5, vocab_size)						)
						self.assertEqual(output.shape       ,       __magic_name__						)

						UpperCamelCase         =					torch.tensor(
						    [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]						)

						self.assertTrue(torch.allclose(output[:, :3, :3]       ,       __magic_name__       ,       atol=1e-4						)						)





			@slow
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)
						UpperCamelCase         =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
						model.to(__magic_name__						)

						torch.manual_seed(0						)
						UpperCamelCase         =					tokenizer("""COVID-19 is"""       ,       return_tensors="""pt"""						).to(__magic_name__						)
						UpperCamelCase         =					model.generate(
						    **__magic_name__       ,       min_length=1_0_0       ,       max_length=1_0_2_4       ,       num_beams=5       ,       early_stopping=__magic_name__       ,       )
						UpperCamelCase         =					tokenizer.decode(output_ids[0]       ,       skip_special_tokens=__magic_name__						)

						UpperCamelCase         =					(
						    """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
						    """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
						    """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
						    """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
						    """ more than 800,000 deaths."""
						)
						self.assertEqual(__magic_name__       ,       __magic_name__						)








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    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					tempfile.mkdtemp()

						# fmt: off
						UpperCamelCase         =					["""""", """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
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
						UpperCamelCase         =					{"""unk_token""": """<unk>"""}

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

						UpperCamelCase         =					{
						    """do_resize""": True,
						    """size""": 2_0,
						    """do_center_crop""": True,
						    """crop_size""": 1_8,
						    """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],
						}
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       __magic_name__						)
						with open(self.image_processor_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									json.dump(__magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		str       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		str						):







						"""simple docstring"""







						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					[np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta						)]

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

						return image_inputs




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_tokenizer()
						UpperCamelCase         =					self.get_rust_tokenizer()
						UpperCamelCase         =					self.get_image_processor()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_slow.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname       ,       use_fast=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_fast.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					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       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.tokenizer       ,       __magic_name__						)

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




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					OwlViTProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						UpperCamelCase         =					self.get_tokenizer(bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""						)
						UpperCamelCase         =					self.get_image_processor(do_normalize=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor.from_pretrained(
						    self.tmpdirname       ,       bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""       ,       do_normalize=__magic_name__						)

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

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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					image_processor(__magic_name__       ,       return_tensors="""np"""						)
						UpperCamelCase         =					processor(images=__magic_name__       ,       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   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""

						UpperCamelCase         =					processor(text=__magic_name__       ,       return_tensors="""np"""						)

						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""np"""						)

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




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

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

						UpperCamelCase         =					processor(text=__magic_name__       ,       images=__magic_name__						)

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

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




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					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(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					[["""cat""", """nasa badge"""], ["""person"""]]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					max([len(__magic_name__						) 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(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					inputs["""input_ids"""]
						UpperCamelCase         =					[
						    [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   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(images=__magic_name__       ,       query_images=__magic_name__						)

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

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





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

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

						UpperCamelCase         =					processor.batch_decode(__magic_name__						)
						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__						)

						self.assertListEqual(__magic_name__       ,       __magic_name__						)




