
"""simple docstring"""





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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=True							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=3_2							,  lowerCAmelCase__=5							,  lowerCAmelCase__=4							,  lowerCAmelCase__=3_7							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=3							,  lowerCAmelCase__=4							,  lowerCAmelCase__=None							,  ):
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = seq_length
				__SCREAMING_SNAKE_CASE            = is_training
				__SCREAMING_SNAKE_CASE            = use_input_mask
				__SCREAMING_SNAKE_CASE            = use_token_type_ids
				__SCREAMING_SNAKE_CASE            = use_labels
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = type_sequence_label_size
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = num_labels
				__SCREAMING_SNAKE_CASE            = num_choices
				__SCREAMING_SNAKE_CASE            = scope




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size)

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

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

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

				__SCREAMING_SNAKE_CASE            = self.get_config()

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




		def 	snake_case_     (     self):
				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=lowerCAmelCase__							,  initializer_range=self.initializer_range							,  )




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = BioGptModel(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__)
				self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size))




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = BioGptForCausalLM(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  labels=lowerCAmelCase__)
				self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size))




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  *lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = BioGptModel(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()

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

				# first forward pass
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__).to_tuple()

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

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

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

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

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

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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  *lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = BioGptModel(config=lowerCAmelCase__).to(lowerCAmelCase__).eval()

				__SCREAMING_SNAKE_CASE            = torch.ones(input_ids.shape							,  dtype=torch.long							,  device=lowerCAmelCase__)

				# first forward pass
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  use_cache=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = outputs.to_tuple()

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

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

				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)["""last_hidden_state"""]
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  past_key_values=lowerCAmelCase__)[
				    """last_hidden_state"""
				]

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

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

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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  *lowerCAmelCase__							,  lowerCAmelCase__=False):
				__SCREAMING_SNAKE_CASE            = BioGptForCausalLM(lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				if gradient_checkpointing:
						model.gradient_checkpointing_enable()

				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  labels=lowerCAmelCase__)
				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 	snake_case_     (     self							,  lowerCAmelCase__							,  *lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = BioGptModel(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 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)							,  0.0_01)
								self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)							,  0.01)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  *lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.num_labels
				__SCREAMING_SNAKE_CASE            = BioGptForTokenClassification(lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)
				self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels))




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



@require_torch
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  str						   =		(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		__lowercase							:  str						   =		(BioGptForCausalLM,) if is_torch_available() else ()
		__lowercase							:  Dict						   =		(
		    {
		        '''feature-extraction''': BioGptModel,
		        '''text-classification''': BioGptForSequenceClassification,
		        '''text-generation''': BioGptForCausalLM,
		        '''token-classification''': BioGptForTokenClassification,
		        '''zero-shot''': BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase							:  Union[str, Any]						   =		False




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = BioGptModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=lowerCAmelCase__							,  hidden_size=3_7)




		def 	snake_case_     (     self):
				self.config_tester.run_common_tests()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						__SCREAMING_SNAKE_CASE            = type
						self.model_tester.create_and_check_model(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__							,  gradient_checkpointing=lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__)




		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""")
				model.to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BioGptTokenizer.from_pretrained("""microsoft/biogpt""")

				__SCREAMING_SNAKE_CASE            = """left"""

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

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

				__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""pt"""							,  padding=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = inputs["""input_ids"""].to(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = model.generate(
				    input_ids=lowerCAmelCase__							,  attention_mask=inputs["""attention_mask"""].to(lowerCAmelCase__)							,  )

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

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

				__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_non_padded[0]							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_padded[0]							,  skip_special_tokens=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = [
				    """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(lowerCAmelCase__							,  lowerCAmelCase__)
				self.assertListEqual(lowerCAmelCase__							,  [non_padded_sentence, padded_sentence])




		@slow
		def 	snake_case_     (     self):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__SCREAMING_SNAKE_CASE            = BioGptModel.from_pretrained(lowerCAmelCase__)
						self.assertIsNotNone(lowerCAmelCase__)




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




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



@require_torch
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""")
				__SCREAMING_SNAKE_CASE            = torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]])
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__)[0]

				__SCREAMING_SNAKE_CASE            = 4_2_3_8_4

				__SCREAMING_SNAKE_CASE            = torch.Size((1, 5, vocab_size))
				self.assertEqual(output.shape							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]])

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




		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = BioGptTokenizer.from_pretrained("""microsoft/biogpt""")
				__SCREAMING_SNAKE_CASE            = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""")
				model.to(lowerCAmelCase__)

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

				__SCREAMING_SNAKE_CASE            = (
				    """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(lowerCAmelCase__							,  lowerCAmelCase__)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1 / 255				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					size if size is not None else {'''shortest_edge''': 224}
         A_      :				Any		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''		)

         A_      :				str		     =					do_resize
         A_      :				Tuple		     =					size
         A_      :				Optional[Any]		     =					resample
         A_      :				Tuple		     =					do_center_crop
         A_      :				List[Any]		     =					crop_size
         A_      :				Optional[int]		     =					do_rescale
         A_      :				Tuple		     =					rescale_factor
         A_      :				Any		     =					do_normalize
         A_      :				int		     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_      :				Any		     =					image_std if image_std is not None else OPENAI_CLIP_STD
         A_      :				Any		     =					do_convert_rgb



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
         A_      :				Any		     =					get_resize_output_image_size(_SCREAMING_SNAKE_CASE				,       size=size['''shortest_edge''']				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         return resize(_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				str		     =					get_size_dict(_SCREAMING_SNAKE_CASE		)
         if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
         return center_crop(_SCREAMING_SNAKE_CASE				,       size=(size['''height'''], size['''width'''])				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





         return rescale(_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         return normalize(_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,       **_SCREAMING_SNAKE_CASE				,       )->PIL.Image.Image:




         '''simple docstring'''





         A_      :				Optional[int]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				int		     =					size if size is not None else self.size
         A_      :				Optional[int]		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					resample if resample is not None else self.resample
         A_      :				Any		     =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A_      :				List[str]		     =					crop_size if crop_size is not None else self.crop_size
         A_      :				int		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				int		     =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A_      :				int		     =					do_normalize if do_normalize is not None else self.do_normalize
         A_      :				Tuple		     =					image_mean if image_mean is not None else self.image_mean
         A_      :				Tuple		     =					image_std if image_std is not None else self.image_std
         A_      :				List[str]		     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_      :				Optional[int]		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

         if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''		)

         if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

         if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           A_      :				List[str]		     =					[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

         # All transformations expect numpy arrays.
         A_      :				int		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

         if do_resize:
           A_      :				Tuple		     =					[self.resize(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_center_crop:
           A_      :				Union[str, Any]		     =					[self.center_crop(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				Tuple		     =					[self.rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_normalize:
           A_      :				List[Any]		     =					[self.normalize(image=_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				str		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Optional[Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)




