


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 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=99   ,    _lowerCamelCase=32   ,    _lowerCamelCase=5   ,    _lowerCamelCase=4   ,    _lowerCamelCase=37   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=3   ,    _lowerCamelCase=4   ,    _lowerCamelCase=None   ,    ):
       a		:Optional[Any]									=			parent
       a		:Tuple									=			batch_size
       a		:Union[str, Any]									=			seq_length
       a		:Tuple									=			is_training
       a		:List[str]									=			use_input_mask
       a		:Tuple									=			use_token_type_ids
       a		:Union[str, Any]									=			use_labels
       a		:List[Any]									=			vocab_size
       a		:Optional[Any]									=			hidden_size
       a		:Optional[int]									=			num_hidden_layers
       a		:Optional[int]									=			num_attention_heads
       a		:Tuple									=			intermediate_size
       a		:Optional[Any]									=			hidden_act
       a		:Optional[int]									=			hidden_dropout_prob
       a		:int									=			attention_probs_dropout_prob
       a		:Union[str, Any]									=			max_position_embeddings
       a		:Union[str, Any]									=			type_vocab_size
       a		:Any									=			type_sequence_label_size
       a		:Dict									=			initializer_range
       a		:str									=			num_labels
       a		:Dict									=			num_choices
       a		:int									=			scope




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size       )

       a		:Tuple									=			None
       if self.use_input_mask:
             a		:List[str]									=			random_attention_mask([self.batch_size, self.seq_length]       )

       a		:Union[str, Any]									=			None
       if self.use_token_type_ids:
             a		:List[Any]									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.type_vocab_size       )

       a		:str									=			None
       a		:str									=			None
       a		:Any									=			None
       if self.use_labels:
             a		:Any									=			ids_tensor([self.batch_size]   ,    self.type_sequence_label_size       )
             a		:Dict									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels       )
             a		:int									=			ids_tensor([self.batch_size]   ,    self.num_choices       )

       a		:int									=			self.get_config()

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




 def 			SCREAMING_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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:List[str]									=			BioGptModel(config=_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )
       a		:Any									=			model(_lowerCamelCase       )
       self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    ):
       a		:Optional[int]									=			BioGptForCausalLM(config=_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()
       a		:Tuple									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       a		:int									=			BioGptModel(config=_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()

       # create attention mask
       a		:Union[str, Any]									=			torch.ones(input_ids.shape   ,    dtype=torch.long   ,    device=_lowerCamelCase       )
       a		:Optional[Any]									=			self.seq_length // 2
       a		:Any									=			0

       # first forward pass
       a				,							a		:Optional[Any]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       ).to_tuple()

       # create hypothetical next token and extent to next_input_ids
       a		:Union[str, Any]									=			ids_tensor((self.batch_size, 1)   ,    config.vocab_size       )

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

       # append to next input_ids and attn_mask
       a		:Union[str, Any]									=			torch.cat([input_ids, next_tokens]   ,    dim=-1       )
       a		:int									=			torch.cat(
           [attn_mask, torch.ones((attn_mask.shape[0], 1)   ,    dtype=torch.long   ,    device=_lowerCamelCase       )]   ,    dim=1   ,    )

       # get two different outputs
       a		:Dict									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )['''last_hidden_state''']
       a		:Optional[Any]									=			model(_lowerCamelCase   ,    past_key_values=_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )['''last_hidden_state''']

       # select random slice
       a		:List[str]									=			ids_tensor((1,)   ,    output_from_past.shape[-1]       ).item()
       a		:int									=			output_from_no_past[:, -1, random_slice_idx].detach()
       a		:Optional[Any]									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       a		:Any									=			BioGptModel(config=_lowerCamelCase       ).to(_lowerCamelCase       ).eval()

       a		:List[Any]									=			torch.ones(input_ids.shape   ,    dtype=torch.long   ,    device=_lowerCamelCase       )

       # first forward pass
       a		:Optional[int]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    use_cache=_lowerCamelCase       )

       a				,							a		:List[Any]									=			outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       a		:Optional[Any]									=			ids_tensor((self.batch_size, 3)   ,    config.vocab_size       )
       a		:int									=			ids_tensor((self.batch_size, 3)   ,    2       )

       # append to next input_ids and
       a		:Tuple									=			torch.cat([input_ids, next_tokens]   ,    dim=-1       )
       a		:List[str]									=			torch.cat([attention_mask, next_attn_mask]   ,    dim=-1       )

       a		:Dict									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )['''last_hidden_state''']
       a		:int									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    past_key_values=_lowerCamelCase       )[
           '''last_hidden_state'''
       ]

       # select random slice
       a		:Optional[int]									=			ids_tensor((1,)   ,    output_from_past.shape[-1]       ).item()
       a		:Dict									=			output_from_no_past[:, -3:, random_slice_idx].detach()
       a		:Optional[int]									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    *_lowerCamelCase   ,    _lowerCamelCase=False       ):
       a		:Tuple									=			BioGptForCausalLM(_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       if gradient_checkpointing:
             model.gradient_checkpointing_enable()

       a		:Dict									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       a		:List[str]									=			BioGptModel(_lowerCamelCase       )
       a		:Optional[int]									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    *_lowerCamelCase       ):
       a		:str									=			self.num_labels
       a		:Tuple									=			BioGptForTokenClassification(_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()
       a		:Any									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.prepare_config_and_inputs()
       (
           (
           a
       )				,							(
           a
       )				,							(
           a
       )				,							(
           a
       )				,							(
           a
       )				,							(
           a
       )				,							(
           a
       )				,							
       )		:int									=			config_and_inputs
       a		:List[str]									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			BioGptModelTester(self       )
       a		:int									=			ConfigTester(self   ,    config_class=_lowerCamelCase   ,    hidden_size=37       )




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             a		:Optional[int]									=			type
             self.model_tester.create_and_check_model(*_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_forward_and_backwards(*_lowerCamelCase   ,    gradient_checkpointing=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*_lowerCamelCase       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
       model.to(_lowerCamelCase       )
       a		:Union[str, Any]									=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

       a		:Tuple									=			'''left'''

       # Define PAD Token = EOS Token = 50256
       a		:Tuple									=			tokenizer.eos_token
       a		:Tuple									=			model.config.eos_token_id

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

       a		:List[Any]									=			tokenizer(_lowerCamelCase   ,    return_tensors='''pt'''   ,    padding=_lowerCamelCase       )
       a		:str									=			inputs['''input_ids'''].to(_lowerCamelCase       )

       a		:str									=			model.generate(
           input_ids=_lowerCamelCase   ,    attention_mask=inputs['''attention_mask'''].to(_lowerCamelCase       )   ,    )

       a		:int									=			tokenizer(sentences[0]   ,    return_tensors='''pt'''       ).input_ids.to(_lowerCamelCase       )
       a		:Optional[Any]									=			model.generate(input_ids=_lowerCamelCase       )

       a		:Optional[Any]									=			inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
       a		:Union[str, Any]									=			tokenizer(sentences[1]   ,    return_tensors='''pt'''       ).input_ids.to(_lowerCamelCase       )
       a		:Optional[Any]									=			model.generate(input_ids=_lowerCamelCase   ,    max_length=model.config.max_length - num_paddings       )

       a		:int									=			tokenizer.batch_decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )
       a		:Tuple									=			tokenizer.decode(output_non_padded[0]   ,    skip_special_tokens=_lowerCamelCase       )
       a		:int									=			tokenizer.decode(output_padded[0]   ,    skip_special_tokens=_lowerCamelCase       )

       a		:Any									=			[
           '''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 			SCREAMING_SNAKE_CASE__							(						self       ):
       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a		:str									=			BioGptModel.from_pretrained(_lowerCamelCase       )
             self.assertIsNotNone(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:Union[str, Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
       a		:Tuple									=			3
       a		:str									=			input_dict['''input_ids''']
       a		:int									=			input_ids.ne(1       ).to(_lowerCamelCase       )
       a		:Dict									=			ids_tensor([self.model_tester.batch_size]   ,    self.model_tester.type_sequence_label_size       )
       a		:int									=			BioGptForSequenceClassification(_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    labels=_lowerCamelCase       )
       self.assertEqual(result.logits.shape   ,    (self.model_tester.batch_size, self.model_tester.num_labels)       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:List[Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
       a		:Dict									=			3
       a		:Dict									=			'''multi_label_classification'''
       a		:Dict									=			input_dict['''input_ids''']
       a		:Tuple									=			input_ids.ne(1       ).to(_lowerCamelCase       )
       a		:Optional[Any]									=			ids_tensor(
           [self.model_tester.batch_size, config.num_labels]   ,    self.model_tester.type_sequence_label_size       ).to(torch.float       )
       a		:Union[str, Any]									=			BioGptForSequenceClassification(_lowerCamelCase       )
       model.to(_lowerCamelCase       )
       model.eval()
       a		:str									=			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 						_snake_case    ( unittest.TestCase				):




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
       a		:Optional[int]									=			torch.tensor([[2, 4805, 9, 656, 21]]       )
       a		:Dict									=			model(_lowerCamelCase       )[0]

       a		:str									=			4_2384

       a		:Union[str, Any]									=			torch.Size((1, 5, vocab_size)       )
       self.assertEqual(output.shape   ,    _lowerCamelCase       )

       a		:Dict									=			torch.tensor(
           [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]       )

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




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )
       a		:int									=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
       model.to(_lowerCamelCase       )

       torch.manual_seed(0       )
       a		:Optional[Any]									=			tokenizer('''COVID-19 is'''   ,    return_tensors='''pt'''       ).to(_lowerCamelCase       )
       a		:Any									=			model.generate(
           **_lowerCamelCase   ,    min_length=100   ,    max_length=1024   ,    num_beams=5   ,    early_stopping=_lowerCamelCase   ,    )
       a		:int									=			tokenizer.decode(output_ids[0]   ,    skip_special_tokens=_lowerCamelCase       )

       a		:Dict									=			(
           '''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       )









import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			UNetaDModel(
           block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2







@slow
@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2





