



import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=99		,__lowerCamelCase=13		,__lowerCamelCase=16		,__lowerCamelCase=7		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=2		,__lowerCamelCase=32		,__lowerCamelCase=4		,__lowerCamelCase=4		,__lowerCamelCase=30		,__lowerCamelCase=0		,__lowerCamelCase=1		,__lowerCamelCase=2		,__lowerCamelCase=None		,)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					parent
						lowerCAmelCase__					:    Dict												=					batch_size
						lowerCAmelCase__					:    int												=					decoder_seq_length
						# For common tests
						lowerCAmelCase__					:    str												=					self.decoder_seq_length
						lowerCAmelCase__					:    List[str]												=					is_training
						lowerCAmelCase__					:    Optional[Any]												=					use_attention_mask
						lowerCAmelCase__					:    int												=					use_labels

						lowerCAmelCase__					:    Dict												=					vocab_size
						lowerCAmelCase__					:    Union[str, Any]												=					d_model
						lowerCAmelCase__					:    int												=					d_model
						lowerCAmelCase__					:    Optional[Any]												=					decoder_layers
						lowerCAmelCase__					:    Optional[int]												=					decoder_layers
						lowerCAmelCase__					:    str												=					decoder_ffn_dim
						lowerCAmelCase__					:    str												=					decoder_attention_heads
						lowerCAmelCase__					:    List[str]												=					decoder_attention_heads
						lowerCAmelCase__					:    int												=					eos_token_id
						lowerCAmelCase__					:    int												=					bos_token_id
						lowerCAmelCase__					:    Optional[Any]												=					pad_token_id
						lowerCAmelCase__					:    Tuple												=					decoder_start_token_id
						lowerCAmelCase__					:    Any												=					use_cache
						lowerCAmelCase__					:    List[str]												=					max_position_embeddings

						lowerCAmelCase__					:    Any												=					None
						lowerCAmelCase__					:    Union[str, Any]												=					decoder_seq_length
						lowerCAmelCase__					:    Union[str, Any]												=					2
						lowerCAmelCase__					:    List[str]												=					1



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					ids_tensor([self.batch_size, self.decoder_seq_length]		,self.vocab_size			)

						lowerCAmelCase__					:    Tuple												=					None
						if self.use_attention_mask:
										lowerCAmelCase__					:    Any												=					ids_tensor([self.batch_size, self.decoder_seq_length]		,vocab_size=2			)

						lowerCAmelCase__					:    Optional[Any]												=					None
						if self.use_labels:
										lowerCAmelCase__					:    Optional[int]												=					ids_tensor([self.batch_size, self.decoder_seq_length]		,self.vocab_size			)

						lowerCAmelCase__					:    Tuple												=					TrOCRConfig(
						    vocab_size=self.vocab_size		,d_model=self.d_model		,decoder_layers=self.decoder_layers		,decoder_ffn_dim=self.decoder_ffn_dim		,decoder_attention_heads=self.decoder_attention_heads		,eos_token_id=self.eos_token_id		,bos_token_id=self.bos_token_id		,use_cache=self.use_cache		,pad_token_id=self.pad_token_id		,decoder_start_token_id=self.decoder_start_token_id		,max_position_embeddings=self.max_position_embeddings		,)

						return (config, input_ids, attention_mask, lm_labels)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					TrOCRDecoder(config=__lowerCamelCase			).to(__lowerCamelCase			).eval()
						lowerCAmelCase__					:    Tuple												=					input_ids[:2]

						input_ids[input_ids == 0] += 1
						# first forward pass
						lowerCAmelCase__					:    Dict												=					model(__lowerCamelCase		,use_cache=__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					model(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					model(__lowerCamelCase		,use_cache=__lowerCamelCase			)

						self.parent.assertTrue(len(__lowerCamelCase			) == len(__lowerCamelCase			)			)
						self.parent.assertTrue(len(__lowerCamelCase			) == len(__lowerCamelCase			) + 1			)

						lowerCAmelCase__					:    str												=					outputs['''past_key_values''']

						# create hypothetical next token and extent to next_input_ids
						lowerCAmelCase__					:    Optional[int]												=					ids_tensor((2, 1)		,config.vocab_size - 1			) + 1

						# append to next input_ids and
						lowerCAmelCase__					:    str												=					torch.cat([input_ids, next_tokens]		,dim=-1			)

						lowerCAmelCase__					:    str												=					model(__lowerCamelCase			)['''last_hidden_state''']
						lowerCAmelCase__					:    str												=					model(__lowerCamelCase		,past_key_values=__lowerCamelCase			)['''last_hidden_state''']

						# select random slice
						lowerCAmelCase__					:    List[str]												=					ids_tensor((1,)		,output_from_past.shape[-1]			).item()
						lowerCAmelCase__					:    Tuple												=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
						lowerCAmelCase__					:    Optional[int]												=					output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						assert torch.allclose(__lowerCamelCase		,__lowerCamelCase		,atol=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.prepare_config_and_inputs()
						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    str												=					config_and_inputs

						lowerCAmelCase__					:    Optional[Any]												=					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict



@require_torch
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
		snake_case_     =(TrOCRForCausalLM,) if is_torch_available() else ()
		snake_case_     ={"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
		snake_case_     =True
		snake_case_     =False



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					TrOCRStandaloneDecoderModelTester(self		,is_training=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					ConfigTester(self		,config_class=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past(*__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return



		@unittest.skip('''The model doesn\'t support left padding'''			)  # and it's not used enough to be worth fixing :)
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						pass






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =KandinskyVaaPriorPipeline
		snake_case_     =["""prompt"""]
		snake_case_     =["""prompt""", """negative_prompt"""]
		snake_case_     =[
		    """num_images_per_prompt""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_     =False



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					CLIPTextConfig(
						    bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
						return CLIPTextModelWithProjection(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					PriorTransformer(**__lowerCamelCase			)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

						lowerCAmelCase__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

						lowerCAmelCase__					:    int												=					{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''scheduler''': scheduler,
						    '''image_processor''': image_processor,
						}

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Any												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''guidance_scale''': 4.0,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''np''',
						}
						return inputs



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

						lowerCAmelCase__					:    Optional[int]												=					self.get_dummy_components()

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

						lowerCAmelCase__					:    Tuple												=					pipe(
						    **self.get_dummy_inputs(__lowerCamelCase			)		,return_dict=__lowerCamelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

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



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					False

						self._test_inference_batch_single_identical(
						    test_max_difference=__lowerCamelCase		,relax_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)

