
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 				lowercase					:



		def __init__( self				,      A_				,      A_=99				,      A_=13				,      A_=16				,      A_=7				,      A_=True				,      A_=True				,      A_=True				,      A_=False				,      A_=True				,      A_=2				,      A_=32				,      A_=4				,      A_=4				,      A_=30				,      A_=0				,      A_=1				,      A_=2				,      A_=None				,      )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     decoder_seq_length
				# For common tests
				UpperCamelCase		=     self.decoder_seq_length
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_attention_mask
				UpperCamelCase		=     use_labels

				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     d_model
				UpperCamelCase		=     d_model
				UpperCamelCase		=     decoder_layers
				UpperCamelCase		=     decoder_layers
				UpperCamelCase		=     decoder_ffn_dim
				UpperCamelCase		=     decoder_attention_heads
				UpperCamelCase		=     decoder_attention_heads
				UpperCamelCase		=     eos_token_id
				UpperCamelCase		=     bos_token_id
				UpperCamelCase		=     pad_token_id
				UpperCamelCase		=     decoder_start_token_id
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     max_position_embeddings

				UpperCamelCase		=     None
				UpperCamelCase		=     decoder_seq_length
				UpperCamelCase		=     2
				UpperCamelCase		=     1



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     ids_tensor([self.batch_size, self.decoder_seq_length]				,      self.vocab_size       )

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

				UpperCamelCase		=     None
				if self.use_labels:
						UpperCamelCase		=     ids_tensor([self.batch_size, self.decoder_seq_length]				,      self.vocab_size       )

				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     True
				UpperCamelCase		=     TrOCRDecoder(config=A_       ).to(A_       ).eval()
				UpperCamelCase		=     input_ids[:2]

				input_ids[input_ids == 0] += 1
				# first forward pass
				UpperCamelCase		=     model(A_				,      use_cache=A_       )
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     model(A_				,      use_cache=A_       )

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

				UpperCamelCase		=     outputs['past_key_values']

				# create hypothetical next token and extent to next_input_ids
				UpperCamelCase		=     ids_tensor((2, 1)				,      config.vocab_size - 1       ) + 1

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

				UpperCamelCase		=     model(A_       )['last_hidden_state']
				UpperCamelCase		=     model(A_				,      past_key_values=A_       )['last_hidden_state']

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

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



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs

				UpperCamelCase		=     {'input_ids': input_ids, 'attention_mask': attention_mask}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       Tuple	     =				(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
		__lowercase       :       Union[str, Any]	     =				(TrOCRForCausalLM,) if is_torch_available() else ()
		__lowercase       :       Union[str, Any]	     =				{"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
		__lowercase       :       Tuple	     =				True
		__lowercase       :       List[str]	     =				False



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     TrOCRStandaloneDecoderModelTester(self				,      is_training=A_       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*A_       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				return



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


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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