



"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_						:    Union[str, Any]				=      logging.get_logger(__name__)

A_						:    List[Any]				=      {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= '''trocr'''
	_SCREAMING_SNAKE_CASE	:		Dict  						= ['''past_key_values''']
	_SCREAMING_SNAKE_CASE	:		int  						= {
	    '''num_attention_heads''': '''decoder_attention_heads''',
	    '''hidden_size''': '''d_model''',
	    '''num_hidden_layers''': '''decoder_layers''',
	}





	def __init__(      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       int=50_265     ,       _SCREAMING_SNAKE_CASE				:       Dict=1_024     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=16     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=4_096     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="gelu"     ,       _SCREAMING_SNAKE_CASE				:       Dict=512     ,       _SCREAMING_SNAKE_CASE				:       int=0.1     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=0.0     ,       _SCREAMING_SNAKE_CASE				:       int=0.0     ,       _SCREAMING_SNAKE_CASE				:       int=2     ,       _SCREAMING_SNAKE_CASE				:       str=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=0.0     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=False     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=True     ,       _SCREAMING_SNAKE_CASE				:       str=1     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       str=2     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]     ,       )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     vocab_size
					SCREAMING_SNAKE_CASE						:							str							=     d_model
					SCREAMING_SNAKE_CASE						:							Tuple							=     decoder_layers
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     decoder_attention_heads
					SCREAMING_SNAKE_CASE						:							str							=     decoder_ffn_dim
					SCREAMING_SNAKE_CASE						:							str							=     activation_function
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							List[Any]							=     dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     attention_dropout
					SCREAMING_SNAKE_CASE						:							Any							=     activation_dropout
					SCREAMING_SNAKE_CASE						:							int							=     init_std
					SCREAMING_SNAKE_CASE						:							Tuple							=     decoder_layerdrop
					SCREAMING_SNAKE_CASE						:							List[Any]							=     use_cache
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     scale_embedding
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     use_learned_position_embeddings
					SCREAMING_SNAKE_CASE						:							List[str]							=     layernorm_embedding

					super().__init__(
					    pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       decoder_start_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )










"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A_						:    Union[str, Any]				=      False
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	pass




@nightly
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[str]:





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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





