




'''simple docstring'''







import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   A     ='0.12'  # assumed parallelism: 8


if is_torch_available():
   import torch





def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]    ,   _a	:							Dict=None     ):
    if rng is None:
        UpperCAmelCase			       =		random.Random()

    UpperCAmelCase			       =		1
    for dim in shape:
        total_dims *= dim

    UpperCAmelCase			       =		[]
    for _ in range(_a     ):
        values.append(rng.randint(0    ,   vocab_size - 1     )     )

    UpperCAmelCase			       =		np.array(_a    ,   dtype=jnp.intaa     ).reshape(_a     )

    return output





def 						snake_case_							(_a	:							Any    ,   _a	:							int=None     ):
    UpperCAmelCase			       =		ids_tensor(_a    ,   vocab_size=2    ,   rng=_a     )
    # make sure that at least one token is attended to for each batch
    UpperCAmelCase			       =		1
    return attn_mask


@require_flax
class 			_a  :
       __a				:     Dict   				=					None
       __a				:     List[Any]   				=					()
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           # cut to half length & take max batch_size 3
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		inputs['''input_ids'''].shape[-1] // 2
           UpperCAmelCase			       =		inputs['''input_ids'''][:max_batch_size, :sequence_length]

           UpperCAmelCase			       =		jnp.ones_like(lowercase				)
           UpperCAmelCase			       =		attention_mask[:max_batch_size, :sequence_length]

           # generate max 5 tokens
           UpperCAmelCase			       =		input_ids.shape[-1] + 5
           if config.eos_token_id is not None and config.pad_token_id is None:
               # hack to allow generate for models such as GPT2 as is done in `generate()`
               UpperCAmelCase			       =		config.eos_token_id
           return config, input_ids, attention_mask, max_length
       @is_pt_flax_cross_test
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		0

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCAmelCase			       =		getattr(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		pt_model_class(lowercase				).eval()
               UpperCAmelCase			       =		load_flax_weights_in_pytorch_model(lowercase   ,				flax_model.params				)

               UpperCAmelCase			       =		flax_model.generate(lowercase				).sequences
               UpperCAmelCase			       =		pt_model.generate(torch.tensor(lowercase   ,				dtype=torch.long				)				)

               if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   UpperCAmelCase			       =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

               self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,				flax_generation_outputs.tolist()				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		2

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[0]   ,				input_ids.shape[0] * config.num_return_sequences				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		0.8
           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		0.3
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()
           UpperCAmelCase			       =		max_length
           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		8
           UpperCAmelCase			       =		9

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		False
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		True
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)







       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		self._get_input_ids_and_config()

           # pad attention mask on the left
           UpperCAmelCase			       =		attention_mask.at[(0, 0)].set(0				)

           UpperCAmelCase			       =		2
           UpperCAmelCase			       =		max_length

           for model_class in self.all_generative_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)

               UpperCAmelCase			       =		model.generate(lowercase   ,				attention_mask=lowercase				).sequences
               self.assertEqual(generation_outputs.shape[-1]   ,				lowercase				)

               UpperCAmelCase			       =		jit(model.generate				)
               UpperCAmelCase			       =		jit_generate(lowercase   ,				attention_mask=lowercase				).sequences

               self.assertListEqual(generation_outputs.tolist()   ,				jit_generation_outputs.tolist()				)






@require_flax
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
           UpperCAmelCase			       =		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

           UpperCAmelCase			       =		'''Hello world'''
           UpperCAmelCase			       =		tokenizer(lowercase   ,				return_tensors='''np'''				).input_ids

           # typos are quickly detected (the correct argument is `do_sample`)
           with self.assertRaisesRegex(lowercase   ,				'''do_samples'''				):
               model.generate(lowercase   ,				do_samples=lowercase				)

           # arbitrary arguments that will not be used anywhere are also not accepted
           with self.assertRaisesRegex(lowercase   ,				'''foo'''				):
               UpperCAmelCase			       =		{'''foo''': '''bar'''}
               model.generate(lowercase   ,				**lowercase				)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
