




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		IFImgaImgSuperResolutionPipeline
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}		)
      UpperCAmelCase_        =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Any:
       return self._get_superresolution_dummy_components()
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :int ,      lowerCamelCase   :Dict=0      )     ->			int:
       if str(lowerCamelCase      ).startswith("mps"      ):
        UpperCAmelCase__							 =						torch.manual_seed(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 16, 16) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						{
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_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   :List[Any]      )     ->			Optional[Any]:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda" ,      reason="float16 requires CUDA"      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[str]:
       # 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   :int      )     ->			List[str]:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Dict:
       self._test_save_load_local()







      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2 ,      )











# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





