

'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard





@slow
@require_flax
class 							_snake_case       (				unittest.TestCase  ):
   def 			__UpperCamelCase  (       self	:					Union[str, Any]							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
   def 			__UpperCamelCase  (       self	:					Optional[Any]							):
     SCREAMING_SNAKE_CASE:str  					=    FlaxControlNetModel.from_pretrained(
         "lllyasviel/sd-controlnet-canny"   ,from_pt=_a   ,dtype=jnp.bfloataa							)
     SCREAMING_SNAKE_CASE:str  					=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"   ,controlnet=_a   ,from_pt=_a   ,dtype=jnp.bfloataa							)
     SCREAMING_SNAKE_CASE:List[Any]  					=    controlnet_params

     SCREAMING_SNAKE_CASE:Any  					=    "bird"
     SCREAMING_SNAKE_CASE:Optional[int]  					=    jax.device_count()
     SCREAMING_SNAKE_CASE:int  					=    pipe.prepare_text_inputs([prompts] * num_samples							)

     SCREAMING_SNAKE_CASE:Tuple  					=    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"							)
     SCREAMING_SNAKE_CASE:List[Any]  					=    pipe.prepare_image_inputs([canny_image] * num_samples							)

     SCREAMING_SNAKE_CASE:List[str]  					=    jax.random.PRNGKey(0							)
     SCREAMING_SNAKE_CASE:str  					=    jax.random.split(_a   ,jax.device_count()							)

     SCREAMING_SNAKE_CASE:List[Any]  					=    replicate(_a							)
     SCREAMING_SNAKE_CASE:Tuple  					=    shard(_a							)
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    shard(_a							)

     SCREAMING_SNAKE_CASE:str  					=    pipe(
         prompt_ids=_a   ,image=_a   ,params=_a   ,prng_seed=_a   ,num_inference_steps=50   ,jit=_a   ,).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     SCREAMING_SNAKE_CASE:Dict  					=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
     SCREAMING_SNAKE_CASE:Dict  					=    images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE:Optional[int]  					=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
     SCREAMING_SNAKE_CASE:Dict  					=    jnp.array(
         [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
     print(F'''output_slice: {output_slice}'''							)
     assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







   def 			__UpperCamelCase  (       self	:					str							):
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    FlaxControlNetModel.from_pretrained(
         "lllyasviel/sd-controlnet-openpose"   ,from_pt=_a   ,dtype=jnp.bfloataa							)
     SCREAMING_SNAKE_CASE:str  					=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"   ,controlnet=_a   ,from_pt=_a   ,dtype=jnp.bfloataa							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    controlnet_params

     SCREAMING_SNAKE_CASE:str  					=    "Chef in the kitchen"
     SCREAMING_SNAKE_CASE:List[Any]  					=    jax.device_count()
     SCREAMING_SNAKE_CASE:int  					=    pipe.prepare_text_inputs([prompts] * num_samples							)

     SCREAMING_SNAKE_CASE:List[Any]  					=    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"							)
     SCREAMING_SNAKE_CASE:Dict  					=    pipe.prepare_image_inputs([pose_image] * num_samples							)

     SCREAMING_SNAKE_CASE:List[str]  					=    jax.random.PRNGKey(0							)
     SCREAMING_SNAKE_CASE:Any  					=    jax.random.split(_a   ,jax.device_count()							)

     SCREAMING_SNAKE_CASE:int  					=    replicate(_a							)
     SCREAMING_SNAKE_CASE:List[Any]  					=    shard(_a							)
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    shard(_a							)

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    pipe(
         prompt_ids=_a   ,image=_a   ,params=_a   ,prng_seed=_a   ,num_inference_steps=50   ,jit=_a   ,).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE:Any  					=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
     SCREAMING_SNAKE_CASE:Any  					=    jnp.array(
         [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
     print(F'''output_slice: {output_slice}'''							)
     assert jnp.abs(output_slice - expected_slice							).max() < 1e-2








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Dict   		=       ["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   _A			:       str   		=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


