"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		str											=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase     :		Optional[int]											=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

_UpperCamelCase     :		List[Any]											=					{
    "squeezebert/squeezebert-uncased": 5_12,
    "squeezebert/squeezebert-mnli": 5_12,
    "squeezebert/squeezebert-mnli-headless": 5_12,
}


_UpperCamelCase     :		List[str]											=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}


class   UpperCAmelCase_			(					__SCREAMING_SNAKE_CASE):
      lowerCamelCase__	:					Dict										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					Tuple										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					Dict										=						PRETRAINED_INIT_CONFIGURATION
      lowerCamelCase__	:					Optional[Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					Optional[Any]										=						SqueezeBertTokenizer


      def __init__(     self	,    a=None	,    a=None	,    a=True	,    a="[UNK]"	,    a="[SEP]"	,    a="[PAD]"	,    a="[CLS]"	,    a="[MASK]"	,    a=True	,    a=None	,    **a	,    ) ->      Any:
             super().__init__(
                 _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    tokenize_chinese_chars=_a	,    strip_accents=_a	,    **_a	,    )

             lowercase__    :	Union[str, Any]     					=			json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
             if (
                 normalizer_state.get('lowercase'	,    _a							) != do_lower_case
                 or normalizer_state.get('strip_accents'	,    _a							) != strip_accents
                 or normalizer_state.get('handle_chinese_chars'	,    _a							) != tokenize_chinese_chars
             ):
                    lowercase__    :	List[Any]     					=			getattr(_a	,    normalizer_state.pop('type'							)							)
                    lowercase__    :	Union[str, Any]     					=			do_lower_case
                    lowercase__    :	List[str]     					=			strip_accents
                    lowercase__    :	Dict     					=			tokenize_chinese_chars
                    lowercase__    :	List[str]     					=			normalizer_class(**_a							)

             lowercase__    :	Any     					=			do_lower_case


      def 	_UpperCAmelCase (     self	,    a	,    a=None							) ->      Dict:
             lowercase__    :	Any     					=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

             if token_ids_a:
                    output += token_ids_a + [self.sep_token_id]

             return output


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[Any]:
             lowercase__    :	Dict     					=			[self.sep_token_id]
             lowercase__    :	Union[str, Any]     					=			[self.cls_token_id]
             if token_ids_a is None:
                    return len(cls + token_ids_a + sep							) * [0]
             return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[str]:
             lowercase__    :	Union[str, Any]     					=			self._tokenizer.model.save(_a	,    name=_a							)
             return tuple(_a							)







"""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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			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							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

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

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			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

