






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					_snake_case						(			UpperCAmelCase__       ,    unittest.TestCase ):
 _A          =   LEDTokenizer
 _A          =   LEDTokenizerFast
 _A          =   True






 def    lowerCAmelCase_							(		self       )						->    List[str]:
     super().setUp()
     snake_case__      :Any         =   [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     snake_case__      :Tuple         =   dict(zip(lowerCamelCase__					,range(len(lowerCamelCase__       )       )       )       )
     snake_case__      :Tuple         =   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     snake_case__      :Optional[int]         =   {"unk_token": "<unk>"}

     snake_case__      :Tuple         =   os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES["vocab_file"]       )
     snake_case__      :str         =   os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES["merges_file"]       )
     with open(self.vocab_file					,"w"					,encoding="utf-8"       ) as fp:
         fp.write(json.dumps(lowerCamelCase__       ) + "\n"       )
     with open(self.merges_file					,"w"					,encoding="utf-8"       ) as fp:
         fp.write("\n".join(lowerCamelCase__       )       )






 def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    Any:
     kwargs.update(self.special_tokens_map       )
     return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowerCamelCase__       )






 def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    Optional[Any]:
     kwargs.update(self.special_tokens_map       )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,**lowerCamelCase__       )






 def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[Any]:
     return "lower newer", "lower newer"






 @cached_property
 def    lowerCAmelCase_							(		self       )						->    str:
     return LEDTokenizer.from_pretrained("allenai/led-base-16384"       )






 @cached_property
 def    lowerCAmelCase_							(		self       )						->    Any:
     return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    str:
     snake_case__      :int         =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
     snake_case__      :Dict         =   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :Any         =   tokenizer(lowerCamelCase__					,max_length=len(lowerCamelCase__       )					,padding=lowerCamelCase__					,return_tensors="pt"       )
         self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__       )

         self.assertEqual((2, 9)					,batch.input_ids.shape       )
         self.assertEqual((2, 9)					,batch.attention_mask.shape       )
         snake_case__      :List[Any]         =   batch.input_ids.tolist()[0]
         self.assertListEqual(lowerCamelCase__					,lowerCamelCase__       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
     snake_case__      :Union[str, Any]         =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :Optional[Any]         =   tokenizer(lowerCamelCase__					,padding=lowerCamelCase__					,return_tensors="pt"       )
         self.assertIn("input_ids"					,lowerCamelCase__       )
         self.assertIn("attention_mask"					,lowerCamelCase__       )
         self.assertNotIn("labels"					,lowerCamelCase__       )
         self.assertNotIn("decoder_attention_mask"					,lowerCamelCase__       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    List[Any]:
     snake_case__      :str         =   [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :Dict         =   tokenizer(text_target=lowerCamelCase__					,max_length=32					,padding="max_length"					,return_tensors="pt"       )
         self.assertEqual(32					,targets["input_ids"].shape[1]       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    Tuple:







     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :Union[str, Any]         =   tokenizer(
             ["I am a small frog" * 1_024, "I am a small frog"]					,padding=lowerCamelCase__					,truncation=lowerCamelCase__					,return_tensors="pt"       )
         self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__       )
         self.assertEqual(batch.input_ids.shape					,(2, 5_122)       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    List[Any]:
     snake_case__      :Tuple         =   ["A long paragraph for summarization."]
     snake_case__      :List[str]         =   [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :List[str]         =   tokenizer(lowerCamelCase__					,return_tensors="pt"       )
         snake_case__      :Tuple         =   tokenizer(text_target=lowerCamelCase__					,return_tensors="pt"       )
         snake_case__      :str         =   inputs["input_ids"]
         snake_case__      :Optional[int]         =   targets["input_ids"]
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )






 @require_torch
 def    lowerCAmelCase_							(		self       )						->    Tuple:







     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__      :List[Any]         =   ["Summary of the text.", "Another summary."]
         snake_case__      :Dict         =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

         snake_case__      :List[str]         =   tokenizer(lowerCamelCase__					,padding=lowerCamelCase__       )
         snake_case__      :List[Any]         =   [[0] * len(lowerCamelCase__       ) for x in encoded_output["input_ids"]]
         snake_case__      :Tuple         =   tokenizer.pad(lowerCamelCase__       )
         self.assertSequenceEqual(outputs["global_attention_mask"]					,lowerCamelCase__       )






 def    lowerCAmelCase_							(		self       )						->    Any:
     pass






 def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:







     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
             snake_case__      :Any         =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__					,**lowerCamelCase__       )
             snake_case__      :Tuple         =   self.tokenizer_class.from_pretrained(lowerCamelCase__					,**lowerCamelCase__       )
             snake_case__      :Dict         =   "A, <mask> AllenNLP sentence."
             snake_case__      :List[str]         =   tokenizer_r.encode_plus(lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,return_token_type_ids=lowerCamelCase__       )
             snake_case__      :List[Any]         =   tokenizer_p.encode_plus(lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,return_token_type_ids=lowerCamelCase__       )
             self.assertEqual(sum(tokens_r["token_type_ids"]       )					,sum(tokens_p["token_type_ids"]       )       )
             self.assertEqual(
                 sum(tokens_r["attention_mask"]       ) / len(tokens_r["attention_mask"]       )					,sum(tokens_p["attention_mask"]       ) / len(tokens_p["attention_mask"]       )					,)

             snake_case__      :int         =   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
             snake_case__      :List[str]         =   tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]       )
             self.assertSequenceEqual(tokens_p["input_ids"]					,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
             self.assertSequenceEqual(tokens_r["input_ids"]					,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

             self.assertSequenceEqual(
                 lowerCamelCase__					,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )
             self.assertSequenceEqual(
                 lowerCamelCase__					,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )







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    lowerCAmelCase_							(		self       )						->    int:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()






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

					snake_case__      :Union[str, Any]         =   "bird"
					snake_case__      :Optional[int]         =   jax.device_count()
					snake_case__      :Tuple         =   pipe.prepare_text_inputs([prompts] * num_samples       )

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

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

					snake_case__      :int         =   replicate(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )
					snake_case__      :Any         =   shard(UpperCamelCase       )

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

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

					snake_case__      :Union[str, Any]         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[Any]         =   jnp.array(
					    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__    ,						snake_case__      :List[str]         =   FlaxControlNetModel.from_pretrained(
					    "lllyasviel/sd-controlnet-openpose"					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__    ,						snake_case__      :Optional[Any]         =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"					,controlnet=UpperCamelCase					,from_pt=UpperCamelCase					,dtype=jnp.bfloataa       )
					snake_case__      :str         =   controlnet_params

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

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

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

					snake_case__      :Dict         =   replicate(UpperCamelCase       )
					snake_case__      :Tuple         =   shard(UpperCamelCase       )
					snake_case__      :Optional[int]         =   shard(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   pipe(
					    prompt_ids=UpperCamelCase					,image=UpperCamelCase					,params=UpperCamelCase					,prng_seed=UpperCamelCase					,num_inference_steps=50					,jit=UpperCamelCase					,).images
					assert images.shape == (jax.device_count(), 1, 768, 512, 3)

					snake_case__      :int         =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
					snake_case__      :List[str]         =   images[0, 253:256, 253:256, -1]

					snake_case__      :Tuple         =   jnp.asarray(jax.device_get(image_slice.flatten()       )       )
					snake_case__      :List[str]         =   jnp.array(
					    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]       )
					print(f'output_slice: {output_slice}'       )
					assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2