
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase												=	logging.getLogger(__name__)


@dataclass
class       _lowerCAmelCase   :







							'''simple docstring'''

							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						42


@dataclass
class       _lowerCAmelCase   :







							'''simple docstring'''

							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						None
							lowerCAmelCase_ 						=						None


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"train"
							lowerCAmelCase_ 						=						"dev"
							lowerCAmelCase_ 						=						"test"


class       _lowerCAmelCase   :







							'''simple docstring'''

							@staticmethod
							def 	lowercase							(UpperCAmelCase  ,  UpperCAmelCase  )					->       List[InputExample]:
														raise NotImplementedError

							@staticmethod
							def 	lowercase							(UpperCAmelCase  )					->       List[str]:
														raise NotImplementedError





							@staticmethod
							def 	lowercase							(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase=1  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=0  ,  UpperCAmelCase=0  ,  UpperCAmelCase=-100  ,  UpperCAmelCase=0  ,  UpperCAmelCase=True  ,  )					->       List[InputFeatures]:
														_snake_case       =  {label: i for i, label in enumerate(UpperCAmelCase  )}

														_snake_case       =  []
														for ex_index, example in enumerate(UpperCAmelCase  ):
																					if ex_index % 10000 == 0:
																												logger.info("""Writing example %d of %d"""  ,  UpperCAmelCase  ,  len(UpperCAmelCase  )  )

																					_snake_case       =  []
																					_snake_case       =  []
																					for word, label in zip(example.words  ,  example.labels  ):
																												_snake_case       =  tokenizer.tokenize(UpperCAmelCase  )

																												# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																												if len(UpperCAmelCase  ) > 0:
																																			tokens.extend(UpperCAmelCase  )
																																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCAmelCase  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																					_snake_case       =  tokenizer.num_special_tokens_to_add()
																					if len(UpperCAmelCase  ) > max_seq_length - special_tokens_count:
																												_snake_case       =  tokens[: (max_seq_length - special_tokens_count)]
																												_snake_case       =  label_ids[: (max_seq_length - special_tokens_count)]

																					# The convention in BERT is:
																					# (a) For sequence pairs:
																					#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																					#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																					# (b) For single sequences:
																					#  tokens:   [CLS] the dog is hairy . [SEP]
																					#  type_ids:   0   0   0   0  0     0   0
																					#
																					# Where "type_ids" are used to indicate whether this is the first
																					# sequence or the second sequence. The embedding vectors for `type=0` and
																					# `type=1` were learned during pre-training and are added to the wordpiece
																					# embedding vector (and position vector). This is not *strictly* necessary
																					# since the [SEP] token unambiguously separates the sequences, but it makes
																					# it easier for the model to learn the concept of sequences.
																					#
																					# For classification tasks, the first vector (corresponding to [CLS]) is
																					# used as the "sentence vector". Note that this only makes sense because
																					# the entire model is fine-tuned.
																					tokens += [sep_token]
																					label_ids += [pad_token_label_id]
																					if sep_token_extra:
																												# roberta uses an extra separator b/w pairs of sentences
																												tokens += [sep_token]
																												label_ids += [pad_token_label_id]
																					_snake_case       =  [sequence_a_segment_id] * len(UpperCAmelCase  )

																					if cls_token_at_end:
																												tokens += [cls_token]
																												label_ids += [pad_token_label_id]
																												segment_ids += [cls_token_segment_id]
																					else:
																												_snake_case       =  [cls_token] + tokens
																												_snake_case       =  [pad_token_label_id] + label_ids
																												_snake_case       =  [cls_token_segment_id] + segment_ids

																					_snake_case       =  tokenizer.convert_tokens_to_ids(UpperCAmelCase  )

																					# The mask has 1 for real tokens and 0 for padding tokens. Only real
																					# tokens are attended to.
																					_snake_case       =  [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase  )

																					# Zero-pad up to the sequence length.
																					_snake_case       =  max_seq_length - len(UpperCAmelCase  )
																					if pad_on_left:
																												_snake_case       =  ([pad_token] * padding_length) + input_ids
																												_snake_case       =  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																												_snake_case       =  ([pad_token_segment_id] * padding_length) + segment_ids
																												_snake_case       =  ([pad_token_label_id] * padding_length) + label_ids
																					else:
																												input_ids += [pad_token] * padding_length
																												input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																												segment_ids += [pad_token_segment_id] * padding_length
																												label_ids += [pad_token_label_id] * padding_length

																					assert len(UpperCAmelCase  ) == max_seq_length
																					assert len(UpperCAmelCase  ) == max_seq_length
																					assert len(UpperCAmelCase  ) == max_seq_length
																					assert len(UpperCAmelCase  ) == max_seq_length

																					if ex_index < 5:
																												logger.info("""*** Example ***"""  )
																												logger.info("""guid: %s"""  ,  example.guid  )
																												logger.info("""tokens: %s"""  ,  """ """.join([str(UpperCAmelCase  ) for x in tokens]  )  )
																												logger.info("""input_ids: %s"""  ,  """ """.join([str(UpperCAmelCase  ) for x in input_ids]  )  )
																												logger.info("""input_mask: %s"""  ,  """ """.join([str(UpperCAmelCase  ) for x in input_mask]  )  )
																												logger.info("""segment_ids: %s"""  ,  """ """.join([str(UpperCAmelCase  ) for x in segment_ids]  )  )
																												logger.info("""label_ids: %s"""  ,  """ """.join([str(UpperCAmelCase  ) for x in label_ids]  )  )

																					if "token_type_ids" not in tokenizer.model_input_names:
																												_snake_case       =  None

																					features.append(
																					    InputFeatures(
																					        input_ids=UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  label_ids=UpperCAmelCase  )  )
														return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset


				class       _lowerCAmelCase   (   __snake_case				):







											'''simple docstring'''

											lowerCAmelCase_ 						=						42
											lowerCAmelCase_ 						=						nn.CrossEntropyLoss().ignore_index

											def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase=False  ,  UpperCAmelCase = Split.train  ,  )					->       Optional[Any]:
																		# Load data features from cache or dataset file
																		_snake_case       =  os.path.join(
																		    UpperCAmelCase  ,  """cached_{}_{}_{}""".format(mode.value  ,  tokenizer.__class__.__name__  ,  str(UpperCAmelCase  )  )  ,  )

																		# Make sure only the first process in distributed training processes the dataset,
																		# and the others will use the cache.
																		_snake_case       =  cached_features_file + """.lock"""
																		with FileLock(UpperCAmelCase  ):
																									if os.path.exists(UpperCAmelCase  ) and not overwrite_cache:
																																logger.info(f"""Loading features from cached file {cached_features_file}"""  )
																																_snake_case       =  torch.load(UpperCAmelCase  )
																									else:
																																logger.info(f"""Creating features from dataset file at {data_dir}"""  )
																																_snake_case       =  token_classification_task.read_examples_from_file(UpperCAmelCase  ,  UpperCAmelCase  )
																																# TODO clean up all this to leverage built-in features of tokenizers
																																_snake_case       =  token_classification_task.convert_examples_to_features(
																																    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  cls_token_at_end=bool(model_type in ["""xlnet"""]  )  ,  cls_token=tokenizer.cls_token  ,  cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0  ,  sep_token=tokenizer.sep_token  ,  sep_token_extra=UpperCAmelCase  ,  pad_on_left=bool(tokenizer.padding_side == """left"""  )  ,  pad_token=tokenizer.pad_token_id  ,  pad_token_segment_id=tokenizer.pad_token_type_id  ,  pad_token_label_id=self.pad_token_label_id  ,  )
																																logger.info(f"""Saving features into cached file {cached_features_file}"""  )
																																torch.save(self.features  ,  UpperCAmelCase  )

											def __len__(self  )					->       Optional[Any]:
																		return len(self.features  )





											def __getitem__(self  ,  UpperCAmelCase  )					->       InputFeatures:
																		return self.features[i]


if is_tf_available():
				import tensorflow as tf


				class       _lowerCAmelCase   :







											'''simple docstring'''

											lowerCAmelCase_ 						=						42
											lowerCAmelCase_ 						=						-1_00

											def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase=False  ,  UpperCAmelCase = Split.train  ,  )					->       Dict:
																		_snake_case       =  token_classification_task.read_examples_from_file(UpperCAmelCase  ,  UpperCAmelCase  )
																		# TODO clean up all this to leverage built-in features of tokenizers
																		_snake_case       =  token_classification_task.convert_examples_to_features(
																		    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  cls_token_at_end=bool(model_type in ["""xlnet"""]  )  ,  cls_token=tokenizer.cls_token  ,  cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0  ,  sep_token=tokenizer.sep_token  ,  sep_token_extra=UpperCAmelCase  ,  pad_on_left=bool(tokenizer.padding_side == """left"""  )  ,  pad_token=tokenizer.pad_token_id  ,  pad_token_segment_id=tokenizer.pad_token_type_id  ,  pad_token_label_id=self.pad_token_label_id  ,  )

																		def gen():
																									for ex in self.features:
																																if ex.token_type_ids is None:
																																							yield (
																																							    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																							    ex.label_ids,
																																							)
																																else:
																																							yield (
																																							    {
																																							        "input_ids": ex.input_ids,
																																							        "attention_mask": ex.attention_mask,
																																							        "token_type_ids": ex.token_type_ids,
																																							    },
																																							    ex.label_ids,
																																							)

																		if "token_type_ids" not in tokenizer.model_input_names:
																									_snake_case       =  tf.data.Dataset.from_generator(
																									    UpperCAmelCase  ,  ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)  ,  (
																									        {"""input_ids""": tf.TensorShape([None]  ), """attention_mask""": tf.TensorShape([None]  )},
																									        tf.TensorShape([None]  ),
																									    )  ,  )
																		else:
																									_snake_case       =  tf.data.Dataset.from_generator(
																									    UpperCAmelCase  ,  ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)  ,  (
																									        {
																									            """input_ids""": tf.TensorShape([None]  ),
																									            """attention_mask""": tf.TensorShape([None]  ),
																									            """token_type_ids""": tf.TensorShape([None]  ),
																									        },
																									        tf.TensorShape([None]  ),
																									    )  ,  )

											def 	lowercase							(self  )					->       Dict:
																		_snake_case       =  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

																		return self.dataset

											def __len__(self  )					->       List[str]:
																		return len(self.features  )





											def __getitem__(self  ,  UpperCAmelCase  )					->       InputFeatures:
																		return self.features[i]

'''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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

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

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2