
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ["""image_processor""", """tokenizer"""]
			__UpperCamelCase      :						List[str]					      = """OwlViTImageProcessor"""
			__UpperCamelCase      :						Any					      = ("""CLIPTokenizer""", """CLIPTokenizerFast""")

			def __init__(       self :       Optional[Any]						,	snake_case_ :       Dict=None						,	snake_case_ :       Any=None						,	**snake_case_ :       Dict      ):
							UpperCamelCase_:							List[Any]	=						None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""						,	snake_case_						,	)
											UpperCamelCase_:							str	=						kwargs.pop("""feature_extractor"""      )

							UpperCamelCase_:							str	=						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""      )
							if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""      )

							super().__init__(snake_case_						,	snake_case_      )

			def __call__(       self :       Optional[int]						,	snake_case_ :       int=None						,	snake_case_ :       Tuple=None						,	snake_case_ :       Any=None						,	snake_case_ :       Tuple="max_length"						,	snake_case_ :       List[str]="np"						,	**snake_case_ :       Optional[Any]      ):

							if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""      )

							if text is not None:
											if isinstance(snake_case_						,	snake_case_      ) or (isinstance(snake_case_						,	snake_case_      ) and not isinstance(text[0]						,	snake_case_      )):
															UpperCamelCase_:							Any	=						[self.tokenizer(snake_case_						,	padding=snake_case_						,	return_tensors=snake_case_						,	**snake_case_      )]

											elif isinstance(snake_case_						,	snake_case_      ) and isinstance(text[0]						,	snake_case_      ):
															UpperCamelCase_:							Union[str, Any]	=						[]

															# Maximum number of queries across batch
															UpperCamelCase_:							Optional[Any]	=						max([len(snake_case_      ) for t in text]      )

															# Pad all batch samples to max number of text queries
															for t in text:
																			if len(snake_case_      ) != max_num_queries:
																							UpperCamelCase_:							Union[str, Any]	=						t + [""" """] * (max_num_queries - len(snake_case_      ))

																			UpperCamelCase_:							Tuple	=						self.tokenizer(snake_case_						,	padding=snake_case_						,	return_tensors=snake_case_						,	**snake_case_      )
																			encodings.append(snake_case_      )
											else:
															raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""      )

											if return_tensors == "np":
															UpperCamelCase_:							List[str]	=						np.concatenate([encoding["""input_ids"""] for encoding in encodings]						,	axis=0      )
															UpperCamelCase_:							Union[str, Any]	=						np.concatenate([encoding["""attention_mask"""] for encoding in encodings]						,	axis=0      )

											elif return_tensors == "jax" and is_flax_available():
															import jax.numpy as jnp

															UpperCamelCase_:							Optional[Any]	=						jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]						,	axis=0      )
															UpperCamelCase_:							Any	=						jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]						,	axis=0      )

											elif return_tensors == "pt" and is_torch_available():
															import torch

															UpperCamelCase_:							Any	=						torch.cat([encoding["""input_ids"""] for encoding in encodings]						,	dim=0      )
															UpperCamelCase_:							int	=						torch.cat([encoding["""attention_mask"""] for encoding in encodings]						,	dim=0      )

											elif return_tensors == "tf" and is_tf_available():
															import tensorflow as tf

															UpperCamelCase_:							Optional[int]	=						tf.stack([encoding["""input_ids"""] for encoding in encodings]						,	axis=0      )
															UpperCamelCase_:							Any	=						tf.stack([encoding["""attention_mask"""] for encoding in encodings]						,	axis=0      )

											else:
															raise ValueError("""Target return tensor type could not be returned"""      )

											UpperCamelCase_:							Any	=						BatchEncoding()
											UpperCamelCase_:							str	=						input_ids
											UpperCamelCase_:							Union[str, Any]	=						attention_mask

							if query_images is not None:
											UpperCamelCase_:							Tuple	=						BatchEncoding()
											UpperCamelCase_:							Dict	=						self.image_processor(
											    snake_case_						,	return_tensors=snake_case_						,	**snake_case_      ).pixel_values
											UpperCamelCase_:							Optional[int]	=						query_pixel_values

							if images is not None:
											UpperCamelCase_:							List[Any]	=						self.image_processor(snake_case_						,	return_tensors=snake_case_						,	**snake_case_      )

							if text is not None and images is not None:
											UpperCamelCase_:							Optional[Any]	=						image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											UpperCamelCase_:							Dict	=						image_features.pixel_values
											return encoding
							elif text is not None or query_images is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_      )						,	tensor_type=snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	*snake_case_ :       Any						,	**snake_case_ :       Any      ):
							return self.image_processor.post_process(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	*snake_case_ :       List[Any]						,	**snake_case_ :       Tuple      ):
							return self.image_processor.post_process_object_detection(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple						,	*snake_case_ :       Optional[int]						,	**snake_case_ :       Optional[int]      ):
							return self.image_processor.post_process_image_guided_detection(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[str]						,	*snake_case_ :       List[str]						,	**snake_case_ :       Union[str, Any]      ):
							return self.tokenizer.batch_decode(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	*snake_case_ :       str						,	**snake_case_ :       int      ):
							return self.tokenizer.decode(*snake_case_						,	**snake_case_      )

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,	snake_case_						,	)
							return self.image_processor_class




			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,	snake_case_						,	)
							return self.image_processor








import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_						: Union[str, Any]         =  logging.getLogger()

lowerCamelCase_						: List[str]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):
							os.makedirs(snake_case_						,	exist_ok=snake_case_      )
							UpperCamelCase_:							int	=						{"""source""": """What is love ?""", """target""": """life"""}
							UpperCamelCase_:							Tuple	=						{"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
															UpperCamelCase_:							Tuple	=						"""\n""".join([contents[field]] * n_lines[split]      )
															with open(os.path.join(snake_case_						,	f'''{split}.{field}'''      )						,	"""w"""      ) as f:
																			f.write(snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str = "pytorch"      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						os.path.join(snake_case_						,	"""output"""      )
							UpperCamelCase_:							Any	=						os.path.join(snake_case_						,	"""data"""      )
							self._create_dummy_data(data_dir=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

							if gpus > 0:
											testargs.append(f'''--gpus={gpus}'''      )
											if is_apex_available():
															testargs.append("""--fp16"""      )
							else:
											testargs.append("""--gpus=0"""      )
											testargs.append("""--distributed_backend=ddp_cpu"""      )
											testargs.append("""--num_processes=2"""      )

							UpperCamelCase_:							Optional[Any]	=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
							execute_subprocess_async(snake_case_						,	env=self.get_env()      )

							UpperCamelCase_:							Optional[int]	=						os.path.join(snake_case_						,	"""metrics.json"""      )
							with open(snake_case_      ) as f:
											UpperCamelCase_:							Any	=						json.load(snake_case_      )
							return result

			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self._run_finetune(gpus=1      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						self._run_finetune(gpus=2      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )




			@require_torch_multi_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )






