


import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


lowerCAmelCase__  :List[Any]             =							get_logger(__name__)


lowerCAmelCase__  :List[str]             =							R'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''

class 		__a				:
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									raise NotImplementedError(
									    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''						)



class 		__a				:
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									raise NotImplementedError(
									    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''						)



class 		__a				(    UpperCAmelCase      ):
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									for processor in self:
																_UpperCAmelCase					=      inspect.signature(processor.__call__						).parameters
																if len(_SCREAMING_SNAKE_CASE						) > 3:
																							if not all(arg in kwargs for arg in list(function_args.keys()						)[2:]						):
																														raise ValueError(
																														    f'''Make sure that all the required parameters: {list(function_args.keys()						)} for '''
																														    f'''{processor.__class__} are passed to the logits processor.'''						)
																							_UpperCAmelCase					=      processor(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
																else:
																							_UpperCAmelCase					=      processor(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or not (temperature > 0):
																raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}'''						)

									_UpperCAmelCase					=      temperature






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      scores / self.temperature
									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -float('Inf'						)      ,   _SCREAMING_SNAKE_CASE = 1						)			->	List[Any]:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or (top_p < 0 or top_p > 1.0):
																raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''						)
									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or (min_tokens_to_keep < 1):
																raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''						)

									_UpperCAmelCase					=      top_p
									_UpperCAmelCase					=      filter_value
									_UpperCAmelCase					=      min_tokens_to_keep






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      lax.top_k(_SCREAMING_SNAKE_CASE      ,   scores.shape[-1]						)

									_UpperCAmelCase					=      jnp.full_like(_SCREAMING_SNAKE_CASE      ,   self.filter_value						)
									_UpperCAmelCase					=      jax.nn.softmax(_SCREAMING_SNAKE_CASE      ,   axis=-1						).cumsum(axis=-1						)
									_UpperCAmelCase					=      cumulative_probs < self.top_p

									# include the token that is higher than top_p as well
									_UpperCAmelCase					=      jnp.roll(_SCREAMING_SNAKE_CASE      ,   1						)
									score_mask |= score_mask.at[:, 0].set(_SCREAMING_SNAKE_CASE						)

									# min tokens to keep
									_UpperCAmelCase					=      score_mask.at[:, : self.min_tokens_to_keep].set(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jax.lax.sort_key_val(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)[-1]

									return next_scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -float('Inf'						)      ,   _SCREAMING_SNAKE_CASE = 1						)			->	int:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or top_k <= 0:
																raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}'''						)

									_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      filter_value






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      scores.shape
									_UpperCAmelCase					=      jnp.full(batch_size * vocab_size      ,   self.filter_value						)

									_UpperCAmelCase					=      min(self.top_k      ,   scores.shape[-1]						)  # Safety check
									_UpperCAmelCase		,	_UpperCAmelCase					=      lax.top_k(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jnp.broadcast_to((jnp.arange(_SCREAMING_SNAKE_CASE						) * vocab_size)[:, None]      ,   (batch_size, topk)						).flatten()
									_UpperCAmelCase					=      topk_scores.flatten()
									_UpperCAmelCase					=      topk_indices.flatten() + shift

									_UpperCAmelCase					=      next_scores_flat.at[topk_indices_flat].set(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      next_scores_flat.reshape(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return next_scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      bos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.full(scores.shape      ,   -float('inf'						)						)

									_UpperCAmelCase					=      1 - jnp.bool_(cur_len - 1						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   new_scores.at[:, self.bos_token_id].set(0						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      max_length
									_UpperCAmelCase					=      eos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.full(scores.shape      ,   -float('inf'						)						)

									_UpperCAmelCase					=      1 - jnp.bool_(cur_len - self.max_length + 1						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   new_scores.at[:, self.eos_token_id].set(0						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or min_length < 0:
																raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}'''						)

									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) or eos_token_id < 0:
																raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''						)

									_UpperCAmelCase					=      min_length
									_UpperCAmelCase					=      eos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      1 - jnp.clip(cur_len - self.min_length      ,   0      ,   1						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   scores.at[:, self.eos_token_id].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      list(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      begin_index






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      1 - jnp.bool_(cur_len - self.begin_index						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   scores.at[:, self.begin_suppress_tokens].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      list(_SCREAMING_SNAKE_CASE						)






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      scores.at[..., self.suppress_tokens].set(-float('inf'						)						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      dict(_SCREAMING_SNAKE_CASE						)
									# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
									# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
									# Indexes without forced tokens will have a negative value.
									_UpperCAmelCase					=      jnp.ones((max(force_token_map.keys()						) + 1)      ,   dtype=jnp.intaa						) * -1
									for index, token in force_token_map.items():
																if token is not None:
																							_UpperCAmelCase					=      force_token_array.at[index].set(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jnp.intaa(_SCREAMING_SNAKE_CASE						)






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									def _force_token(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      scores.shape[0]
																_UpperCAmelCase					=      self.force_token_array[generation_idx]

																_UpperCAmelCase					=      jnp.ones_like(_SCREAMING_SNAKE_CASE      ,   dtype=scores.dtype						) * -float('inf'						)
																_UpperCAmelCase					=      jnp.zeros((batch_size, 1)      ,   dtype=scores.dtype						)
																_UpperCAmelCase					=      lax.dynamic_update_slice(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   (0, current_token)						)
																return new_scores

									_UpperCAmelCase					=      lax.cond(
									    cur_len >= self.force_token_array.shape[0]      ,   lambda: scores      ,   lambda: lax.cond(
									        self.force_token_array[cur_len] >= 0      ,   lambda: _force_token(_SCREAMING_SNAKE_CASE						)      ,   lambda: scores      ,   )      ,   )
									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      generate_config.eos_token_id
									_UpperCAmelCase					=      generate_config.no_timestamps_token_id
									_UpperCAmelCase					=      generate_config.no_timestamps_token_id + 1

									_UpperCAmelCase					=      decoder_input_length + 1

									if generate_config.is_multilingual:
																# room for language token and task token
																self.begin_index += 2
									if hasattr(_SCREAMING_SNAKE_CASE      ,   'max_initial_timestamp_index'						):
																_UpperCAmelCase					=      generate_config.max_initial_timestamp_index
									else:
																_UpperCAmelCase					=      model_config.vocab_size
									if self.max_initial_timestamp_index is None:
																_UpperCAmelCase					=      model_config.vocab_size






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




									"""simple docstring"""





									_UpperCAmelCase					=      scores.at[:, self.no_timestamps_token_id].set(-float('inf'						)						)

									def handle_pairs(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      jnp.where((cur_len - self.begin_index) >= 1      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      jnp.where(
																    input_ids_k[cur_len - 1] >= self.timestamp_begin      ,   True and last_was_timestamp      ,   _SCREAMING_SNAKE_CASE      ,   )

																_UpperCAmelCase					=      jnp.where((cur_len - self.begin_index) < 2      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      jnp.where(
																    input_ids_k[cur_len - 2] >= self.timestamp_begin      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )

																return jnp.where(
																    _SCREAMING_SNAKE_CASE      ,   jnp.where(
																        penultimate_was_timestamp > 0      ,   scores_k.at[self.timestamp_begin :].set(-float('inf'						)						)      ,   scores_k.at[: self.eos_token_id].set(-float('inf'						)						)      ,   )      ,   _SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      jax.vmap(_SCREAMING_SNAKE_CASE						)(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      jnp.where(cur_len == self.begin_index      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jnp.where(
									    self.max_initial_timestamp_index is not None      ,   True and apply_max_initial_timestamp      ,   _SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      self.timestamp_begin + self.max_initial_timestamp_index

									_UpperCAmelCase					=      jnp.where(
									    _SCREAMING_SNAKE_CASE      ,   scores.at[:, last_allowed + 1 :].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE      ,   )

									# if sum of probability over timestamps is above any other token, sample timestamp
									_UpperCAmelCase					=      jax.nn.log_softmax(_SCREAMING_SNAKE_CASE      ,   axis=-1						)

									def handle_cumulative_probs(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      ,   axis=-1						)
																_UpperCAmelCase					=      jnp.max(logprobs_k[: self.timestamp_begin]						)
																return jnp.where(
																    timestamp_logprob > max_text_token_logprob      ,   scores_k.at[: self.timestamp_begin].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE      ,   )

									_UpperCAmelCase					=      jax.vmap(_SCREAMING_SNAKE_CASE						)(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									return scores




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ConditionalDetrImageProcessor

class 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1 / 255      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      min_resolution
									_UpperCAmelCase					=      max_resolution
									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean
									_UpperCAmelCase					=      image_std
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_pad
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[Any]:




									"""simple docstring"""





									if not batched:
																_UpperCAmelCase					=      image_inputs[0]
																if isinstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						):
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.size
																else:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.shape[1], image.shape[2]
																if w < h:
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * h / w						)
																							_UpperCAmelCase					=      self.size['shortest_edge']
																elif w > h:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * w / h						)
																else:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      self.size['shortest_edge']

									else:
																_UpperCAmelCase					=      []
																for image in image_inputs:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      self.get_expected_values([image]						)
																							expected_values.append((expected_height, expected_width)						)
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[0]						)[0]
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[1]						)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Tuple           =     ConditionalDetrImageProcessor if is_vision_available() else None
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      ConditionalDetrImageProcessingTester(self						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_mean'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_std'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_normalize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_resize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'size'						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 18, 'longest_edge': 1333}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.image_processing_class.from_dict(
									    self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 42, 'longest_edge': 84}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE      ,   numpify=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE      ,   torchify=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   torch.Tensor						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,   'r'						) as f:
																_UpperCAmelCase					=      json.loads(f.read()						)

									_UpperCAmelCase					=      {'image_id': 39769, 'annotations': target}

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([0.2796, 0.3138, 0.3481]						)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)

									# verify area
									_UpperCAmelCase					=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([75, 75, 63, 65, 17, 17]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,   'r'						) as f:
																_UpperCAmelCase					=      json.loads(f.read()						)

									_UpperCAmelCase					=      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									_UpperCAmelCase					=      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor(format='coco_panoptic'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   masks_path=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([0.2796, 0.3138, 0.3481]						)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)

									# verify area
									_UpperCAmelCase					=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([17, 17, 63, 75, 75, 93]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify masks
									_UpperCAmelCase					=      822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,   _SCREAMING_SNAKE_CASE						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)
