






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch





class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Tuple       						= {
							    """task_specific_params""": {
							        """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
							        """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
							        """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
							    }
							}
							__magic_name__      :       Union[str, Any]       						= {
							    """task_specific_params.summarization.length_penalty""": 1.0,
							    """task_specific_params.summarization.max_length""": 1_28,
							    """task_specific_params.summarization.min_length""": 12,
							    """task_specific_params.summarization.num_beams""": 4,
							    """task_specific_params.summarization_cnn.length_penalty""": 2.0,
							    """task_specific_params.summarization_cnn.max_length""": 1_42,
							    """task_specific_params.summarization_cnn.min_length""": 56,
							    """task_specific_params.summarization_cnn.num_beams""": 4,
							    """task_specific_params.summarization_xsum.length_penalty""": 1.0,
							    """task_specific_params.summarization_xsum.max_length""": 62,
							    """task_specific_params.summarization_xsum.min_length""": 11,
							    """task_specific_params.summarization_xsum.num_beams""": 6,
							}

							self.assertEqual(flatten_dict(lowerCAmelCase__			)      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       str       						= np.random.randn(3      ,       4			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__			)      ,       x.transpose()			)			)

							__magic_name__      :       int       						= np.random.randn(3      ,       4      ,       5			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			)      ,       x.transpose((1, 2, 0)			)			)			)
	@require_torch
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       List[str]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       Optional[int]       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__			)      ,       transpose(lowerCAmelCase__			).numpy()			)			)

							__magic_name__      :       str       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       Dict       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			)      ,       transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			).numpy()			)			)
	@require_tf
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       Optional[Any]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       Optional[int]       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__			)      ,       transpose(lowerCAmelCase__			).numpy()			)			)

							__magic_name__      :       str       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       Any       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			)      ,       transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			).numpy()			)			)
	@require_flax
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Optional[Any]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       List[Any]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__			)      ,       np.asarray(transpose(lowerCAmelCase__			)			)			)			)

							__magic_name__      :       Dict       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       Union[str, Any]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			)      ,       np.asarray(transpose(lowerCAmelCase__      ,       axes=(1, 2, 0)			)			)			)			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= np.random.randn(3      ,       4			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (4, 3)			)      ,       np.reshape(lowerCAmelCase__      ,       (4, 3)			)			)			)

							__magic_name__      :       Union[str, Any]       						= np.random.randn(3      ,       4      ,       5			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (12, 5)			)      ,       np.reshape(lowerCAmelCase__      ,       (12, 5)			)			)			)
	@require_torch
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Optional[int]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       Tuple       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (4, 3)			)      ,       reshape(lowerCAmelCase__      ,       (4, 3)			).numpy()			)			)

							__magic_name__      :       Any       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       int       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (12, 5)			)      ,       reshape(lowerCAmelCase__      ,       (12, 5)			).numpy()			)			)
	@require_tf
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       Union[str, Any]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       str       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (4, 3)			)      ,       reshape(lowerCAmelCase__      ,       (4, 3)			).numpy()			)			)

							__magic_name__      :       Any       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       str       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (12, 5)			)      ,       reshape(lowerCAmelCase__      ,       (12, 5)			).numpy()			)			)
	@require_flax
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       List[str]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       List[Any]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (4, 3)			)      ,       np.asarray(reshape(lowerCAmelCase__      ,       (4, 3)			)			)			)			)

							__magic_name__      :       Dict       						= np.random.randn(3      ,       4      ,       5			)
							__magic_name__      :       List[str]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(reshape(lowerCAmelCase__      ,       (12, 5)			)      ,       np.asarray(reshape(lowerCAmelCase__      ,       (12, 5)			)			)			)			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Optional[int]       						= np.random.randn(1      ,       3      ,       4			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__			)      ,       np.squeeze(lowerCAmelCase__			)			)			)

							__magic_name__      :       List[Any]       						= np.random.randn(1      ,       4      ,       1      ,       5			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__      ,       axis=2			)      ,       np.squeeze(lowerCAmelCase__      ,       axis=2			)			)			)
	@require_torch
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Any       						= np.random.randn(1      ,       3      ,       4			)
							__magic_name__      :       Tuple       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__			)      ,       squeeze(lowerCAmelCase__			).numpy()			)			)

							__magic_name__      :       Any       						= np.random.randn(1      ,       4      ,       1      ,       5			)
							__magic_name__      :       Optional[int]       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__      ,       axis=2			)      ,       squeeze(lowerCAmelCase__      ,       axis=2			).numpy()			)			)
	@require_tf
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Optional[int]       						= np.random.randn(1      ,       3      ,       4			)
							__magic_name__      :       List[str]       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__			)      ,       squeeze(lowerCAmelCase__			).numpy()			)			)

							__magic_name__      :       Dict       						= np.random.randn(1      ,       4      ,       1      ,       5			)
							__magic_name__      :       List[Any]       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__      ,       axis=2			)      ,       squeeze(lowerCAmelCase__      ,       axis=2			).numpy()			)			)
	@require_flax
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Optional[int]       						= np.random.randn(1      ,       3      ,       4			)
							__magic_name__      :       List[Any]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__			)      ,       np.asarray(squeeze(lowerCAmelCase__			)			)			)			)

							__magic_name__      :       List[str]       						= np.random.randn(1      ,       4      ,       1      ,       5			)
							__magic_name__      :       Optional[int]       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(squeeze(lowerCAmelCase__      ,       axis=2			)      ,       np.asarray(squeeze(lowerCAmelCase__      ,       axis=2			)			)			)			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       int       						= np.random.randn(3      ,       4			)
							self.assertTrue(np.allclose(expand_dims(lowerCAmelCase__      ,       axis=1			)      ,       np.expand_dims(lowerCAmelCase__      ,       axis=1			)			)			)
	@require_torch
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       List[Any]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       Optional[Any]       						= torch.tensor(lowerCAmelCase__			)
							self.assertTrue(np.allclose(expand_dims(lowerCAmelCase__      ,       axis=1			)      ,       expand_dims(lowerCAmelCase__      ,       axis=1			).numpy()			)			)
	@require_tf
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       List[Any]       						= np.random.randn(3      ,       4			)
							__magic_name__      :       List[Any]       						= tf.constant(lowerCAmelCase__			)
							self.assertTrue(np.allclose(expand_dims(lowerCAmelCase__      ,       axis=1			)      ,       expand_dims(lowerCAmelCase__      ,       axis=1			).numpy()			)			)







	@require_flax
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Tuple       						= np.random.randn(3      ,       4			)
							__magic_name__      :       Any       						= jnp.array(lowerCAmelCase__			)
							self.assertTrue(np.allclose(expand_dims(lowerCAmelCase__      ,       axis=1			)      ,       np.asarray(expand_dims(lowerCAmelCase__      ,       axis=1			)			)			)			)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
