

"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						A_				:      Union[str, Any]           =			"platform"

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)

def 						A							(  snake_case__		,					snake_case__		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					):




			'''simple docstring'''


			if attention_mask is None:
						SCREAMING_SNAKE_CASE__										=   np.where(input_ids != config.pad_token_id		,					1		,					0     )
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE__										=   np.where(decoder_input_ids != config.pad_token_id		,					1		,					0     )
			if head_mask is None:
						SCREAMING_SNAKE_CASE__										=   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						SCREAMING_SNAKE_CASE__										=   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						SCREAMING_SNAKE_CASE__										=   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}





class  lowerCamelCase    :

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]=1_3     ,					__UpperCAmelCase					:    Union[str, Any]=7     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Optional[int]=False     ,					__UpperCAmelCase					:    Union[str, Any]=9_9     ,					__UpperCAmelCase					:    List[str]=1_6     ,					__UpperCAmelCase					:    Any=2     ,					__UpperCAmelCase					:    List[str]=4     ,					__UpperCAmelCase					:    Dict=4     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    Tuple=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=3_2     ,					__UpperCAmelCase					:    List[str]=2     ,					__UpperCAmelCase					:    List[Any]=1     ,					__UpperCAmelCase					:    Union[str, Any]=0     ,					__UpperCAmelCase					:    Optional[Any]=0.02     ,					) ->     int:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   seq_length
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   eos_token_id
						SCREAMING_SNAKE_CASE__										=   pad_token_id
						SCREAMING_SNAKE_CASE__										=   bos_token_id
						SCREAMING_SNAKE_CASE__										=   initializer_range

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,					self.vocab_size							)     ,					3     ,					self.vocab_size							)
						SCREAMING_SNAKE_CASE__										=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,					dtype=np.intaa							))     ,					-1							)

						SCREAMING_SNAKE_CASE__										=   shift_tokens_right(__UpperCAmelCase     ,					1     ,					2							)

						SCREAMING_SNAKE_CASE__										=   BlenderbotSmallConfig(
						    vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					encoder_layers=self.num_hidden_layers     ,					decoder_layers=self.num_hidden_layers     ,					encoder_attention_heads=self.num_attention_heads     ,					decoder_attention_heads=self.num_attention_heads     ,					encoder_ffn_dim=self.intermediate_size     ,					decoder_ffn_dim=self.intermediate_size     ,					dropout=self.hidden_dropout_prob     ,					attention_dropout=self.attention_probs_dropout_prob     ,					max_position_embeddings=self.max_position_embeddings     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.bos_token_id     ,					pad_token_id=self.pad_token_id     ,					initializer_range=self.initializer_range     ,					use_cache=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   prepare_blenderbot_inputs_dict(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)
						return config, inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[str]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						return config, inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   2_0
						SCREAMING_SNAKE_CASE__										=   model_class_name(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   model.encode(inputs_dict["""input_ids"""]							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   (
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

						SCREAMING_SNAKE_CASE__										=   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,					dtype="""i4"""							)

						SCREAMING_SNAKE_CASE__										=   jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)
						SCREAMING_SNAKE_CASE__										=   model.decode(
						    decoder_input_ids[:, :-1]     ,					__UpperCAmelCase     ,					decoder_attention_mask=__UpperCAmelCase     ,					past_key_values=__UpperCAmelCase     ,					decoder_position_ids=__UpperCAmelCase     ,					)

						SCREAMING_SNAKE_CASE__										=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""							)
						SCREAMING_SNAKE_CASE__										=   model.decode(
						    decoder_input_ids[:, -1:]     ,					__UpperCAmelCase     ,					decoder_attention_mask=__UpperCAmelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_position_ids=__UpperCAmelCase     ,					)

						SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
						self.parent.assertTrue(diff < 1e-3     ,					msg=F"""Max diff is {diff}"""							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   2_0
						SCREAMING_SNAKE_CASE__										=   model_class_name(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   model.encode(inputs_dict["""input_ids"""]							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   (
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

						SCREAMING_SNAKE_CASE__										=   jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
						    ]     ,					axis=-1     ,					)

						SCREAMING_SNAKE_CASE__										=   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)

						SCREAMING_SNAKE_CASE__										=   model.decode(
						    decoder_input_ids[:, :-1]     ,					__UpperCAmelCase     ,					decoder_attention_mask=__UpperCAmelCase     ,					past_key_values=__UpperCAmelCase     ,					decoder_position_ids=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,					dtype="""i4"""							)
						SCREAMING_SNAKE_CASE__										=   model.decode(
						    decoder_input_ids[:, -1:]     ,					__UpperCAmelCase     ,					past_key_values=outputs_cache.past_key_values     ,					decoder_attention_mask=__UpperCAmelCase     ,					decoder_position_ids=__UpperCAmelCase     ,					)

						SCREAMING_SNAKE_CASE__										=   model.decode(__UpperCAmelCase     ,					__UpperCAmelCase     ,					decoder_attention_mask=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
						self.parent.assertTrue(diff < 1e-3     ,					msg=F"""Max diff is {diff}"""							)





@require_flax
class  lowerCamelCase    (unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				9_9

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   np.array(
						    [
						        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
						        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
						        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
						        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
						        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
						        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
						        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
						        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
						        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
						        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
						        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
						        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
						        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
						    ]     ,					dtype=np.intaa     ,					)

						SCREAMING_SNAKE_CASE__										=   input_ids.shape[0]
						SCREAMING_SNAKE_CASE__										=   BlenderbotSmallConfig(
						    vocab_size=self.vocab_size     ,					d_model=2_4     ,					encoder_layers=2     ,					decoder_layers=2     ,					encoder_attention_heads=2     ,					decoder_attention_heads=2     ,					encoder_ffn_dim=3_2     ,					decoder_ffn_dim=3_2     ,					max_position_embeddings=4_8     ,					eos_token_id=2     ,					pad_token_id=1     ,					bos_token_id=0     ,					)
						return config, input_ids, batch_size

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Any:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self._get_config_and_data()
						SCREAMING_SNAKE_CASE__										=   FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   lm_model(input_ids=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   BlenderbotSmallConfig(
						    vocab_size=self.vocab_size     ,					d_model=1_4     ,					encoder_layers=2     ,					decoder_layers=2     ,					encoder_attention_heads=2     ,					decoder_attention_heads=2     ,					encoder_ffn_dim=8     ,					decoder_ffn_dim=8     ,					max_position_embeddings=4_8     ,					)
						SCREAMING_SNAKE_CASE__										=   FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]     ,					dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__										=   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]     ,					dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__										=   lm_model(input_ids=__UpperCAmelCase     ,					decoder_input_ids=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape     ,					__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     int:
						SCREAMING_SNAKE_CASE__										=   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]     ,					dtype=np.intaa							)
						SCREAMING_SNAKE_CASE__										=   shift_tokens_right(__UpperCAmelCase     ,					1     ,					2							)
						SCREAMING_SNAKE_CASE__										=   np.equal(__UpperCAmelCase     ,					1							).astype(np.floataa							).sum()
						SCREAMING_SNAKE_CASE__										=   np.equal(__UpperCAmelCase     ,					1							).astype(np.floataa							).sum()
						self.assertEqual(shifted.shape     ,					input_ids.shape							)
						self.assertEqual(__UpperCAmelCase     ,					n_pad_before - 1							)
						self.assertTrue(np.equal(shifted[:, 0]     ,					2							).all()							)





@require_flax
class  lowerCamelCase    (A__      ,unittest.TestCase      ,A__      ):
			lowerCamelCase__ :		Any       		=				True
			lowerCamelCase__ :		Any       		=				(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__ :		Optional[int]       		=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   FlaxBlenderbotSmallModelTester(self							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Tuple:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												SCREAMING_SNAKE_CASE__										=   self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

												@jax.jit
												def encode_jitted(__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Tuple=None     ,					**__UpperCAmelCase					:    str							):
															return model.encode(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   encode_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   encode_jitted(**__UpperCAmelCase							).to_tuple()

												self.assertEqual(len(__UpperCAmelCase							)     ,					len(__UpperCAmelCase							)							)
												for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase							):
															self.assertEqual(jitted_output.shape     ,					output.shape							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model.encode(inputs_dict["""input_ids"""]     ,					inputs_dict["""attention_mask"""]							)

												SCREAMING_SNAKE_CASE__										=   {
												    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
												    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
												    """encoder_outputs""": encoder_outputs,
												}

												@jax.jit
												def decode_jitted(__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]							):
															return model.decode(
															    decoder_input_ids=__UpperCAmelCase     ,					decoder_attention_mask=__UpperCAmelCase     ,					encoder_outputs=__UpperCAmelCase     ,					)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   decode_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   decode_jitted(**__UpperCAmelCase							).to_tuple()

												self.assertEqual(len(__UpperCAmelCase							)     ,					len(__UpperCAmelCase							)							)
												for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase							):
															self.assertEqual(jitted_output.shape     ,					output.shape							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Union[str, Any]:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE__										=   np.ones((1, 1)							) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
									self.assertIsNotNone(__UpperCAmelCase							)





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Any=7     ,					__UpperCAmelCase					:    Union[str, Any]=3     ,					__UpperCAmelCase					:    Any=1_8     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    List[Any]=4_0_0     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    int=None     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Any=True     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   size if size is not None else {"""shortest_edge""": 2_0}
						SCREAMING_SNAKE_CASE__										=   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   min_resolution
						SCREAMING_SNAKE_CASE__										=   max_resolution
						SCREAMING_SNAKE_CASE__										=   do_resize
						SCREAMING_SNAKE_CASE__										=   size
						SCREAMING_SNAKE_CASE__										=   do_center_crop
						SCREAMING_SNAKE_CASE__										=   crop_size
						SCREAMING_SNAKE_CASE__										=   do_flip_channel_order





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}





@require_torch
@require_vision
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				MobileViTImageProcessor if is_vision_available() else None

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   MobileViTImageProcessingTester(self							)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_resize"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""size"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_flip_channel_order"""							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 2_0}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 1_8, """width""": 1_8}							)

						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2     ,					crop_size=8_4							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 4_2}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 8_4, """width""": 8_4}							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					Image.Image							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					numpify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					np.ndarray							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					torchify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					torch.Tensor							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)


