


"""simple docstring"""


import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import CvtForImageClassification, CvtModel
	from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class   _UpperCamelCase  (   __lowercase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										self.parent.assertTrue(hasattr(UpperCAmelCase__    ,   "embed_dim"	)	)
										self.parent.assertTrue(hasattr(UpperCAmelCase__    ,   "num_heads"	)	)
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=13    ,   __a=64    ,   __a=3    ,   __a=[16, 48, 96]    ,   __a=[1, 3, 6]    ,   __a=[1, 2, 10]    ,   __a=[7, 3, 3]    ,   __a=[4, 2, 2]    ,   __a=[2, 1, 1]    ,   __a=[2, 2, 2]    ,   __a=[False, False, True]    ,   __a=[0.0, 0.0, 0.0]    ,   __a=0.0_2    ,   __a=1e-1_2    ,   __a=True    ,   __a=True    ,   __a=2    ,   ):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			batch_size
										__lowerCAmelCase				    =			image_size
										__lowerCAmelCase				    =			patch_sizes
										__lowerCAmelCase				    =			patch_stride
										__lowerCAmelCase				    =			patch_padding
										__lowerCAmelCase				    =			is_training
										__lowerCAmelCase				    =			use_labels
										__lowerCAmelCase				    =			num_labels
										__lowerCAmelCase				    =			num_channels
										__lowerCAmelCase				    =			embed_dim
										__lowerCAmelCase				    =			num_heads
										__lowerCAmelCase				    =			stride_kv
										__lowerCAmelCase				    =			depth
										__lowerCAmelCase				    =			cls_token
										__lowerCAmelCase				    =			attention_drop_rate
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			layer_norm_eps

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										__lowerCAmelCase				    =			None
										if self.use_labels:
													__lowerCAmelCase				    =			ids_tensor([self.batch_size]    ,   self.num_labels	)

										__lowerCAmelCase				    =			self.get_config()
										return config, pixel_values, labels

							def 			snake_case				(     self	):
										return CvtConfig(
										    image_size=self.image_size    ,   num_labels=self.num_labels    ,   num_channels=self.num_channels    ,   embed_dim=self.embed_dim    ,   num_heads=self.num_heads    ,   patch_sizes=self.patch_sizes    ,   patch_padding=self.patch_padding    ,   patch_stride=self.patch_stride    ,   stride_kv=self.stride_kv    ,   depth=self.depth    ,   cls_token=self.cls_token    ,   attention_drop_rate=self.attention_drop_rate    ,   initializer_range=self.initializer_range    ,   )

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			CvtModel(config=UpperCAmelCase__	)
										model.to(UpperCAmelCase__	)
										model.eval()
										__lowerCAmelCase				    =			model(UpperCAmelCase__	)
										__lowerCAmelCase				    =			(self.image_size, self.image_size)
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			image_size[0], image_size[1]
										for i in range(len(self.depth	)	):
													__lowerCAmelCase				    =			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
													__lowerCAmelCase				    =			floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
										self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.embed_dim[-1], height, width)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			CvtForImageClassification(UpperCAmelCase__	)
										model.to(UpperCAmelCase__	)
										model.eval()
										__lowerCAmelCase				    =			model(UpperCAmelCase__    ,   labels=UpperCAmelCase__	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.prepare_config_and_inputs()
										__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			config_and_inputs
										__lowerCAmelCase				    =			{"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class   _UpperCamelCase  (   __lowercase       ,__lowercase       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase						:    str				      =(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase						:    Optional[Any]				      =False
							__UpperCAmelCase						:    Dict				      =False
							__UpperCAmelCase						:    List[str]				      =False
							__UpperCAmelCase						:    Union[str, Any]				      =False
							__UpperCAmelCase						:    Tuple				      =False

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			CvtModelTester(self	)
										__lowerCAmelCase				    =			ConfigTester(self    ,   config_class=UpperCAmelCase__    ,   has_text_modality=UpperCAmelCase__    ,   hidden_size=37	)

							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()

							def 			snake_case				(     self	):
										return

							@unittest.skip(reason="Cvt does not output attentions"	)
							def 			snake_case				(     self	):
										pass

							@unittest.skip(reason="Cvt does not use inputs_embeds"	)
							def 			snake_case				(     self	):
										pass

							@unittest.skip(reason="Cvt does not support input and output embeddings"	)
							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self	):
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase				    =			model_class(UpperCAmelCase__	)
													__lowerCAmelCase				    =			inspect.signature(model.forward	)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__lowerCAmelCase				    =			[*signature.parameters.keys()]

													__lowerCAmelCase				    =			["pixel_values"]
													self.assertListEqual(arg_names[:1]    ,   UpperCAmelCase__	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__	)

							def 			snake_case				(     self	):
										def check_hidden_states_output(__a    ,   __a    ,   __a	):
													__lowerCAmelCase				    =			model_class(UpperCAmelCase__	)
													model.to(UpperCAmelCase__	)
													model.eval()

													with torch.no_grad():
																__lowerCAmelCase				    =			model(**self._prepare_for_class(UpperCAmelCase__    ,   UpperCAmelCase__	)	)

													__lowerCAmelCase				    =			outputs.hidden_states

													__lowerCAmelCase				    =			len(self.model_tester.depth	)
													self.assertEqual(len(UpperCAmelCase__	)    ,   UpperCAmelCase__	)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]	)    ,   [
													        self.model_tester.embed_dim[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]    ,   )

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase				    =			True
													check_hidden_states_output(UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__	)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__lowerCAmelCase				    =			True

													check_hidden_states_output(UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
							def 			snake_case				(     self	):
										pass



							@slow
							def 			snake_case				(     self	):
										for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase				    =			CvtModel.from_pretrained(UpperCAmelCase__	)
													self.assertIsNotNone(UpperCAmelCase__	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
			return image


@require_torch
@require_vision
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							@cached_property
							def 			snake_case				(     self	):
										return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)



							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(UpperCAmelCase__	)

										__lowerCAmelCase				    =			self.default_image_processor
										__lowerCAmelCase				    =			prepare_img()
										__lowerCAmelCase				    =			image_processor(images=UpperCAmelCase__    ,   return_tensors="pt"	).to(UpperCAmelCase__	)

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase				    =			model(**UpperCAmelCase__	)

										# verify the logits
										__lowerCAmelCase				    =			torch.Size((1, 10_00)	)
										self.assertEqual(outputs.logits.shape    ,   UpperCAmelCase__	)

										__lowerCAmelCase				    =			torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]	).to(UpperCAmelCase__	)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,   UpperCAmelCase__    ,   atol=1e-4	)	)








'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





