


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_snake_case  :	Optional[int]			      =       logging.get_logger(__name__)

_snake_case  :	Any			      =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case  :	Union[str, Any]			      =       {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_snake_case  :	str			      =       {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
_snake_case  :	Tuple			      =       {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case  :	List[str]			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_snake_case  :	str			      =       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_snake_case  :	Optional[int]			      =       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_snake_case  :	List[str]			      =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	Optional[int]			      =       {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_snake_case  :	Tuple			      =       {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Optional[int]  						=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							List[str]  						=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							List[str]  						=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						DPRContextEncoderTokenizer

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							List[Any]  						=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							int  						=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							Tuple  						=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						DPRQuestionEncoderTokenizer
_snake_case  :	Optional[Any]			      =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_snake_case  :	str			      =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_snake_case  :	Optional[Any]			      =       R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "

@add_start_docstrings(_lowerCAmelCase   )
class        a	:




							"""simple docstring"""


							def __call__(							self :						Optional[int]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Union[bool, str] = False					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						Optional[bool] = None					,			**lowerCamelCase :						Optional[Any]					,			)			->					BatchEncoding:
														if titles is None and texts is None:
																					return super().__call__(
																					    lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase					,			return_attention_mask=lowerCamelCase					,			**lowerCamelCase					,			)
														elif titles is None or texts is None:
																					__snake_case	:    Optional[int]											=     titles if texts is None else texts
																					return super().__call__(
																					    lowerCamelCase					,			lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase					,			return_attention_mask=lowerCamelCase					,			**lowerCamelCase					,			)
														__snake_case	:    List[str]											=     titles if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [titles]
														__snake_case	:    int											=     texts if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [texts]
														__snake_case	:    str											=     len(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     questions if not isinstance(lowerCamelCase					,			lowerCamelCase       ) else [questions] * n_passages
														assert len(lowerCamelCase       ) == len(
														    lowerCamelCase       ), F'There should be as many titles than texts but got {len(lowerCamelCase       )} titles and {len(lowerCamelCase       )} texts.'
														__snake_case	:    Optional[Any]											=     super().__call__(lowerCamelCase					,			lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    Union[str, Any]											=     super().__call__(lowerCamelCase					,			add_special_tokens=lowerCamelCase					,			padding=lowerCamelCase					,			truncation=lowerCamelCase       )["input_ids"]
														__snake_case	:    str											=     {
														    "input_ids": [
														        (encoded_question_and_title + encoded_text)[:max_length]
														        if max_length is not None and truncation
														        else encoded_question_and_title + encoded_text
														        for encoded_question_and_title, encoded_text in zip(lowerCamelCase					,			lowerCamelCase       )
														    ]
														}
														if return_attention_mask is not False:
																					__snake_case	:    Optional[Any]											=     []
																					for input_ids in encoded_inputs["input_ids"]:
																												attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
																					__snake_case	:    Optional[int]											=     attention_mask
														return self.pad(lowerCamelCase					,			padding=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors=lowerCamelCase       )

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						BatchEncoding					,			lowerCamelCase :						DPRReaderOutput					,			lowerCamelCase :						int = 16					,			lowerCamelCase :						int = 64					,			lowerCamelCase :						int = 4					,			)			->					List[DPRSpanPrediction]:
														__snake_case	:    Dict											=     reader_input["input_ids"]
														__snake_case				,    __snake_case				,    __snake_case	:    List[str]											=     reader_output[:3]
														__snake_case	:    Optional[int]											=     len(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     sorted(range(lowerCamelCase       )					,			reverse=lowerCamelCase					,			key=relevance_logits.__getitem__       )
														__snake_case	:    List[DPRReaderOutput]											=     []
														for doc_id in sorted_docs:
																					__snake_case	:    List[Any]											=     list(input_ids[doc_id]       )
																					# assuming question & title information is at the beginning of the sequence
																					__snake_case	:    Optional[Any]											=     sequence_ids.index(self.sep_token_id					,			2       ) + 1  # second sep id
																					if sequence_ids[-1] == self.pad_token_id:
																												__snake_case	:    Optional[Any]											=     sequence_ids.index(self.pad_token_id       )
																					else:
																												__snake_case	:    Optional[int]											=     len(lowerCamelCase       )

																					__snake_case	:    Union[str, Any]											=     self._get_best_spans(
																					    start_logits=start_logits[doc_id][passage_offset:sequence_len]					,			end_logits=end_logits[doc_id][passage_offset:sequence_len]					,			max_answer_length=lowerCamelCase					,			top_spans=lowerCamelCase					,			)
																					for start_index, end_index in best_spans:
																												start_index += passage_offset
																												end_index += passage_offset
																												nbest_spans_predictions.append(
																												    DPRSpanPrediction(
																												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]					,			relevance_score=relevance_logits[doc_id]					,			doc_id=lowerCamelCase					,			start_index=lowerCamelCase					,			end_index=lowerCamelCase					,			text=self.decode(sequence_ids[start_index : end_index + 1]       )					,			)       )
																					if len(lowerCamelCase       ) >= num_spans:
																												break
														return nbest_spans_predictions[:num_spans]






							def 					__snake_case      (							self :						int					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						List[int]					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			)			->					List[DPRSpanPrediction]:
														__snake_case	:    int											=     []
														for start_index, start_score in enumerate(lowerCamelCase       ):
																					for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
																												scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
														__snake_case	:    Optional[Any]											=     sorted(lowerCamelCase					,			key=lambda lowerCamelCase       : x[1]					,			reverse=lowerCamelCase       )
														__snake_case	:    str											=     []
														for (start_index, end_index), score in scores:
																					assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
																					__snake_case	:    Dict											=     end_index - start_index + 1
																					assert length <= max_answer_length, F'Span is too long: {length} > {max_answer_length}'
																					if any(
																					    start_index <= prev_start_index <= prev_end_index <= end_index
																					    or prev_start_index <= start_index <= end_index <= prev_end_index
																					    for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
																												continue
																					chosen_span_intervals.append((start_index, end_index)       )

																					if len(lowerCamelCase       ) == top_spans:
																												break
														return chosen_span_intervals

@add_end_docstrings(_lowerCAmelCase   )
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						VOCAB_FILES_NAMES
							__UpperCAmelCase					:							Tuple  						=						READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase					:							List[str]  						=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase					:							Dict  						=						READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase					:							List[str]  						=						["input_ids", "attention_mask"]
							__UpperCAmelCase					:							Dict  						=						DPRReaderTokenizer





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        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    Any											=     self.config_class(**self.inputs_dict       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"embed_dim"       )       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"num_heads"       )       )

class        a	:




							"""simple docstring"""


							def __init__(							self :						str					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Any=13					,			lowerCamelCase :						Any=64					,			lowerCamelCase :						int=3					,			lowerCamelCase :						Tuple=[16, 48, 96]					,			lowerCamelCase :						Optional[int]=[1, 3, 6]					,			lowerCamelCase :						List[str]=[1, 2, 10]					,			lowerCamelCase :						Any=[7, 3, 3]					,			lowerCamelCase :						Any=[4, 2, 2]					,			lowerCamelCase :						Optional[Any]=[2, 1, 1]					,			lowerCamelCase :						str=[2, 2, 2]					,			lowerCamelCase :						Dict=[False, False, True]					,			lowerCamelCase :						Dict=[0.0, 0.0, 0.0]					,			lowerCamelCase :						Optional[Any]=0.02					,			lowerCamelCase :						Tuple=1E-12					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						List[str]=2					,			)			->					Optional[int]:
														__snake_case	:    Tuple											=     parent
														__snake_case	:    List[str]											=     batch_size
														__snake_case	:    Optional[int]											=     image_size
														__snake_case	:    Optional[int]											=     patch_sizes
														__snake_case	:    Union[str, Any]											=     patch_stride
														__snake_case	:    int											=     patch_padding
														__snake_case	:    Optional[Any]											=     is_training
														__snake_case	:    Optional[Any]											=     use_labels
														__snake_case	:    Tuple											=     num_labels
														__snake_case	:    Union[str, Any]											=     num_channels
														__snake_case	:    Tuple											=     embed_dim
														__snake_case	:    List[str]											=     num_heads
														__snake_case	:    Dict											=     stride_kv
														__snake_case	:    Optional[int]											=     depth
														__snake_case	:    List[Any]											=     cls_token
														__snake_case	:    List[Any]											=     attention_drop_rate
														__snake_case	:    Any											=     initializer_range
														__snake_case	:    str											=     layer_norm_eps

							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    str											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

														__snake_case	:    Optional[Any]											=     self.get_config()
														return config, pixel_values, labels

							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														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 :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						str       )			->					Tuple:
														__snake_case	:    int											=     CvtModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[str]											=     model(lowerCamelCase       )
														__snake_case	:    List[Any]											=     (self.image_size, self.image_size)
														__snake_case				,    __snake_case	:    List[str]											=     image_size[0], image_size[1]
														for i in range(len(self.depth       )       ):
																					__snake_case	:    int											=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
																					__snake_case	:    Union[str, Any]											=     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 :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						int       )			->					Optional[Any]:
														__snake_case	:    str											=     self.num_labels
														__snake_case	:    Any											=     CvtForImageClassification(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[Any]											=     model(lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)       )






							def 					__snake_case      (							self :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.prepare_config_and_inputs()
														__snake_case				,    __snake_case				,    __snake_case	:    str											=     config_and_inputs
														__snake_case	:    List[str]											=     {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[str]  						=						(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase					:							List[str]  						=						(
							    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase					:							List[str]  						=						False
							__UpperCAmelCase					:							Tuple  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False
							__UpperCAmelCase					:							Optional[int]  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					List[str]:
														__snake_case	:    Tuple											=     CvtModelTester(self       )
														__snake_case	:    Tuple											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														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 :						Tuple       )			->					Union[str, Any]:
														return

							@unittest.skip(reason="Cvt does not output attentions"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
														pass

							@unittest.skip(reason="Cvt does not use inputs_embeds"       )
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														pass

							@unittest.skip(reason="Cvt does not support input and output embeddings"       )
							def 					__snake_case      (							self :						int       )			->					List[Any]:
														pass

							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case				,    __snake_case	:    Optional[int]											=     self.model_tester.prepare_config_and_inputs_for_common()

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

																					__snake_case	:    Dict											=     ["pixel_values"]
																					self.assertListEqual(arg_names[:1]					,			lowerCamelCase       )

							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						int       )			->					Optional[Any]:
														def check_hidden_states_output(lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any]       ):
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()

																					with torch.no_grad():
																												__snake_case	:    List[str]											=     model(**self._prepare_for_class(lowerCamelCase					,			lowerCamelCase       )       )

																					__snake_case	:    List[Any]											=     outputs.hidden_states

																					__snake_case	:    Optional[int]											=     len(self.model_tester.depth       )
																					self.assertEqual(len(lowerCamelCase       )					,			lowerCamelCase       )

																					# 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,
																					    ]					,			)

														__snake_case				,    __snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Union[str, Any]											=     True
																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__snake_case	:    List[str]											=     True

																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[Any]:
														__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase       )

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
							def 					__snake_case      (							self :						List[str]       )			->					Optional[int]:
														pass






							@slow
							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case	:    int											=     CvtModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    Any											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

@require_torch
@require_vision
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






							@slow
							def 					__snake_case      (							self :						int       )			->					Tuple:
														__snake_case	:    Optional[Any]											=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowerCamelCase       )

														__snake_case	:    Optional[int]											=     self.default_image_processor
														__snake_case	:    Union[str, Any]											=     prepare_img()
														__snake_case	:    Dict											=     image_processor(images=lowerCamelCase					,			return_tensors="pt"       ).to(lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    Any											=     model(**lowerCamelCase       )

														# verify the logits
														__snake_case	:    Any											=     torch.Size((1, 1000)       )
														self.assertEqual(outputs.logits.shape					,			lowerCamelCase       )

														__snake_case	:    Optional[Any]											=     torch.tensor([0.92_85, 0.90_15, -0.31_50]       ).to(lowerCamelCase       )

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

