'''simple docstring'''




from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
  import pyximport

  pyximport.install(setup_args={"""include_dirs""": np.get_include()})
  from . import algos_graphormer  # noqa E402

def 			UpperCamelCase_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   int = 512					)					->					List[str]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					x.shape[1] if len(x.shape					) > 1 else 1
       _UpperCAmelCase  :						List[str]							   =					1 + np.arange(0    ,			feature_num * offset    ,			_UpperCAmelCase    ,			dtype=np.intaa					)
       _UpperCAmelCase  :						int							   =					x + feature_offset
       return x

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Any=True					)					->					Any:


       """simple docstring"""







       requires_backends(_UpperCAmelCase    ,			["cython"]					)

       if keep_features and "edge_attr" in item.keys():  # edge_attr
              _UpperCAmelCase  :						Optional[int]							   =					np.asarray(item["edge_attr"]    ,			dtype=np.intaa					)
       else:
              _UpperCAmelCase  :						List[str]							   =					np.ones((len(item["edge_index"][0]					), 1)    ,			dtype=np.intaa					)  # same embedding for all

       if keep_features and "node_feat" in item.keys():  # input_nodes
              _UpperCAmelCase  :						Optional[Any]							   =					np.asarray(item["node_feat"]    ,			dtype=np.intaa					)
       else:
              _UpperCAmelCase  :						Dict							   =					np.ones((item["num_nodes"], 1)    ,			dtype=np.intaa					)  # same embedding for all

       _UpperCAmelCase  :						Optional[int]							   =					np.asarray(item["edge_index"]    ,			dtype=np.intaa					)

       _UpperCAmelCase  :						Union[str, Any]							   =					convert_to_single_emb(_UpperCAmelCase					) + 1
       _UpperCAmelCase  :						int							   =					item["num_nodes"]

       if len(edge_attr.shape					) == 1:
              _UpperCAmelCase  :						Union[str, Any]							   =					edge_attr[:, None]
       _UpperCAmelCase  :						int							   =					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,			dtype=np.intaa					)
       _UpperCAmelCase  :						Optional[int]							   =					convert_to_single_emb(_UpperCAmelCase					) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       _UpperCAmelCase  :						int							   =					np.zeros([num_nodes, num_nodes]    ,			dtype=_UpperCAmelCase					)
       _UpperCAmelCase  :						Dict							   =					True

       _UpperCAmelCase  :						Union[str, Any]							   =					algos_graphormer.floyd_warshall(_UpperCAmelCase					)
       _UpperCAmelCase  :						List[Any]							   =					np.amax(_UpperCAmelCase					)

       _UpperCAmelCase  :						List[Any]							   =					algos_graphormer.gen_edge_input(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						List[Any]							   =					np.zeros([num_nodes + 1, num_nodes + 1]    ,			dtype=np.single					)  # with graph token

       # combine
       _UpperCAmelCase  :						Union[str, Any]							   =					input_nodes + 1  # we shift all indices by one for padding
       _UpperCAmelCase  :						Union[str, Any]							   =					attn_bias
       _UpperCAmelCase  :						str							   =					attn_edge_type
       _UpperCAmelCase  :						str							   =					shortest_path_result.astype(np.intaa					) + 1  # we shift all indices by one for padding
       _UpperCAmelCase  :						Any							   =					np.sum(_UpperCAmelCase    ,			axis=1					).reshape(-1					) + 1  # we shift all indices by one for padding
       _UpperCAmelCase  :						List[Any]							   =					item["in_degree"]  # for undirected graph
       _UpperCAmelCase  :						int							   =					input_edges + 1  # we shift all indices by one for padding
       if "labels" not in item:
              _UpperCAmelCase  :						str							   =					item["y"]

       return item


class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       Any=20		,      A		:       Optional[Any]=False ):
             if not is_cython_available():
                    raise ImportError("Graphormer preprocessing needs Cython (pyximport)" )

             _UpperCAmelCase  :						List[str]							   =					spatial_pos_max
             _UpperCAmelCase  :						Union[str, Any]							   =					on_the_fly_processing





      def __call__( self		:       str		,      A		:       List[dict] ):
             if self.on_the_fly_processing:
                    _UpperCAmelCase  :						List[str]							   =					[preprocess_item(A ) for i in features]

             if not isinstance(features[0]		,      A ):
                    _UpperCAmelCase  :						str							   =					[vars(A ) for f in features]
             _UpperCAmelCase  :						Any							   =					{}

             _UpperCAmelCase  :						int							   =					max(len(i["input_nodes"] ) for i in features )
             _UpperCAmelCase  :						Optional[int]							   =					len(features[0]["input_nodes"][0] )
             _UpperCAmelCase  :						Dict							   =					len(features[0]["attn_edge_type"][0][0] )
             _UpperCAmelCase  :						Optional[int]							   =					max(len(i["input_edges"][0][0] ) for i in features )
             _UpperCAmelCase  :						Optional[int]							   =					len(features[0]["input_edges"][0][0][0] )
             _UpperCAmelCase  :						Any							   =					len(A )

             _UpperCAmelCase  :						Dict							   =					torch.zeros(A		,      max_node_num + 1		,      max_node_num + 1		,      dtype=torch.float )
             _UpperCAmelCase  :						List[str]							   =					torch.zeros(A		,      A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Any							   =					torch.zeros(A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.zeros(A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						int							   =					torch.zeros(A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Optional[int]							   =					torch.zeros(
                 A		,      A		,      A		,      A		,      A		,      dtype=torch.long )

             for ix, f in enumerate(A ):
                    for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                           _UpperCAmelCase  :						Optional[int]							   =					torch.tensor(f[k] )

                    if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max] ) > 0:
                           _UpperCAmelCase  :						Optional[int]							   =					float("-inf" )

                    _UpperCAmelCase  :						Any							   =					f["attn_bias"]
                    _UpperCAmelCase  :						List[str]							   =					f[
                        "attn_edge_type"
                    ]
                    _UpperCAmelCase  :						str							   =					f["spatial_pos"]
                    _UpperCAmelCase  :						List[Any]							   =					f["in_degree"]
                    _UpperCAmelCase  :						Any							   =					f["input_nodes"]
                    _UpperCAmelCase  :						Union[str, Any]							   =					f["input_edges"]

             _UpperCAmelCase  :						Dict							   =					batch["in_degree"]

             _UpperCAmelCase  :						Union[str, Any]							   =					features[0]["labels"]
             if len(A ) == 1:  # one task
                    if isinstance(sample[0]		,      A ):  # regression
                           _UpperCAmelCase  :						List[str]							   =					torch.from_numpy(np.concatenate([i["labels"] for i in features] ) )
                    else:  # binary classification
                           _UpperCAmelCase  :						Union[str, Any]							   =					torch.from_numpy(np.concatenate([i["labels"] for i in features] ) )
             else:  # multi task classification, left to float to keep the NaNs
                    _UpperCAmelCase  :						List[Any]							   =					torch.from_numpy(np.stack([i["labels"] for i in features]		,      axis=0 ) )

             return batch


'''simple docstring'''




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  __SCREAMING_SNAKE_CASE :  Any  =						False


if is_vision_available():
  from PIL import Image
else:

  class 			lowerCamelCase_			:
        '''simple docstring'''

        @staticmethod
        def   _A			( *A		:       str		,      **A		:       Optional[Any] ):
               pass

@is_pipeline_test
@require_vision
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		MODEL_FOR_VISION_2_SEQ_MAPPING
      __UpperCamelCase:						int      			=		TF_MODEL_FOR_VISION_2_SEQ_MAPPING




      def   _A			( self		:       Any		,      A		:       Any		,      A		:       str		,      A		:       Optional[int] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model=A		,      tokenizer=A		,      image_processor=A )
             _UpperCAmelCase  :						Union[str, Any]							   =					[
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]
             return pipe, examples




      def   _A			( self		:       Tuple		,      A		:       Union[str, Any]		,      A		:       Optional[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": ANY(A )}],
                     [{"generated_text": ANY(A )}],
                 ]		,      )




      @require_tf
      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2"		,      framework="tf" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						Tuple							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )

             _UpperCAmelCase  :						Dict							   =					pipe(A		,      max_new_tokens=1 )
             self.assertEqual(
                 A		,      [{"generated_text": "growth"}]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2" )
             _UpperCAmelCase  :						Optional[int]							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Optional[Any]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						str							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-BlipForConditionalGeneration" )
             _UpperCAmelCase  :						Any							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"
             _UpperCAmelCase  :						int							   =					"a photo of"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertTrue(outputs[0]["generated_text"].startswith(A ) )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						int							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )




      @slow
      @require_torch
      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						Tuple							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						int							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Optional[int]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}] )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Tuple							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Dict							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cartoon of a purple character."}] )




      @slow
      @require_torch
      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						List[str]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						int							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Dict							   =					"a photography of"

             _UpperCAmelCase  :						Optional[int]							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photography of a volcano"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[Any]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Optional[int]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Optional[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[str]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[Any]							   =					"a photo of a"

             _UpperCAmelCase  :						Tuple							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photo of a tent with a tent and a tent in the background."}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11		,      reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." )
      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="google/pix2struct-ai2d-base" )
             _UpperCAmelCase  :						List[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[Any]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Optional[int]							   =					"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "ash cloud"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )





      @slow
      @require_tf
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en"		,      framework="tf" )
             _UpperCAmelCase  :						str							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						Optional[Any]							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )

