






'''simple docstring'''







import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:




							class 		UpperCamelCase__    :




									'''simple docstring'''





									@staticmethod
									def 			UpperCamelCase_					(      *_lowerCAmelCase   ,**_lowerCAmelCase       ):
															pass




@is_pipeline_test
@require_torch
@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  pipeline("""visual-question-answering"""   ,model="""hf-internal-testing/tiny-vilt-random-vqa"""       )
								lowerCamelCase__         =  [
								    {
								        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       ),
								        """question""": """How many cats are there?""",
								    },
								    {
								        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
								        """question""": """How many cats are there?""",
								    },
								]
								return vqa_pipeline, examples






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  vqa_pipeline(_lowerCAmelCase   ,top_k=1       )
								self.assertEqual(
								    _lowerCAmelCase   ,[
								        [{"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}],
								        [{"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}],
								    ]   ,)






		@require_torch
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  pipeline("""visual-question-answering"""   ,model="""hf-internal-testing/tiny-vilt-random-vqa"""       )
								lowerCamelCase__         =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""
								lowerCamelCase__         =  """How many cats are there?"""

								lowerCamelCase__         =  vqa_pipeline(image=_lowerCAmelCase   ,question="""How many cats are there?"""   ,top_k=2       )
								self.assertEqual(
								    _lowerCAmelCase   ,[{"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}, {"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}]       )

								lowerCamelCase__         =  vqa_pipeline({"""image""": image, """question""": question}   ,top_k=2       )
								self.assertEqual(
								    _lowerCAmelCase   ,[{"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}, {"""score""": ANY(_lowerCAmelCase       ), """answer""": ANY(_lowerCAmelCase       )}]       )






		@slow
		@require_torch
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  pipeline("""visual-question-answering"""   ,model="""dandelin/vilt-b32-finetuned-vqa"""       )
								lowerCamelCase__         =  """./tests/fixtures/tests_samples/COCO/000000039769.png"""
								lowerCamelCase__         =  """How many cats are there?"""

								lowerCamelCase__         =  vqa_pipeline(image=_lowerCAmelCase   ,question=_lowerCAmelCase   ,top_k=2       )
								self.assertEqual(
								    nested_simplify(_lowerCAmelCase   ,decimals=4       )   ,[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]       )

								lowerCamelCase__         =  vqa_pipeline({"""image""": image, """question""": question}   ,top_k=2       )
								self.assertEqual(
								    nested_simplify(_lowerCAmelCase   ,decimals=4       )   ,[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]       )

								lowerCamelCase__         =  vqa_pipeline(
								    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]   ,top_k=2       )
								self.assertEqual(
								    nested_simplify(_lowerCAmelCase   ,decimals=4       )   ,[[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2   ,)






		@require_tf
		@unittest.skip("""Visual question answering not implemented in TF"""       )
		def 			UpperCamelCase_					(      self       ):
								pass









'''simple docstring'''







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    :      Union[str, Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  R"""\w+[.]\d+"""
						lowerCamelCase__         =  re.findall(__lowerCAmelCase   ,    __lowerCAmelCase						)
						for pat in pats:
												lowerCamelCase__         =  key.replace(__lowerCAmelCase   ,    """_""".join(pat.split("""."""						)						)						)
						return key
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple						):
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
						if (
						    any("""norm""" in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""embedding""",)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowerCamelCase__         =  pt_tensor.transpose(2   ,    3   ,    1   ,    0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight":
												lowerCamelCase__         =  pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""weight""",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""bias""",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple=42						):
						# Step 1: Convert pytorch tensor to numpy
						lowerCamelCase__         =  {k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowerCamelCase__         =  flax_model.init_weights(PRNGKey(__lowerCAmelCase						)						)

						lowerCamelCase__         =  flatten_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  {}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
												lowerCamelCase__         =  tuple(renamed_pt_key.split("""."""						)						)

												# Correctly rename weight parameters
												lowerCamelCase__       ,			lowerCamelCase__         =  rename_key_and_reshape_tensor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowerCamelCase__         =  jnp.asarray(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)

