




import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      def __init__(       self   :	Tuple	,    snake_case   :	Dict	,    snake_case   :	Any=3	,    snake_case   :	List[Any]=32	,    snake_case   :	Any=3	,    snake_case   :	Optional[Any]=10	,    snake_case   :	int=[10, 20, 30, 40]	,    snake_case   :	Tuple=[1, 1, 2, 1]	,    snake_case   :	Dict=True	,    snake_case   :	Optional[int]=True	,    snake_case   :	Tuple="relu"	,    snake_case   :	List[Any]=3	,    snake_case   :	Tuple=None	,    ):
         __UpperCamelCase											=  parent
         __UpperCamelCase											=  batch_size
         __UpperCamelCase											=  image_size
         __UpperCamelCase											=  num_channels
         __UpperCamelCase											=  embeddings_size
         __UpperCamelCase											=  hidden_sizes
         __UpperCamelCase											=  depths
         __UpperCamelCase											=  is_training
         __UpperCamelCase											=  use_labels
         __UpperCamelCase											=  hidden_act
         __UpperCamelCase											=  num_labels
         __UpperCamelCase											=  scope
         __UpperCamelCase											=  len(snake_case			)







      def  snake_case		(       self   :	List[Any]			):
         __UpperCamelCase											=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

         __UpperCamelCase											=  self.get_config()

         return config, pixel_values







      def  snake_case		(       self   :	Any			):
         return ResNetConfig(
             num_channels=self.num_channels	,    embeddings_size=self.embeddings_size	,    hidden_sizes=self.hidden_sizes	,    depths=self.depths	,    hidden_act=self.hidden_act	,    num_labels=self.num_labels	,    image_size=self.image_size	,    )







      def  snake_case		(       self   :	Any	,    snake_case   :	Any	,    snake_case   :	Dict			):
         __UpperCamelCase											=  FlaxResNetModel(config=snake_case			)
         __UpperCamelCase											=  model(snake_case			)

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape	,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,    )







      def  snake_case		(       self   :	List[str]	,    snake_case   :	Optional[int]	,    snake_case   :	str			):
         __UpperCamelCase											=  self.num_labels
         __UpperCamelCase											=  FlaxResNetForImageClassification(config=snake_case			)
         __UpperCamelCase											=  model(snake_case			)
         self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)			)







      def  snake_case		(       self   :	Any			):
         __UpperCamelCase											=  self.prepare_config_and_inputs()
         __UpperCamelCase						, __UpperCamelCase											=  config_and_inputs
         __UpperCamelCase											=  {'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_flax
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Optional[Any]      =   (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

      lowerCAmelCase__     :	List[Any]      =   False
      lowerCAmelCase__     :	Any      =   False
      lowerCAmelCase__     :	Tuple      =   False







      def  snake_case		(       self   :	str			):
         __UpperCamelCase											=  FlaxResNetModelTester(self			)
         __UpperCamelCase											=  ConfigTester(self	,    config_class=snake_case	,    has_text_modality=snake_case			)







      def  snake_case		(       self   :	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   :	List[Any]			):
         return







      def  snake_case		(       self   :	Union[str, Any]			):
         __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case			)







      def  snake_case		(       self   :	Union[str, Any]			):
         __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case			)







      @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
      def  snake_case		(       self   :	Tuple			):
         pass







      @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
      def  snake_case		(       self   :	List[str]			):
         pass







      def  snake_case		(       self   :	int			):
         __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase											=  ['''pixel_values''']
            self.assertListEqual(arg_names[:1]	,    snake_case			)







      def  snake_case		(       self   :	Any			):
         def check_hidden_states_output(snake_case   :	Dict	,    snake_case   :	Any	,    snake_case   :	Dict			):
            __UpperCamelCase											=  model_class(snake_case			)

            __UpperCamelCase											=  model(**self._prepare_for_class(snake_case	,    snake_case			)			)

            __UpperCamelCase											=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            __UpperCamelCase											=  self.model_tester.num_stages
            self.assertEqual(len(snake_case			)	,    expected_num_stages + 1			)







      @unittest.skip(reason='''ResNet does not use feedforward chunking'''			)
      def  snake_case		(       self   :	Optional[int]			):
         pass







      def  snake_case		(       self   :	int			):
         __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__			):
               __UpperCamelCase											=  self._prepare_for_class(snake_case	,    snake_case			)
               __UpperCamelCase											=  model_class(snake_case			)

               @jax.jit
               def model_jitted(snake_case   :	int	,    **snake_case   :	Dict			):
                  return model(pixel_values=snake_case	,    **snake_case			)

               with self.subTest('''JIT Enabled'''			):
                  __UpperCamelCase											=  model_jitted(**snake_case			).to_tuple()

               with self.subTest('''JIT Disabled'''			):
                  with jax.disable_jit():
                     __UpperCamelCase											=  model_jitted(**snake_case			).to_tuple()

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







def    __SCREAMING_SNAKE_CASE				(					)    ->		Any:

   """simple docstring"""


   __UpperCamelCase											=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
   return image


@require_flax
class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      @cached_property
      def  snake_case		(       self   :	Any			):
         return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''			) if is_vision_available() else None







      @slow
      def  snake_case		(       self   :	Any			):
         __UpperCamelCase											=  FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''			)

         __UpperCamelCase											=  self.default_image_processor
         __UpperCamelCase											=  prepare_img()
         __UpperCamelCase											=  image_processor(images=snake_case	,    return_tensors='''np'''			)

         __UpperCamelCase											=  model(**snake_case			)

         # verify the logits
         __UpperCamelCase											=  (1, 1000)
         self.assertEqual(outputs.logits.shape	,    snake_case			)

         __UpperCamelCase											=  jnp.array([-11.10_69, -9.78_77, -8.37_77]			)

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












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a_										=       "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	Union[str, Any]      =   (
          "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the "
          "text where to find the answer, and `question`, which is the question, and returns the answer to the question."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

          return output_ids.reshape(snake_case	,    out_b // in_b	,    *output_ids.shape[1:]			)[0][0]







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






