

'''simple docstring'''
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_lowerCamelCase      					=     """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class 					_snake_case		(unittest.TestCase			,			__SCREAMING_SNAKE_CASE):






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Any						=  load_tool("text-question-answering"     )
				self.tool.setup()
				UpperCAmelCase_     :      Any						=  load_tool("text-question-answering"			,remote=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  self.tool(_snake_case			,"What did Hugging Face do in April 2021?"     )
				self.assertEqual(_snake_case			,"launched the BigScience Research Workshop"     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  self.remote_tool(_snake_case			,"What did Hugging Face do in April 2021?"     )
				self.assertEqual(_snake_case			,"launched the BigScience Research Workshop"     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.tool(text=_snake_case			,question="What did Hugging Face do in April 2021?"     )
				self.assertEqual(_snake_case			,"launched the BigScience Research Workshop"     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  self.remote_tool(text=_snake_case			,question="What did Hugging Face do in April 2021?"     )
				self.assertEqual(_snake_case			,"launched the BigScience Research Workshop"     )












'''simple docstring'''
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor


class 							_UpperCAmelCase    :




    def __init__(      self	:							int	,  a	:							Dict	,  a	:							Optional[Any]=1_3	,  a	:							str=3_0	,  a	:							Dict=2	,  a	:							List[Any]=3	,  a	:							Optional[int]=True	,  a	:							Tuple=True	,  a	:							List[Any]=3_2	,  a	:							int=2	,  a	:							List[str]=4	,  a	:							Dict=3_7	,  a	:							Optional[Any]="gelu"	,  a	:							List[str]=0.1	,  a	:							Dict=0.1	,  a	:							List[Any]=1_0	,  a	:							Union[str, Any]=0.02	,  a	:							Union[str, Any]=3	,  a	:							int=None	,  a	:							Any=2	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  parent
       lowercase_						:     Union[str, Any]							     =  batch_size
       lowercase_						:     Any							     =  image_size
       lowercase_						:     str							     =  patch_size
       lowercase_						:     int							     =  num_channels
       lowercase_						:     str							     =  is_training
       lowercase_						:     str							     =  use_labels
       lowercase_						:     List[Any]							     =  hidden_size
       lowercase_						:     List[str]							     =  num_hidden_layers
       lowercase_						:     Dict							     =  num_attention_heads
       lowercase_						:     Optional[int]							     =  intermediate_size
       lowercase_						:     str							     =  hidden_act
       lowercase_						:     Union[str, Any]							     =  hidden_dropout_prob
       lowercase_						:     List[str]							     =  attention_probs_dropout_prob
       lowercase_						:     List[str]							     =  type_sequence_label_size
       lowercase_						:     List[Any]							     =  initializer_range
       lowercase_						:     str							     =  scope
       lowercase_						:     Tuple							     =  encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       lowercase_						:     Any							     =  (image_size // patch_size) ** 2
       lowercase_						:     str							     =  num_patches + 2




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       lowercase_						:     List[Any]							     =  None
       if self.use_labels:
          lowercase_						:     int							     =  ids_tensor([self.batch_size]	,  self.type_sequence_label_size      )

       lowercase_						:     Optional[Any]							     =  self.get_config()

       return config, pixel_values, labels




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return DeiTConfig(
           image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  is_decoder=a	,  initializer_range=self.initializer_range	,  encoder_stride=self.encoder_stride	,  )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							List[str]	,  a	:							str	,  a	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTModel(config=a      )
       lowercase_						:     int							     =  model(a      )
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)      )




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Optional[int]	,  a	:							List[Any]	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  TFDeiTForMaskedImageModeling(config=a      )
       lowercase_						:     Union[str, Any]							     =  model(a      )
       self.parent.assertEqual(
           result.reconstruction.shape	,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     Tuple							     =  TFDeiTForMaskedImageModeling(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Dict							     =  model(a      )
       self.parent.assertEqual(result.reconstruction.shape	,  (self.batch_size, 1, self.image_size, self.image_size)      )




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							List[str]	,  a	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.type_sequence_label_size
       lowercase_						:     Union[str, Any]							     =  TFDeiTForImageClassification(a      )
       lowercase_						:     int							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       lowercase_						:     Dict							     =  1
       lowercase_						:     str							     =  TFDeiTForImageClassification(a      )

       lowercase_						:     Tuple							     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowercase_						:     Any							     =  model(a	,  labels=a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  self.prepare_config_and_inputs()
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  config_and_inputs
       lowercase_						:     Optional[Any]							     =  {"pixel_values": pixel_values}
       return config, inputs_dict


@require_tf
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Tuple									=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase: Tuple									=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    __lowerCamelCase: Union[str, Any]									=     False
    __lowerCamelCase: Any									=     False
    __lowerCamelCase: List[str]									=     False
    __lowerCamelCase: Optional[Any]									=     False




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     int							     =  TFDeiTModelTester(self      )
       lowercase_						:     Any							     =  ConfigTester(self	,  config_class=a	,  has_text_modality=a	,  hidden_size=3_7      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DeiT does not use inputs_embeds"      )
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Tuple							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Dict							     =  model_class(a      )
          self.assertIsInstance(model.get_input_embeddings()	,  (tf.keras.layers.Layer)      )
          lowercase_						:     Optional[int]							     =  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a	,  tf.keras.layers.Dense      )      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     Union[str, Any]							     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase_						:     Any							     =  model_class(a      )
          lowercase_						:     str							     =  inspect.signature(model.call      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase_						:     int							     =  [*signature.parameters.keys()]

          lowercase_						:     Dict							     =  ["pixel_values"]
          self.assertListEqual(arg_names[:1]	,  a      )




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a      )




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							int	,  a	:							Union[str, Any]	,  a	:							Any=False      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  super()._prepare_for_class(a	,  a	,  return_labels=a      )

       if return_labels:
          if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
             del inputs_dict["labels"]

       return inputs_dict





    @slow
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_						:     List[Any]							     =  TFDeiTModel.from_pretrained(a      )
          self.assertIsNotNone(a      )




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




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





@require_tf
@require_vision
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @cached_property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
           if is_vision_available()
           else None
       )





    @slow
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

       lowercase_						:     Union[str, Any]							     =  self.default_image_processor
       lowercase_						:     Tuple							     =  prepare_img()
       lowercase_						:     Optional[Any]							     =  image_processor(images=a	,  return_tensors="tf"      )

       # forward pass
       lowercase_						:     int							     =  model(**a      )

       # verify the logits
       lowercase_						:     Union[str, Any]							     =  tf.TensorShape((1, 1_0_0_0)      )
       self.assertEqual(outputs.logits.shape	,  a      )

       lowercase_						:     Optional[Any]							     =  tf.constant([-1.0266, 0.1912, -1.2861]      )

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




