




from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

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

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
     from transformers import (
         TFBertModel,
         TFCLIPVisionModel,
         TFDeiTModel,
         TFRobertaModel,
         TFVisionTextDualEncoderModel,
         TFViTModel,
         VisionTextDualEncoderConfig,
     )

if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  if isinstance(UpperCamelCase	,      collections.abc.Iterable      ):
    return x
  return (x, x)






@require_tf
class  _lowerCAmelCase     :

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        pass

      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCAmelCase      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  Union[str, Any]		       =     TFVisionTextDualEncoderModel(__UpperCAmelCase		)

        lowerCAmelCase__				:  Any		       =     model(input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)

        self.assertEqual(output['''text_embeds'''].shape      ,				(input_ids.shape[0], config.projection_dim)		)
        self.assertEqual(output['''image_embeds'''].shape      ,				(pixel_values.shape[0], config.projection_dim)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.get_vision_text_model(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase      ,				text_model=__UpperCAmelCase		)

        lowerCAmelCase__				:  int		       =     model(input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)

        self.assertEqual(output['''text_embeds'''].shape      ,				(input_ids.shape[0], model.config.projection_dim)		)
        self.assertEqual(output['''image_embeds'''].shape      ,				(pixel_values.shape[0], model.config.projection_dim)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.get_vision_text_model(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     {'''vision_model''': vision_model, '''text_model''': text_model}
        lowerCAmelCase__				:  List[str]		       =     TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCAmelCase		)

        lowerCAmelCase__				:  List[Any]		       =     model(input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)

        self.assertEqual(output['''text_embeds'''].shape      ,				(input_ids.shape[0], model.config.projection_dim)		)
        self.assertEqual(output['''image_embeds'''].shape      ,				(pixel_values.shape[0], model.config.projection_dim)		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     self.get_vision_text_model(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase      ,				text_model=__UpperCAmelCase		)

        lowerCAmelCase__				:  Tuple		       =     model(input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     output[0].numpy()

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(__UpperCAmelCase		)
          lowerCAmelCase__				:  Any		       =     TFVisionTextDualEncoderModel.from_pretrained(__UpperCAmelCase		)

          lowerCAmelCase__				:  List[Any]		       =     model(input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)
          lowerCAmelCase__				:  str		       =     after_output[0].numpy()
          lowerCAmelCase__				:  Union[str, Any]		       =     np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(__UpperCAmelCase      ,				1e-5		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     self.get_vision_text_model(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase      ,				text_model=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     model(
            input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase      ,				output_attentions=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     output.vision_model_output.attentions
        self.assertEqual(len(__UpperCAmelCase		)      ,				vision_config.num_hidden_layers		)

        # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCAmelCase__				:  Dict		       =     to_atuple(vision_model.config.image_size		)
        lowerCAmelCase__				:  Union[str, Any]		       =     to_atuple(vision_model.config.patch_size		)
        lowerCAmelCase__				:  Optional[int]		       =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        lowerCAmelCase__				:  str		       =     num_patches + 1
        self.assertEqual(vision_attentions[0].shape[-3:]      ,				(vision_config.num_attention_heads, seq_len, seq_len)		)

        lowerCAmelCase__				:  Any		       =     output.text_model_output.attentions
        self.assertEqual(len(__UpperCAmelCase		)      ,				text_config.num_hidden_layers		)

        self.assertEqual(
            text_attentions[0].shape[-3:]      ,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     np.abs((a - b)		).max()
        self.assertLessEqual(__UpperCAmelCase      ,				__UpperCAmelCase      ,				f"""Difference between torch and flax is {diff} (>= {tol})."""		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_model(**__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.prepare_config_and_inputs()
        self.check_model_from_pretrained_configs(**__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_from_pretrained(**__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.prepare_config_and_inputs()
        self.check_save_load(**__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.prepare_config_and_inputs()
        self.check_vision_text_output_attention(**__UpperCAmelCase		)







      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     self.get_pretrained_model_and_inputs()

        lowerCAmelCase__				:  Optional[Any]		       =     model_a(**__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     outputs[0].numpy()

        with tempfile.TemporaryDirectory() as tmp_dirname:
          model_a.save_pretrained(__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[int]		       =     TFVisionTextDualEncoderModel.from_pretrained(__UpperCAmelCase		)

          lowerCAmelCase__				:  int		       =     model_a(**__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[Any]		       =     after_outputs[0].numpy()
          lowerCAmelCase__				:  int		       =     np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(__UpperCAmelCase      ,				1e-5		)






@require_tf
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            '''hf-internal-testing/tiny-random-vit'''      ,				'''hf-internal-testing/tiny-random-bert'''		)
        lowerCAmelCase__				:  List[str]		       =     13
        lowerCAmelCase__				:  int		       =     floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]		)
        lowerCAmelCase__				:  Any		       =     ids_tensor([batch_size, 4]      ,				model.text_model.config.vocab_size		)
        lowerCAmelCase__				:  Dict		       =     random_attention_mask([batch_size, 4]		)
        lowerCAmelCase__				:  str		       =     {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

        return model, inputs

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     TFViTModel(__UpperCAmelCase      ,				name='''vision_model'''		)
        lowerCAmelCase__				:  int		       =     TFBertModel(__UpperCAmelCase      ,				name='''text_model'''		)
        return vision_model, text_model







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFViTModelTester(self		)
        lowerCAmelCase__				:  Union[str, Any]		       =     TFBertModelTester(self		)
        lowerCAmelCase__				:  Tuple		       =     vit_model_tester.prepare_config_and_inputs()
        lowerCAmelCase__				:  Union[str, Any]		       =     bert_model_tester.prepare_config_and_inputs()

        lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     vision_config_and_inputs

        (
            (
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			
        )				:  str		       =     text_config_and_inputs

        return {
            "text_config": text_config,
            "vision_config": vision_config,
            "pixel_values": pixel_values,
            "attention_mask": input_mask,
            "input_ids": input_ids,
            "text_token_type_ids": token_type_ids,
            "text_sequence_labels": sequence_labels,
            "text_token_labels": token_labels,
            "text_choice_labels": choice_labels,
        }






@require_tf
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):

      def 	__magic_name__(   self		):
        # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
        # just reinitialize it.
        lowerCAmelCase__				:  Union[str, Any]		       =     TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            '''Rocketknight1/tiny-random-deit-tf'''      ,				'''hf-internal-testing/tiny-random-roberta'''		)
        lowerCAmelCase__				:  Tuple		       =     13
        lowerCAmelCase__				:  int		       =     floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]		)
        lowerCAmelCase__				:  List[str]		       =     ids_tensor([batch_size, 4]      ,				model.text_model.config.vocab_size		)
        lowerCAmelCase__				:  Optional[int]		       =     random_attention_mask([batch_size, 4]		)
        lowerCAmelCase__				:  Optional[int]		       =     {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

        return model, inputs

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     self.get_vision_text_model(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase      ,				text_model=__UpperCAmelCase		)

        lowerCAmelCase__				:  List[str]		       =     model(
            input_ids=__UpperCAmelCase      ,				pixel_values=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase      ,				output_attentions=__UpperCAmelCase		)

        lowerCAmelCase__				:  Tuple		       =     output.vision_model_output.attentions
        self.assertEqual(len(__UpperCAmelCase		)      ,				vision_config.num_hidden_layers		)

        # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
        lowerCAmelCase__				:  str		       =     to_atuple(vision_model.config.image_size		)
        lowerCAmelCase__				:  List[str]		       =     to_atuple(vision_model.config.patch_size		)
        lowerCAmelCase__				:  Tuple		       =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        lowerCAmelCase__				:  Union[str, Any]		       =     num_patches + 2
        self.assertEqual(vision_attentions[0].shape[-3:]      ,				(vision_config.num_attention_heads, seq_len, seq_len)		)

        lowerCAmelCase__				:  int		       =     output.text_model_output.attentions
        self.assertEqual(len(__UpperCAmelCase		)      ,				text_config.num_hidden_layers		)

        self.assertEqual(
            text_attentions[0].shape[-3:]      ,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     TFDeiTModel(__UpperCAmelCase      ,				name='''vision_model'''		)
        lowerCAmelCase__				:  Any		       =     TFRobertaModel(__UpperCAmelCase      ,				name='''text_model'''		)
        return vision_model, text_model







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     TFDeiTModelTester(self		)
        lowerCAmelCase__				:  Union[str, Any]		       =     TFRobertaModelTester(self		)
        lowerCAmelCase__				:  Any		       =     vit_model_tester.prepare_config_and_inputs()
        lowerCAmelCase__				:  str		       =     bert_model_tester.prepare_config_and_inputs()

        lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     vision_config_and_inputs

        (
            (
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			
        )				:  Union[str, Any]		       =     text_config_and_inputs

        return {
            "text_config": text_config,
            "vision_config": vision_config,
            "pixel_values": pixel_values,
            "attention_mask": input_mask,
            "input_ids": input_ids,
            "text_token_type_ids": token_type_ids,
            "text_sequence_labels": sequence_labels,
            "text_token_labels": token_labels,
            "text_choice_labels": choice_labels,
        }






@require_tf
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            '''Rocketknight1/tiny-random-clip-tf'''      ,				'''hf-internal-testing/tiny-random-bert'''		)
        lowerCAmelCase__				:  List[str]		       =     13
        lowerCAmelCase__				:  Optional[int]		       =     floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]		)
        lowerCAmelCase__				:  Tuple		       =     ids_tensor([batch_size, 4]      ,				model.text_model.config.vocab_size		)
        lowerCAmelCase__				:  Dict		       =     random_attention_mask([batch_size, 4]		)
        lowerCAmelCase__				:  List[Any]		       =     {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

        return model, inputs

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     TFCLIPVisionModel(__UpperCAmelCase      ,				name='''vision_model'''		)
        lowerCAmelCase__				:  Optional[Any]		       =     TFBertModel(__UpperCAmelCase      ,				name='''text_model'''		)
        return vision_model, text_model







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     TFCLIPVisionModelTester(self		)
        lowerCAmelCase__				:  Tuple		       =     TFBertModelTester(self		)
        lowerCAmelCase__				:  str		       =     clip_model_tester.prepare_config_and_inputs()
        lowerCAmelCase__				:  List[str]		       =     bert_model_tester.prepare_config_and_inputs()

        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     vision_config_and_inputs

        (
            (
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			(
            lowerCAmelCase__
        )       ,			
        )				:  Dict		       =     text_config_and_inputs

        return {
            "text_config": text_config,
            "vision_config": vision_config,
            "pixel_values": pixel_values,
            "attention_mask": input_mask,
            "input_ids": input_ids,
            "text_token_type_ids": token_type_ids,
            "text_sequence_labels": sequence_labels,
            "text_token_labels": token_labels,
            "text_choice_labels": choice_labels,
        }






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

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFVisionTextDualEncoderModel.from_pretrained(
            '''clip-italian/clip-italian'''      ,				logit_scale_init_value=1.0      ,				from_pt=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''		)

        lowerCAmelCase__				:  Dict		       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     processor(
            text=['''una foto di un gatto''', '''una foto di un cane''']      ,				images=__UpperCAmelCase      ,				padding=__UpperCAmelCase      ,				return_tensors='''np'''		)

        lowerCAmelCase__				:  List[Any]		       =     model(**__UpperCAmelCase		)

        # verify the logits
        self.assertEqual(outputs.logits_per_image.shape      ,				(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
        self.assertEqual(
            outputs.logits_per_text.shape      ,				(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])      ,				)

        lowerCAmelCase__				:  Tuple		       =     np.array([[1.2284727, 0.3104122]]		)

        self.assertTrue(np.allclose(outputs.logits_per_image.numpy()      ,				__UpperCAmelCase      ,				atol=1e-3		)		)







from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class  _lowerCAmelCase     (  _lowercase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     SMALL_MODEL_IDENTIFIER
        lowerCAmelCase__				:  List[str]		       =     '''pt'''
        lowerCAmelCase__				:  int		       =     '''tf'''

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     AutoModel.from_pretrained(self.test_model		)
        model_pt.save_pretrained(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     TFAutoModel.from_pretrained(self.test_model      ,				from_pt=__UpperCAmelCase		)
        model_tf.save_pretrained(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     '''mock_framework'''

        # Framework provided - return whatever the user provides
        lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(self.test_model      ,				__UpperCAmelCase		)
        self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

        # Local checkpoint and framework provided - return provided framework
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(__UpperCAmelCase      ,				__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  List[str]		       =     FeaturesManager.determine_framework(__UpperCAmelCase      ,				__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_tf		)

        # Invalid local checkpoint
        with TemporaryDirectory() as local_invalid_ckpt:
          with self.assertRaises(__UpperCAmelCase		):
            lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # PyTorch not in environment -> use TensorFlow
        lowerCAmelCase__				:  Union[str, Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Optional[int]		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_tf		)

        # Both in environment -> use PyTorch
        lowerCAmelCase__				:  Union[str, Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		), patch(
            '''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[Any]		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # Both not in environment -> raise error
        lowerCAmelCase__				:  Optional[int]		       =     MagicMock(return_value=__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		), patch(
            '''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          with self.assertRaises(__UpperCAmelCase		):
            lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(self.test_model		)

