




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowerCAmelCase_					    = """platform"""

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None	,      )     ->	Any:
  if attention_mask is None:
    lowerCAmelCase__				:  str		       =     np.where(input_ids != config.pad_token_id	,      1	,      0      )
  if decoder_attention_mask is None:
    lowerCAmelCase__				:  Dict		       =     np.where(decoder_input_ids != config.pad_token_id	,      1	,      0      )
  if head_mask is None:
    lowerCAmelCase__				:  Dict		       =     np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  if decoder_head_mask is None:
    lowerCAmelCase__				:  List[Any]		       =     np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  if cross_attn_head_mask is None:
    lowerCAmelCase__				:  List[Any]		       =     np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=13      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=99      ,				__UpperCAmelCase=16      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase="gelu"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=0.02      ,				):
        lowerCAmelCase__				:  List[str]		       =     parent
        lowerCAmelCase__				:  Dict		       =     batch_size
        lowerCAmelCase__				:  Optional[Any]		       =     seq_length
        lowerCAmelCase__				:  Tuple		       =     is_training
        lowerCAmelCase__				:  Any		       =     use_labels
        lowerCAmelCase__				:  int		       =     vocab_size
        lowerCAmelCase__				:  Dict		       =     hidden_size
        lowerCAmelCase__				:  Dict		       =     num_hidden_layers
        lowerCAmelCase__				:  List[Any]		       =     num_attention_heads
        lowerCAmelCase__				:  Tuple		       =     intermediate_size
        lowerCAmelCase__				:  Tuple		       =     hidden_act
        lowerCAmelCase__				:  int		       =     hidden_dropout_prob
        lowerCAmelCase__				:  str		       =     attention_probs_dropout_prob
        lowerCAmelCase__				:  Dict		       =     max_position_embeddings
        lowerCAmelCase__				:  Dict		       =     eos_token_id
        lowerCAmelCase__				:  Tuple		       =     pad_token_id
        lowerCAmelCase__				:  Optional[Any]		       =     bos_token_id
        lowerCAmelCase__				:  Any		       =     initializer_range

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,				self.vocab_size		)      ,				3      ,				self.vocab_size		)
        lowerCAmelCase__				:  List[Any]		       =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,				dtype=np.intaa		))      ,				-1		)

        lowerCAmelCase__				:  List[Any]		       =     shift_tokens_right(__UpperCAmelCase      ,				1      ,				2		)

        lowerCAmelCase__				:  Optional[int]		       =     BlenderbotConfig(
            vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				encoder_layers=self.num_hidden_layers      ,				decoder_layers=self.num_hidden_layers      ,				encoder_attention_heads=self.num_attention_heads      ,				decoder_attention_heads=self.num_attention_heads      ,				encoder_ffn_dim=self.intermediate_size      ,				decoder_ffn_dim=self.intermediate_size      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				eos_token_id=self.eos_token_id      ,				bos_token_id=self.bos_token_id      ,				pad_token_id=self.pad_token_id      ,				initializer_range=self.initializer_range      ,				use_cache=__UpperCAmelCase      ,				)
        lowerCAmelCase__				:  Dict		       =     prepare_blenderbot_inputs_dict(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        return config, inputs_dict

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     self.prepare_config_and_inputs()
        return config, inputs_dict

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     20
        lowerCAmelCase__				:  Optional[Any]		       =     model_class_name(__UpperCAmelCase		)

        lowerCAmelCase__				:  Any		       =     model.encode(inputs_dict['''input_ids''']		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        lowerCAmelCase__				:  Any		       =     model.init_cache(decoder_input_ids.shape[0]      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,				dtype='''i4'''		)

        lowerCAmelCase__				:  int		       =     jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)
        lowerCAmelCase__				:  Optional[Any]		       =     model.decode(
            decoder_input_ids[:, :-1]      ,				__UpperCAmelCase      ,				decoder_attention_mask=__UpperCAmelCase      ,				past_key_values=__UpperCAmelCase      ,				decoder_position_ids=__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  List[str]		       =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype='''i4'''		)
        lowerCAmelCase__				:  Optional[Any]		       =     model.decode(
            decoder_input_ids[:, -1:]      ,				__UpperCAmelCase      ,				decoder_attention_mask=__UpperCAmelCase      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_position_ids=__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  Optional[Any]		       =     model.decode(__UpperCAmelCase      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3      ,				msg=f"""Max diff is {diff}"""		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     20
        lowerCAmelCase__				:  Tuple		       =     model_class_name(__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     model.encode(inputs_dict['''input_ids''']		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        lowerCAmelCase__				:  List[Any]		       =     jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
            ]      ,				axis=-1      ,				)

        lowerCAmelCase__				:  int		       =     model.init_cache(decoder_input_ids.shape[0]      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]      ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,				)

        lowerCAmelCase__				:  Optional[int]		       =     model.decode(
            decoder_input_ids[:, :-1]      ,				__UpperCAmelCase      ,				decoder_attention_mask=__UpperCAmelCase      ,				past_key_values=__UpperCAmelCase      ,				decoder_position_ids=__UpperCAmelCase      ,				)
        lowerCAmelCase__				:  Tuple		       =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,				dtype='''i4'''		)
        lowerCAmelCase__				:  Dict		       =     model.decode(
            decoder_input_ids[:, -1:]      ,				__UpperCAmelCase      ,				past_key_values=outputs_cache.past_key_values      ,				decoder_attention_mask=__UpperCAmelCase      ,				decoder_position_ids=__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  Union[str, Any]		       =     model.decode(__UpperCAmelCase      ,				__UpperCAmelCase      ,				decoder_attention_mask=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3      ,				msg=f"""Max diff is {diff}"""		)






@require_flax
class  _lowerCAmelCase     (  unittest.TestCase	):
      A__		     =		99

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]      ,				dtype=np.intaa      ,				)

        lowerCAmelCase__				:  Any		       =     input_ids.shape[0]
        lowerCAmelCase__				:  Optional[Any]		       =     BlenderbotConfig(
            vocab_size=self.vocab_size      ,				d_model=24      ,				encoder_layers=2      ,				decoder_layers=2      ,				encoder_attention_heads=2      ,				decoder_attention_heads=2      ,				encoder_ffn_dim=32      ,				decoder_ffn_dim=32      ,				max_position_embeddings=48      ,				eos_token_id=2      ,				pad_token_id=1      ,				bos_token_id=0      ,				)
        return config, input_ids, batch_size

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     self._get_config_and_data()
        lowerCAmelCase__				:  Tuple		       =     FlaxBlenderbotForConditionalGeneration(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     lm_model(input_ids=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     BlenderbotConfig(
            vocab_size=self.vocab_size      ,				d_model=14      ,				encoder_layers=2      ,				decoder_layers=2      ,				encoder_attention_heads=2      ,				decoder_attention_heads=2      ,				encoder_ffn_dim=8      ,				decoder_ffn_dim=8      ,				max_position_embeddings=48      ,				)
        lowerCAmelCase__				:  int		       =     FlaxBlenderbotForConditionalGeneration(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,				dtype=np.intaa		)
        lowerCAmelCase__				:  int		       =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,				dtype=np.intaa		)
        lowerCAmelCase__				:  List[str]		       =     lm_model(input_ids=__UpperCAmelCase      ,				decoder_input_ids=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,				dtype=np.intaa		)
        lowerCAmelCase__				:  Tuple		       =     shift_tokens_right(__UpperCAmelCase      ,				1      ,				2		)
        lowerCAmelCase__				:  Optional[Any]		       =     np.equal(__UpperCAmelCase      ,				1		).astype(np.floataa		).sum()
        lowerCAmelCase__				:  List[str]		       =     np.equal(__UpperCAmelCase      ,				1		).astype(np.floataa		).sum()
        self.assertEqual(shifted.shape      ,				input_ids.shape		)
        self.assertEqual(__UpperCAmelCase      ,				n_pad_before - 1		)
        self.assertTrue(np.equal(shifted[:, 0]      ,				2		).all()		)






@require_flax
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase    ,			_lowercase	):
      A__		     =		True
      A__		     =		(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      A__		     =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     FlaxBlenderbotModelTester(self		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__		):
            lowerCAmelCase__				:  Tuple		       =     self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)
            lowerCAmelCase__				:  Optional[int]		       =     model_class(__UpperCAmelCase		)

            @jax.jit
            def encode_jitted(__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
              return model.encode(input_ids=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)

            with self.subTest('''JIT Enabled'''		):
              lowerCAmelCase__				:  str		       =     encode_jitted(**__UpperCAmelCase		).to_tuple()

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                lowerCAmelCase__				:  Tuple		       =     encode_jitted(**__UpperCAmelCase		).to_tuple()

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__		):
            lowerCAmelCase__				:  List[Any]		       =     model_class(__UpperCAmelCase		)
            lowerCAmelCase__				:  List[Any]		       =     model.encode(inputs_dict['''input_ids''']      ,				inputs_dict['''attention_mask''']		)

            lowerCAmelCase__				:  Tuple		       =     {
                '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                '''encoder_outputs''': encoder_outputs,
            }

            @jax.jit
            def decode_jitted(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
              return model.decode(
                  decoder_input_ids=__UpperCAmelCase      ,				decoder_attention_mask=__UpperCAmelCase      ,				encoder_outputs=__UpperCAmelCase      ,				)

            with self.subTest('''JIT Enabled'''		):
              lowerCAmelCase__				:  List[Any]		       =     decode_jitted(**__UpperCAmelCase		).to_tuple()

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                lowerCAmelCase__				:  Dict		       =     decode_jitted(**__UpperCAmelCase		).to_tuple()

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

      @slow
      def 	__magic_name__(   self		):
        for model_class_name in self.all_model_classes:
          lowerCAmelCase__				:  int		       =     model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          lowerCAmelCase__				:  List[Any]		       =     np.ones((1, 1)		) * model.config.eos_token_id
          lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)
          self.assertIsNotNone(__UpperCAmelCase		)







      @unittest.skipUnless(jax_device != '''cpu'''      ,				'''3B test too slow on CPU.'''		)
      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
        lowerCAmelCase__				:  Optional[Any]		       =     {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

        lowerCAmelCase__				:  Dict		       =     FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''      ,				from_pt=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''		)

        lowerCAmelCase__				:  Any		       =     ['''Sam''']
        lowerCAmelCase__				:  int		       =     tokenizer(__UpperCAmelCase      ,				return_tensors='''jax'''		)

        lowerCAmelCase__				:  List[str]		       =     model.generate(**__UpperCAmelCase      ,				**__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     '''Sam is a great name. It means "sun" in Gaelic.'''

        lowerCAmelCase__				:  Any		       =     tokenizer.batch_decode(__UpperCAmelCase      ,				**__UpperCAmelCase		)
        assert generated_txt[0].strip() == tgt_text







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		)

