
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )



class 		UpperCamelCase__  :
   def __init__(self :					Dict					,				snake_case_ :					int					,				):
    __a :  int						=  parent
    __a :  Optional[int]						=  1_3
    __a :  Union[str, Any]						=  7
    __a :  int						=  3_0
    __a :  Union[str, Any]						=  self.seq_length + self.mem_len
    __a :  Tuple						=  1_5
    __a :  int						=  True
    __a :  List[Any]						=  True
    __a :  Any						=  9_9
    __a :  Union[str, Any]						=  [1_0, 5_0, 8_0]
    __a :  Tuple						=  3_2
    __a :  Dict						=  3_2
    __a :  Optional[int]						=  4
    __a :  Tuple						=  8
    __a :  Optional[Any]						=  1_2_8
    __a :  Dict						=  2
    __a :  Union[str, Any]						=  2
    __a :  List[Any]						=  None
    __a :  int						=  1
    __a :  int						=  0
    __a :  Tuple						=  3
    __a :  str						=  self.vocab_size - 1
    __a :  Optional[Any]						=  0.01
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )
    __a :  List[str]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  Dict						=  None
    if self.use_labels:
     __a :  List[str]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  Optional[Any]						=  TransfoXLConfig(
        vocab_size=self.vocab_size					,				mem_len=self.mem_len					,				clamp_len=self.clamp_len					,				cutoffs=self.cutoffs					,				d_model=self.hidden_size					,				d_embed=self.d_embed					,				n_head=self.num_attention_heads					,				d_head=self.d_head					,				d_inner=self.d_inner					,				div_val=self.div_val					,				n_layer=self.num_hidden_layers					,				eos_token_id=self.eos_token_id					,				pad_token_id=self.vocab_size - 1					,				init_range=self.init_range					,				num_labels=self.num_labels					,				)

    return (config, input_ids_a, input_ids_a, lm_labels)
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    random.seed(self.seed   )
    tf.random.set_seed(self.seed   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					Dict					,				snake_case_ :					Any   ):
    __a :  List[Any]						=  TFTransfoXLModel(snake_case_   )

    __a				,       __a :  str						=  model(snake_case_   ).to_tuple()

    __a :  Dict						=  {'''input_ids''': input_ids_a, '''mems''': mems_a}

    __a				,       __a :  Dict						=  model(snake_case_   ).to_tuple()

    self.parent.assertEqual(hidden_states_a.shape					,				(self.batch_size, self.seq_length, self.hidden_size)   )
    self.parent.assertEqual(hidden_states_a.shape					,				(self.batch_size, self.seq_length, self.hidden_size)   )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					List[str]					,				snake_case_ :					str					,				snake_case_ :					Tuple   ):
    __a :  int						=  TFTransfoXLLMHeadModel(snake_case_   )

    __a				,       __a :  str						=  model(snake_case_   ).to_tuple()

    __a :  str						=  {'''input_ids''': input_ids_a, '''labels''': lm_labels}
    __a				,       __a :  Tuple						=  model(snake_case_   ).to_tuple()

    __a				,       __a :  Tuple						=  model([input_ids_a, mems_a]   ).to_tuple()

    __a :  Optional[Any]						=  {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

    __a				,       __a :  Tuple						=  model(snake_case_   ).to_tuple()

    self.parent.assertEqual(lm_logits_a.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)

    self.parent.assertEqual(lm_logits_a.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]					,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers					,				)
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					int					,				snake_case_ :					str   ):
    __a :  int						=  TFTransfoXLForSequenceClassification(snake_case_   )
    __a :  Union[str, Any]						=  model(snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )







   def       lowerCAmelCase			(self :					Dict   ):
    __a :  List[Any]						=  self.prepare_config_and_inputs()
    ((__a)				,       (__a)				,       (__a)				,       (__a)) :  int						=  config_and_inputs
    __a :  Dict						=  {'''input_ids''': input_ids_a}
    return config, inputs_dict





@require_tf
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   _SCREAMING_SNAKE_CASE      :							str	       =      () if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE      :							Any	       =      (
       {
           "feature-extraction": TFTransfoXLModel,
           "text-classification": TFTransfoXLForSequenceClassification,
           "text-generation": TFTransfoXLLMHeadModel,
           "zero-shot": TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   _SCREAMING_SNAKE_CASE      :							Dict	       =      False
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      False
   _SCREAMING_SNAKE_CASE      :							Dict	       =      False
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      False
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					str					,				snake_case_ :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					List[Any]   ):
    if pipeline_test_casse_name == "TextGenerationPipelineTests":
     # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
     # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
     # tokenizer.
     return True

    return False
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Tuple						=  TFTransfoXLModelTester(self   )
    __a :  int						=  ConfigTester(self					,				config_class=snake_case_					,				d_embed=3_7   )
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[int]   ):
    self.model_tester.set_seed()
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_model(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    self.model_tester.set_seed()
    __a :  int						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case_   )
   def       lowerCAmelCase			(self :					List[str]   ):
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    __a				,       __a :  Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
    __a :  Optional[Any]						=  [TFTransfoXLForSequenceClassification]

    for model_class in self.all_model_classes:
     __a :  int						=  model_class(snake_case_   )
     assert isinstance(model.get_input_embeddings()					,				tf.keras.layers.Layer   )
     if model_class in list_other_models_with_output_ebd:
      __a :  Optional[int]						=  model.get_output_embeddings()
      assert isinstance(snake_case_					,				tf.keras.layers.Layer   )
      __a :  int						=  model.get_bias()
      assert name is None
     else:
      __a :  Optional[Any]						=  model.get_output_embeddings()
      assert x is None
      __a :  List[str]						=  model.get_bias()
      assert name is None
   def       lowerCAmelCase			(self :					int   ):
    # TODO JP: Make TransfoXL XLA compliant
    pass
   @slow
   def       lowerCAmelCase			(self :					int   ):
    for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __a :  Optional[int]						=  TFTransfoXLModel.from_pretrained(snake_case_   )
     self.assertIsNotNone(snake_case_   )







   @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    pass





@require_tf
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @unittest.skip('''Skip test until #12651 is resolved.'''   )
   @slow
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Tuple						=  TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
    # fmt: off
    __a :  Union[str, Any]						=  tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]					,				dtype=tf.intaa   )  # noqa: E231
    # fmt: on
    #  In 1991 , the remains of Russian Tsar Nicholas II and his family
    #  ( except for Alexei and Maria ) are discovered .
    #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
    #  remainder of the story . 1883 Western Siberia ,
    #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
    #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
    #  father initially slaps him for making such an accusation , Rasputin watches as the
    #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
    #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
    #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

    # fmt: off
    __a :  int						=  [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
    # fmt: on
    #  In 1991, the remains of Russian Tsar Nicholas II and his family (
    #  except for Alexei and Maria ) are discovered. The voice of young son,
    #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
    #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
    #  and a group of men to perform magic. Rasputin has a vision and
    #  denounces one of the men as a horse thief. Although his father initially
    #  slaps him for making such an accusation, Rasputin watches as the man
    #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
    #  of the Virgin Mary, prompting him to become a priest.
    #  Rasputin quickly becomes famous, with people, even a bishop, begging for
    #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
    # Nicholas II and his family were discovered. The voice of <unk> young son,
    # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

    __a :  Optional[int]						=  model.generate(snake_case_					,				max_length=2_0_0					,				do_sample=snake_case_   )
    self.assertListEqual(output_ids[0].numpy().tolist()					,				snake_case_   )


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

 for input_type in input_types:
  if input_type == "text":
   inputs.append('''Text input'''  )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''  )  ) / '''000000039769.png'''  ).resize((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

 for output in outputs:
  if isinstance(lowerCAmelCase__  ,   (str, AgentText)  ):
   output_types.append('''text'''  )
  elif isinstance(lowerCAmelCase__  ,   (Image.Image, AgentImage)  ):
   output_types.append('''image'''  )
  elif isinstance(lowerCAmelCase__  ,   (torch.Tensor, AgentAudio)  ):
   output_types.append('''audio'''  )
  else:
   raise ValueError(f"Invalid output: {output}"  )

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

    # There is a single output
    if len(self.tool.outputs   ) == 1:
     __a :  Tuple						=  [outputs]

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    self.assertTrue(hasattr(self.tool					,				'''description'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''default_checkpoint'''   )   )
    self.assertTrue(self.tool.description.startswith('''This is a tool that'''   )   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				snake_case_   ):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )
