
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.xglm.modeling_tf_xglm import (
         TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXGLMForCausalLM,
         TFXGLMModel,
     )



@require_tf
class       _snake_case   :
 __lowerCAmelCase  :     int        =			XGLMConfig
 __lowerCAmelCase  :     List[Any]        =			{}
 __lowerCAmelCase  :     List[str]        =			'gelu'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=14					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=5_12					,  SCREAMING_SNAKE_CASE_=0.0_2					,  ):


   '''simple docstring'''




   lowercase__  :   Dict    		=      parent
   lowercase__  :   Union[str, Any]    		=      batch_size
   lowercase__  :   Union[str, Any]    		=      seq_length
   lowercase__  :   List[Any]    		=      is_training
   lowercase__  :   Optional[int]    		=      use_input_mask
   lowercase__  :   Optional[int]    		=      use_labels
   lowercase__  :   Optional[int]    		=      vocab_size
   lowercase__  :   int    		=      d_model
   lowercase__  :   str    		=      num_hidden_layers
   lowercase__  :   Optional[int]    		=      num_attention_heads
   lowercase__  :   Union[str, Any]    		=      ffn_dim
   lowercase__  :   Union[str, Any]    		=      activation_function
   lowercase__  :   Union[str, Any]    		=      activation_dropout
   lowercase__  :   Any    		=      attention_dropout
   lowercase__  :   int    		=      max_position_embeddings
   lowercase__  :   List[Any]    		=      initializer_range
   lowercase__  :   str    		=      None
   lowercase__  :   Dict    		=      0
   lowercase__  :   Any    		=      2
   lowercase__  :   str    		=      1


 def      lowercase__     (					self):


   '''simple docstring'''




   return XGLMConfig.from_pretrained("""facebook/xglm-564M""")


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      tf.clip_by_value(
       ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)					,  clip_value_min=0					,  clip_value_max=3)

   lowercase__  :   Tuple    		=      None
   if self.use_input_mask:
     lowercase__  :   Any    		=      random_attention_mask([self.batch_size, self.seq_length])

   lowercase__  :   str    		=      self.get_config()

   lowercase__  :   List[str]    		=      floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,  2)

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
   )


 def      lowercase__     (					self):


   '''simple docstring'''




   return XGLMConfig(
       vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  num_layers=self.num_hidden_layers					,  attention_heads=self.num_attention_heads					,  ffn_dim=self.ffn_dim					,  activation_function=self.activation_function					,  activation_dropout=self.activation_dropout					,  attention_dropout=self.attention_dropout					,  max_position_embeddings=self.max_position_embeddings					,  initializer_range=self.initializer_range					,  use_cache=SCREAMING_SNAKE_CASE_					,  bos_token_id=self.bos_token_id					,  eos_token_id=self.eos_token_id					,  pad_token_id=self.pad_token_id					,  return_dict=SCREAMING_SNAKE_CASE_					,  )



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.prepare_config_and_inputs()

   (
       (
       lowercase__
   )		,    (
       lowercase__
   )		,    (
       lowercase__
   )		,    (
       lowercase__
   )		,    
   )  :   str    		=      config_and_inputs

   lowercase__  :   Any    		=      {
       """input_ids""": input_ids,
       """head_mask""": head_mask,
   }

   return config, inputs_dict



@require_tf
class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Any        =			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __lowerCAmelCase  :     Dict        =			(TFXGLMForCausalLM,) if is_tf_available() else ()
 __lowerCAmelCase  :     Tuple        =			(
     {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __lowerCAmelCase  :     int        =			False
 __lowerCAmelCase  :     List[Any]        =			False
 __lowerCAmelCase  :     Any        =			False


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      TFXGLMModelTester(self)
   lowercase__  :   Optional[int]    		=      ConfigTester(self					,  config_class=SCREAMING_SNAKE_CASE_					,  n_embd=37)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__  :   Optional[Any]    		=      TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)



 @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""")
 def      lowercase__     (					self):


   '''simple docstring'''




   super().test_resize_token_embeddings()



@require_tf
class       _snake_case   (	unittest.TestCase							):


 @slow
 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=True):


   '''simple docstring'''




   lowercase__  :   Any    		=      TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
   lowercase__  :   Optional[int]    		=      tf.convert_to_tensor([[2, 2_68, 98_65]]					,  dtype=tf.intaa)  # The dog
   # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
   # fmt: off
   lowercase__  :   Union[str, Any]    		=      [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
   # fmt: on
   lowercase__  :   int    		=      model.generate(SCREAMING_SNAKE_CASE_					,  do_sample=SCREAMING_SNAKE_CASE_					,  num_beams=1)
   if verify_outputs:
     self.assertListEqual(output_ids[0].numpy().tolist()					,  SCREAMING_SNAKE_CASE_)


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")
   lowercase__  :   Optional[Any]    		=      TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")

   tf.random.set_seed(0)
   lowercase__  :   int    		=      tokenizer("""Today is a nice day and"""					,  return_tensors="""tf""")
   lowercase__  :   Dict    		=      tokenized.input_ids
   # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
   with tf.device(""":/CPU:0"""):
     lowercase__  :   str    		=      model.generate(SCREAMING_SNAKE_CASE_					,  do_sample=SCREAMING_SNAKE_CASE_					,  seed=[7, 0])
   lowercase__  :   List[str]    		=      tokenizer.decode(output_ids[0]					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[int]    		=      (
       """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
   )
   self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)



 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
   lowercase__  :   List[str]    		=      XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")

   lowercase__  :   str    		=      """left"""

   # use different length sentences to test batching
   lowercase__  :   Optional[int]    		=      [
       """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
       """left-padding, such as in batched generation. The output for the sequence below should be the same """
       """regardless of whether left padding is applied or not. When""",
       """Hello, my dog is a little""",
   ]

   lowercase__  :   Dict    		=      tokenizer(SCREAMING_SNAKE_CASE_					,  return_tensors="""tf"""					,  padding=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      inputs["""input_ids"""]

   lowercase__  :   List[Any]    		=      model.generate(input_ids=SCREAMING_SNAKE_CASE_					,  attention_mask=inputs["""attention_mask"""]					,  max_new_tokens=12)

   lowercase__  :   Dict    		=      tokenizer(sentences[0]					,  return_tensors="""tf""").input_ids
   lowercase__  :   Union[str, Any]    		=      model.generate(input_ids=SCREAMING_SNAKE_CASE_					,  max_new_tokens=12)

   lowercase__  :   List[Any]    		=      tokenizer(sentences[1]					,  return_tensors="""tf""").input_ids
   lowercase__  :   Any    		=      model.generate(input_ids=SCREAMING_SNAKE_CASE_					,  max_new_tokens=12)

   lowercase__  :   int    		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      tokenizer.decode(output_non_padded[0]					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      tokenizer.decode(output_padded[0]					,  skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Tuple    		=      [
       """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
       """left-padding, such as in batched generation. The output for the sequence below should be the same """
       """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
       """a single""",
       """Hello, my dog is a little bit of a shy one, but he is very friendly""",
   ]
   self.assertListEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   self.assertListEqual(SCREAMING_SNAKE_CASE_					,  [non_padded_sentence, padded_sentence])








# 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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			'dandelin/vilt-b32-finetuned-vqa'
 __lowerCAmelCase  :     Union[str, Any]        =			(
     'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
     'image containing the information, as well as a `question` which should be the question in English. It '
     'returns a text that is the answer to the question.'
 )
 __lowerCAmelCase  :     str        =			'image_qa'
 __lowerCAmelCase  :     int        =			AutoProcessor
 __lowerCAmelCase  :     Optional[Any]        =			AutoModelForVisualQuestionAnswering

 __lowerCAmelCase  :     str        =			['image', 'text']
 __lowerCAmelCase  :     Optional[int]        =			['text']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""vision"""])
   super().__init__(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.pre_processor(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with torch.no_grad():
     return self.model(**SCREAMING_SNAKE_CASE_).logits



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      outputs.argmax(-1).item()
   return self.model.config.idalabel[idx]






