from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.ctrl.modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
    )






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       str	      =				parent
        A_							:       int	      =				13
        A_							:       List[Any]	      =				7
        A_							:       Union[str, Any]	      =				True
        A_							:       List[Any]	      =				True
        A_							:       List[Any]	      =				True
        A_							:       Optional[int]	      =				True
        A_							:       int	      =				True
        A_							:       Tuple	      =				99
        A_							:       List[str]	      =				32
        A_							:       Dict	      =				2
        A_							:       List[str]	      =				4
        A_							:       Any	      =				37
        A_							:       Optional[Any]	      =				'''gelu'''
        A_							:       Optional[int]	      =				0.1
        A_							:       Dict	      =				0.1
        A_							:       Dict	      =				512
        A_							:       Union[str, Any]	      =				16
        A_							:       str	      =				2
        A_							:       int	      =				0.02
        A_							:       Any	      =				3
        A_							:       Optional[Any]	      =				4
        A_							:       Tuple	      =				None
        A_							:       str	      =				self.vocab_size - 1
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size      )

        A_							:       List[str]	      =				None
        if self.use_input_mask:
               A_							:       List[Any]	      =				random_attention_mask([self.batch_size, self.seq_length]      )

        A_							:       str	      =				None
        if self.use_token_type_ids:
               A_							:       List[str]	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size      )

        A_							:       str	      =				None
        if self.use_mc_token_ids:
               A_							:       Dict	      =				ids_tensor([self.batch_size, self.num_choices] ,       self.seq_length      )

        A_							:       Any	      =				None
        A_							:       Union[str, Any]	      =				None
        A_							:       Union[str, Any]	      =				None
        if self.use_labels:
               A_							:       Dict	      =				ids_tensor([self.batch_size] ,       self.type_sequence_label_size      )
               A_							:       Optional[Any]	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels      )
               A_							:       Optional[int]	      =				ids_tensor([self.batch_size] ,       self.num_choices      )

        A_							:       Optional[Any]	      =				CTRLConfig(
            vocab_size=self.vocab_size ,       n_embd=self.hidden_size ,       n_layer=self.num_hidden_layers ,       n_head=self.num_attention_heads ,       n_positions=self.max_position_embeddings ,       pad_token_id=self.pad_token_id ,       )

        A_							:       List[Any]	      =				ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,       2      )

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       *__magic_name__      ):

        """simple docstring"""

        A_							:       Tuple	      =				TFCTRLModel(config=lowerCAmelCase_      )
        A_							:       Optional[int]	      =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
        A_							:       Union[str, Any]	      =				model(lowerCAmelCase_      )

        A_							:       List[Any]	      =				[input_ids, None, input_mask]  # None is the input for 'past'
        A_							:       List[Any]	      =				model(lowerCAmelCase_      )

        A_							:       Optional[Any]	      =				model(lowerCAmelCase_      )

        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       *__magic_name__      ):

        """simple docstring"""

        A_							:       int	      =				TFCTRLLMHeadModel(config=lowerCAmelCase_      )
        A_							:       Optional[int]	      =				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
        A_							:       Tuple	      =				model(lowerCAmelCase_      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       *__magic_name__      ):

        """simple docstring"""

        A_							:       Any	      =				self.num_labels
        A_							:       Union[str, Any]	      =				ids_tensor([self.batch_size] ,       self.type_sequence_label_size      )
        A_							:       List[Any]	      =				{
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''labels''': sequence_labels,
        }
        A_							:       str	      =				TFCTRLForSequenceClassification(lowerCAmelCase_      )
        A_							:       Tuple	      =				model(lowerCAmelCase_      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.prepare_config_and_inputs()

        (
            (
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,							(
            A_						
        )      ,													
        )							:       List[Any]	      =				config_and_inputs

        A_							:       Tuple	      =				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






@require_tf
class     __UpperCAmelCase(    A__      ,	A__      ,	unittest.TestCase				):






 """simple docstring"""




 __magic_name__          =   (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
 __magic_name__          =   (TFCTRLLMHeadModel,) if is_tf_available() else ()
 __magic_name__          =   (
     {
         """feature-extraction""": TFCTRLModel,
         """text-classification""": TFCTRLForSequenceClassification,
         """text-generation""": TFCTRLLMHeadModel,
         """zero-shot""": TFCTRLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __magic_name__          =   False
 __magic_name__          =   False
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
               # config could not be created.
               return True

        return False
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				TFCTRLModelTester(self      )
        A_							:       Any	      =				ConfigTester(self ,       config_class=lowerCAmelCase_ ,       n_embd=37      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        self.config_tester.run_common_tests()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       str	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase_      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_lm_head(*lowerCAmelCase_      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowerCAmelCase_      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_      ,							A_							:       Tuple	      =				self.model_tester.prepare_config_and_inputs_for_common()
        A_							:       Optional[Any]	      =				[TFCTRLLMHeadModel]
        A_							:       List[str]	      =				[TFCTRLForSequenceClassification]

        for model_class in self.all_model_classes:
               A_							:       Tuple	      =				model_class(lowerCAmelCase_      )
               model.build()  # may be needed for the get_bias() call below
               assert isinstance(model.get_input_embeddings() ,       tf.keras.layers.Layer      )

               if model_class in list_lm_models:
                      A_							:       int	      =				model.get_output_embeddings()
                      assert isinstance(lowerCAmelCase_ ,       tf.keras.layers.Layer      )
                      A_							:       Tuple	      =				model.get_bias()
                      assert isinstance(lowerCAmelCase_ ,       lowerCAmelCase_      )
                      for k, v in name.items():
                             assert isinstance(lowerCAmelCase_ ,       tf.Variable      )
               elif model_class in list_other_models_with_output_ebd:
                      A_							:       List[str]	      =				model.get_output_embeddings()
                      assert isinstance(lowerCAmelCase_ ,       tf.keras.layers.Layer      )
                      A_							:       Optional[Any]	      =				model.get_bias()
                      assert name is None
               else:
                      A_							:       Optional[int]	      =				model.get_output_embeddings()
                      assert x is None
                      A_							:       str	      =				model.get_bias()
                      assert name is None
 @slow
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_							:       Optional[int]	      =				TFCTRLModel.from_pretrained(lowerCAmelCase_      )
               self.assertIsNotNone(lowerCAmelCase_      )






@require_tf
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 @slow
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				TFCTRLLMHeadModel.from_pretrained('''ctrl'''      )
        A_							:       Union[str, Any]	      =				tf.convert_to_tensor([[1_1859, 0, 1611, 8]] ,       dtype=tf.intaa      )  # Legal the president is
        A_							:       Any	      =				[
            1_1859,
            0,
            1611,
            8,
            5,
            150,
            2_6449,
            2,
            19,
            348,
            469,
            3,
            2595,
            48,
            2_0740,
            24_6533,
            24_6533,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        A_							:       Any	      =				model.generate(lowerCAmelCase_ ,       do_sample=lowerCAmelCase_      )
        self.assertListEqual(output_ids[0].numpy().tolist() ,       lowerCAmelCase_      )




'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     A							     = False

if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         PixaStructImageProcessor,
         PixaStructProcessor,
         PreTrainedTokenizerFast,
         TaTokenizer,
     )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'       ,				)
@require_vision
@require_torch
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				PixaStructImageProcessor()
        snake_case_    							=				TaTokenizer.from_pretrained("t5-small"     )

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

        snake_case_    							=				[Image.fromarray(np.moveaxis(lowerCAmelCase_						,			0						,			-1     )     ) for x in image_inputs]

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_image_processor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				PixaStructProcessor.from_pretrained(
            self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer						,			lowerCAmelCase_     )

        self.assertEqual(processor.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(images=lowerCAmelCase_						,			return_tensors="np"     )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()						,			input_processor[key].sum()						,			delta=1E-2     )

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_						,			return_token_type_ids=lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_     )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase_     ):
              processor()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        snake_case_    							=				[5_12, 10_24, 20_48, 40_96]
        snake_case_    							=				[7_70, 7_70, 7_70, 7_70]
        # with text
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

        # without text input
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()     )						,			["input_ids", "attention_mask"]     )

