from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
    import tensorflow as tf

    from transformers import TFBlipTextModel
    from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_=12						,							lowerCAmelCase_=7						,							lowerCAmelCase_=True						,							lowerCAmelCase_=True						,							lowerCAmelCase_=True						,							lowerCAmelCase_=99						,							lowerCAmelCase_=32						,							lowerCAmelCase_=32						,							lowerCAmelCase_=2						,							lowerCAmelCase_=4						,							lowerCAmelCase_=37						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=5_12						,							lowerCAmelCase_=0.02						,							lowerCAmelCase_=0						,							lowerCAmelCase_=None						,							)    ->   List[Any]:
        _A	  =    parent
        _A	  =    batch_size
        _A	  =    seq_length
        _A	  =    is_training
        _A	  =    use_input_mask
        _A	  =    use_labels
        _A	  =    vocab_size
        _A	  =    hidden_size
        _A	  =    projection_dim
        _A	  =    num_hidden_layers
        _A	  =    num_attention_heads
        _A	  =    intermediate_size
        _A	  =    dropout
        _A	  =    attention_dropout
        _A	  =    max_position_embeddings
        _A	  =    initializer_range
        _A	  =    scope
        _A	  =    bos_token_id



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        _A	  =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size				)

        _A	  =    None
        if self.use_input_mask:
               _A	  =    random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
               _A	  =    input_mask.numpy()
               _A  ,    _A	  =    input_mask.shape
               _A	  =    np.random.randint(1						,							seq_length - 1						,							size=(batch_size,)				)
               for batch_idx, start_index in enumerate(lowerCamelCase__				):
                      _A	  =    1
                      _A	  =    0

        _A	  =    self.get_config()

        return config, input_ids, tf.convert_to_tensor(lowerCamelCase__				)



 def 	UpperCAmelCase				(   self				)    ->   Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size						,							hidden_size=self.hidden_size						,							projection_dim=self.projection_dim						,							num_hidden_layers=self.num_hidden_layers						,							num_attention_heads=self.num_attention_heads						,							intermediate_size=self.intermediate_size						,							dropout=self.dropout						,							attention_dropout=self.attention_dropout						,							max_position_embeddings=self.max_position_embeddings						,							initializer_range=self.initializer_range						,							bos_token_id=self.bos_token_id						,							)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   Optional[Any]:
        _A	  =    TFBlipTextModel(config=lowerCamelCase__				)
        _A	  =    model(lowerCamelCase__						,							attention_mask=lowerCamelCase__						,							training=lowerCamelCase__				)
        _A	  =    model(lowerCamelCase__						,							training=lowerCamelCase__				)
        self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)				)
        self.parent.assertEqual(result.pooler_output.shape						,							(self.batch_size, self.hidden_size)				)



 def 	UpperCAmelCase				(   self				)    ->   Any:
        _A	  =    self.prepare_config_and_inputs()
        _A  ,    _A  ,    _A	  =    config_and_inputs
        _A	  =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_tf
class 							a				(     __lowerCAmelCase			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :int            =						(TFBlipTextModel,) if is_tf_available() else ()
 lowerCamelCase      :Optional[int]            =						False
 lowerCamelCase      :Optional[Any]            =						False
 lowerCamelCase      :Any            =						False



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    BlipTextModelTester(self				)
        _A	  =    ConfigTester(self						,							config_class=lowerCamelCase__						,							hidden_size=37				)



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        self.config_tester.run_common_tests()



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        _A	  =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__				)



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        pass



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        pass



 @unittest.skip(reason="""Blip does not use inputs_embeds"""				)
 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        pass



 @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""				)
 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        pass



 @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""				)
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        pass



 @slow
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A	  =    TFBlipTextModel.from_pretrained(lowerCamelCase__				)
               self.assertIsNotNone(lowerCamelCase__				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_=True				)    ->   Dict:
        super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__				)










from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


