


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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.layoutlm.modeling_tf_layoutlm import (
           TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFLayoutLMForMaskedLM,
           TFLayoutLMForQuestionAnswering,
           TFLayoutLMForSequenceClassification,
           TFLayoutLMForTokenClassification,
           TFLayoutLMModel,
       )


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     List[str]=1_3      ,							lowerCamelCase_      :     str=7      ,							lowerCamelCase_      :     Dict=True      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     int=True      ,							lowerCamelCase_      :     int=9_9      ,							lowerCamelCase_      :     List[str]=3_2      ,							lowerCamelCase_      :     int=2      ,							lowerCamelCase_      :     Optional[int]=4      ,							lowerCamelCase_      :     str=3_7      ,							lowerCamelCase_      :     Optional[Any]="gelu"      ,							lowerCamelCase_      :     List[str]=0.1      ,							lowerCamelCase_      :     Optional[int]=0.1      ,							lowerCamelCase_      :     Optional[int]=5_1_2      ,							lowerCamelCase_      :     str=1_6      ,							lowerCamelCase_      :     Union[str, Any]=2      ,							lowerCamelCase_      :     Tuple=0.0_2      ,							lowerCamelCase_      :     str=3      ,							lowerCamelCase_      :     Optional[int]=4      ,							lowerCamelCase_      :     str=None      ,							lowerCamelCase_      :     str=1_0_0_0      ,							):
            a_    :     Optional[Any]										=    parent
            a_    :     Optional[Any]										=    batch_size
            a_    :     Tuple										=    seq_length
            a_    :     Tuple										=    is_training
            a_    :     Any										=    use_input_mask
            a_    :     List[Any]										=    use_token_type_ids
            a_    :     List[Any]										=    use_labels
            a_    :     Tuple										=    vocab_size
            a_    :     Optional[int]										=    hidden_size
            a_    :     List[Any]										=    num_hidden_layers
            a_    :     str										=    num_attention_heads
            a_    :     Optional[int]										=    intermediate_size
            a_    :     str										=    hidden_act
            a_    :     Optional[Any]										=    hidden_dropout_prob
            a_    :     Any										=    attention_probs_dropout_prob
            a_    :     str										=    max_position_embeddings
            a_    :     str										=    type_vocab_size
            a_    :     List[Any]										=    type_sequence_label_size
            a_    :     List[str]										=    initializer_range
            a_    :     List[str]										=    num_labels
            a_    :     List[str]										=    num_choices
            a_    :     List[Any]										=    scope
            a_    :     List[str]										=    range_bbox

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[int]										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size    )

            # convert bbox to numpy since TF does not support item assignment
            a_    :     int										=    ids_tensor([self.batch_size, self.seq_length, 4]      ,							self.range_bbox    ).numpy()
            # Ensure that bbox is legal
            for i in range(bbox.shape[0]    ):
                 for j in range(bbox.shape[1]    ):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                           a_    :     List[str]										=    bbox[i, j, 3]
                           a_    :     Union[str, Any]										=    bbox[i, j, 1]
                           a_    :     Dict										=    t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                           a_    :     int										=    bbox[i, j, 2]
                           a_    :     Optional[int]										=    bbox[i, j, 0]
                           a_    :     str										=    t
            a_    :     Optional[Any]										=    tf.convert_to_tensor(lowerCamelCase_    )

            a_    :     Optional[int]										=    None
            if self.use_input_mask:
                 a_    :     Optional[Any]										=    random_attention_mask([self.batch_size, self.seq_length]    )

            a_    :     Union[str, Any]										=    None
            if self.use_token_type_ids:
                 a_    :     Optional[int]										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size    )

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

            a_    :     str										=    LayoutLMConfig(
                vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							type_vocab_size=self.type_vocab_size      ,							initializer_range=self.initializer_range      ,							)

            return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     str    ):
            a_    :     int										=    TFLayoutLMModel(config=lowerCamelCase_    )

            a_    :     Dict										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )
            a_    :     int										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )
            a_    :     int										=    model(lowerCamelCase_      ,							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      :     Tuple      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     str										=    TFLayoutLMForMaskedLM(config=lowerCamelCase_    )

            a_    :     Tuple										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=lowerCamelCase_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[str]    ):
            a_    :     str										=    self.num_labels
            a_    :     Optional[int]										=    TFLayoutLMForSequenceClassification(config=lowerCamelCase_    )

            a_    :     Any										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Dict    ):
            a_    :     List[str]										=    self.num_labels
            a_    :     Any										=    TFLayoutLMForTokenClassification(config=lowerCamelCase_    )

            a_    :     List[str]										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=lowerCamelCase_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     List[str]    ):
            a_    :     List[Any]										=    TFLayoutLMForQuestionAnswering(config=lowerCamelCase_    )

            a_    :     Optional[int]										=    model(lowerCamelCase_      ,							lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )
            self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)    )
            self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Dict										=    self.prepare_config_and_inputs()
            (
                (
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			
            )    :     List[Any]										=    config_and_inputs
            a_    :     Union[str, Any]										=    {
                """input_ids""": input_ids,
                """bbox""": bbox,
                """token_type_ids""": token_type_ids,
                """attention_mask""": input_mask,
            }
            return config, inputs_dict


@require_tf
class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Optional[Any]							    =     (
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       lowerCamelCase__:     Tuple							    =     (
           {
               """feature-extraction""": TFLayoutLMModel,
               """fill-mask""": TFLayoutLMForMaskedLM,
               """text-classification""": TFLayoutLMForSequenceClassification,
               """token-classification""": TFLayoutLMForTokenClassification,
               """zero-shot""": TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCamelCase__:     List[str]							    =     False
       lowerCamelCase__:     Tuple							    =     True
       lowerCamelCase__:     List[Any]							    =     10

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Tuple										=    TFLayoutLMModelTester(self    )
            a_    :     str										=    ConfigTester(self      ,							config_class=lowerCamelCase_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            self.config_tester.run_common_tests()

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     str										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Tuple										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Any										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Optional[int]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_    :     Tuple										=    TFLayoutLMModel.from_pretrained(lowerCamelCase_    )
                 self.assertIsNotNone(lowerCamelCase_    )

       @unittest.skip("""Onnx compliancy broke with TF 2.10"""    )
       def 						UpperCAmelCase(   self      :     str    ):
            pass



def  _a			(						):
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     a_    :     Tuple										=    tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]] )  # noqa: E231
     a_    :     Optional[Any]										=    tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] )  # noqa: E231
     a_    :     Any										=    tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]] )  # noqa: E231
     a_    :     int										=    tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     a_    :     Dict										=    tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]] )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels


@require_tf
class   a__	(						unittest.TestCase    ):

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     str										=    TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""    )

            a_			,			a_			,			a_			,			a_			,			a_    :     int										=    prepare_layoutlm_batch_inputs()

            # forward pass
            a_    :     Optional[Any]										=    model(input_ids=lowerCamelCase_      ,							bbox=lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )

            # test the sequence output on [0, :3, :3]
            a_    :     int										=    tf.convert_to_tensor(
                [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]      ,							)

            self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,							lowerCamelCase_      ,							atol=1E-3    )    )

            # test the pooled output on [1, :3]
            a_    :     List[Any]										=    tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]    )

            self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,							lowerCamelCase_      ,							atol=1E-3    )    )

       @slow
       def 						UpperCAmelCase(   self      :     Tuple    ):
            # initialize model with randomly initialized sequence classification head
            a_    :     Tuple										=    TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,							num_labels=2    )

            a_			,			a_			,			a_			,			a_			,			a_    :     Dict										=    prepare_layoutlm_batch_inputs()

            # forward pass
            a_    :     Union[str, Any]										=    model(
                input_ids=lowerCamelCase_      ,							bbox=lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=tf.convert_to_tensor([1, 1]    )      ,							)

            # test whether we get a loss as a scalar
            a_    :     Dict										=    outputs.loss
            a_    :     Dict										=    (2,)
            self.assertEqual(loss.shape      ,							lowerCamelCase_    )

            # test the shape of the logits
            a_    :     Optional[int]										=    outputs.logits
            a_    :     str										=    (2, 2)
            self.assertEqual(logits.shape      ,							lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     str    ):
            # initialize model with randomly initialized token classification head
            a_    :     Dict										=    TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,							num_labels=1_3    )

            a_			,			a_			,			a_			,			a_			,			a_    :     Optional[Any]										=    prepare_layoutlm_batch_inputs()

            # forward pass
            a_    :     Optional[int]										=    model(
                input_ids=lowerCamelCase_      ,							bbox=lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=lowerCamelCase_    )

            # test the shape of the logits
            a_    :     Union[str, Any]										=    outputs.logits
            a_    :     Any										=    tf.convert_to_tensor((2, 2_5, 1_3)    )
            self.assertEqual(logits.shape      ,							lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     Any    ):
            # initialize model with randomly initialized token classification head
            a_    :     Union[str, Any]										=    TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""    )

            a_			,			a_			,			a_			,			a_			,			a_    :     Optional[Any]										=    prepare_layoutlm_batch_inputs()

            # forward pass
            a_    :     str										=    model(input_ids=lowerCamelCase_      ,							bbox=lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )

            # test the shape of the logits
            a_    :     Optional[int]										=    tf.convert_to_tensor((2, 2_5)    )
            self.assertEqual(outputs.start_logits.shape      ,							lowerCamelCase_    )
            self.assertEqual(outputs.end_logits.shape      ,							lowerCamelCase_    )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



